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The digital and sustainable transformation of container terminals is increasingly shaped by the rapid expansion of Electric Cargo Handling Equipment (eCHE). These battery-electric and plug-in hybrid machines offer clear benefits—lower emissions, reduced noise, and higher energy efficiency—yet their successful deployment requires more than simply replacing diesel engines with electric powertrains. Electrification introduces new operational dependencies: charging cycles must be synchronised with logistics, energy availability must be continuously monitored, and multiple systems must communicate reliably in real time.

In this evolving landscape, interoperability becomes essential. Today, terminals operate fleets composed of heterogeneous equipment, manufacturers, charging technologies, and management systems, all generating and consuming operational, energy, and maintenance data. Without a shared semantic foundation, this diversity leads to fragmented data flows, bespoke integrations, limited visibility, and rising complexity—ultimately constraining the scalability and efficiency of eCHE operations.

TIC4.0 addresses this gap by providing a unified semantic and data-model framework for port-terminal digitalisation. By defining standardised concepts, observed properties, and communication structures, TIC4.0 establishes the foundations for seamless data exchange across electric vehicles, charging systems, terminal control platforms, and energy-management functions. This common vocabulary enables real-time coordination, predictive analytics, and the orchestration of charging and operations at fleet and terminal scale.

The purpose of this document is to establish a structured, information-centric framework that enables the efficient, safe, and scalable operation of battery-electric Cargo Handling Equipment (BE-CHE) within container terminals. Building on the context, challenges, and system interactions analysed throughout the paper, it defines the data requirements, semantic elements, and functional exchanges needed for real-time coordination between equipment, operational systems, charging management, and terminal-level energy management.

Through this work, the document aims to clarify what information must flow, between which systems, and for what operational purpose, so that terminal operators, system vendors, and equipment manufacturers can achieve full interoperability across heterogeneous fleets and infrastructures. The ultimate goal is to provide the foundations for a unified TIC4.0 semantic model that supports energy-aware decision-making, predictive capabilities, and end-to-end optimisation of electrified terminal operations.

Scope:

• Focus on container terminals.

• Address challenges such as aligning charging with logistics and ensuring effective communication and coordination.

• Support fleet optimization, minimize operational disruptions, and reduce environmental impact.

• Define key information needs: battery status, vehicle location, operator availability, job orders, charging station status, and grid capacity.

Below is a short overview of the main topics addressed throughout this paper, providing a concise guide to the elements that structure the work.

• Operational Context (Chapter 2):
  Defines the technological and organisational transformation driven by electrification, highlighting the new dependencies between battery behaviour, charging needs, vehicle utilisation, and mission planning.

• Challenges and Constraints (Chapter 3):
  Frames the operational, energy, and battery-health challenges that smart data exchange must address—such as synchronising charging with logistics, managing grid limitations, and handling ageing or weather-dependent battery performance.

• Standardisation Landscape (Chapter 4):
  Summarises existing IEC/ISO initiatives relevant to electric industrial vehicles and charging systems, clarifying their relationship with TIC4.0 and identifying the gaps that this document addresses.

• Information Requirements (Chapter 5):
  Defines the data needed at each functional layer—CHE telemetry, operational planning, charging control, and energy management—serving as the backbone of the future Smart eCHE semantic model.

• System-to-System Integration (Chapter 6):
  Details the bidirectional information flows between CHE, Terminal Control Systems, Charging Management Systems, and Terminal Energy Management Systems, describing how energy-aware operations must be orchestrated digitally.

• Semantic Framework to Be Developed (Chapter 7):
  Outlines the candidate data elements that will be formalised in upcoming iterations of the TIC4.0 data model, establishing a roadmap for semantic standardisation.

• Implementation Roadmap (Chapter 8):
  Describes the phased methodology to integrate each functional layer into the TIC4.0 data model—CHE → TCS → CMS → TEMS—ensuring coherent, incremental standardisation.

The electrification of terminal equipment is reshaping the operational landscape of container terminals, driven by environmental regulations, sustainability objectives, and the growing demand for data-driven, efficient logistics. Historically, ports have relied on manually operated diesel machinery, whose behaviour and performance characteristics were highly predictable. Diesel engines offered high availability, consistent refuelling times, and straightforward integration into existing operational processes.

Electric terminal trucks, automated stacking cranes, and other container-handling vehicles are increasingly forming the backbone of modern ports. Their adoption is not merely a technological upgrade but a strategic transformation of operational processes. Effective electrification requires integrating charging management, dynamic route planning, real-time coordination, and the synchronization of vehicles with charging points, all while optimizing workflows within individual terminals and across the broader logistics ecosystem. As highlighted by Portwise, achieving this transformation demands a structured and holistic approach in which terminals first define their objectives, operational characteristics, local constraints and possible technological solutions; then configure feasible alternatives involving different layouts, operational modes and equipment combinations; subsequently model the expected behaviour of batteries, charging systems and operations; and ultimately analyse the results in terms of electricity demand patterns, peak loads, fleet size requirements, operational performance and overall CAPEX/OPEX implications. This staged methodology reinforces the idea that electrification is not simply a matter of selecting equipment, but of evaluating entire operational, infrastructural and economic ecosystems as coherent systems.

At the same time, the introduction of diverse OEMs, charging technologies, and management systems is increasing the risk of technological fragmentation. Without coordinated frameworks, terminals struggle to ensure compatibility across fleets, chargers, and operational systems, which can undermine operational performance and scalability. Portwise stresses that identifying the right electrification strategy requires balancing a wide set of interdependent variables—including propulsion technology, charging methods, battery size and characteristics, charging regimes, infrastructure capacity, external factors, and the demands of other electricity consumers within the terminal. This interplay of variables highlights that electrification decisions cannot be optimised in isolation: each choice influences equipment availability, grid behaviour, operational efficiency, and long-term cost, reinforcing the need for a unified and interoperable approach such as the one supported by TIC4.0 standards.

Recent industry work, including the analysis by Portwise, shows that simulation has become essential for understanding the complexities of electrified terminal operations, enabling terminals to translate raw data and operational uncertainties into meaningful insights, quantified behaviours, and forward-looking scenarios. However, simulation is only as powerful as the quality, consistency, and interoperability of the data feeding it. This is precisely where TIC4.0 plays a critical role: by defining standardized data models, harmonized semantics, and unified message structures, TIC4.0 creates the foundational layer that allows batteries, chargers, vehicles, and operational systems to be represented coherently within simulation environments.

From Manual to Smart: The Evolution of Terminal Equipment

The increasing incorporation of eCHE—such as electric or plug-in hybrid terminal trucks, automated guided vehicles (AGVs), and other container handling equipment—is radically redefining operational processes within port terminals. By moving from conventional diesel-powered equipment to electric fleets, ports are not only aiming to reduce emissions or improve energy efficiency: they are also facing the need to rethink how daily operations are planned, coordinated, and monitored.

This technological shift, beyond simply replacing engines, demands a transformation of traditional workflows. Elements such as the management of charging, dynamic route planning to maximize autonomy, and synchronization between vehicles and charging points all require traditional processes (e.g. scheduling) to be adapted to these new needs , and integration of real-time information (especially for multifactor optimization), both at the level of individual terminals and across the broader logistics ecosystem. And this is not only limited to vehicles: due to power requirements of the electric batteries' charging, energy and grid management (from an infrastructure side) in the terminal must also be taken into consideration.

Here lies the central challenge: the proliferation of manufacturers, charging solutions, and heterogeneous management systems introduces a high risk of technological “silos” and incompatibilities, which can undermine overall efficiency and even hamper large-scale adoption. Without a foundation of standardized data and messaging, each new vehicle or system may require costly integrations, generate operational errors, and hinder future scalability.

This is why the mission of TIC4.0 is important: to provide a common language and clear semantic standards, data models, and communication protocols that allow electric vehicles, charging infrastructures, and operational systems to “speak the same language.” This framework facilitates coordinated processes, supports interoperability, and helps develop new approaches to electric port mobility that contribute to sustainability, competitiveness, and resilience in the sector.

Types of Electric Terminal Equipment

The electrification of container terminals extends beyond a single vehicle type, encompassing a diverse array of equipment designed to handle the high-volume, precision-driven demands of port operations. These electric terminal technologies, often referred to collectively as electric Terminal Trucks or more broadly as Battery-Electric Container Handling Equipment (BE-CHE), include vehicles and machinery optimized for tasks such as container transport, stacking, and lifting. Drawing from industry analyses like the Zero Emissions Port Alliance (ZEPA) report on projected global demand for BE-CHE (2024), as well as insights from the Port Equipment Manufacturers Association (PEMA) and literature reviews on port sustainability, this section outlines the primary categories of electric equipment. These technologies are pivotal in reducing scope 1 and 2 emissions while enhancing operational efficiency, but their effective integration hinges on standardized data frameworks like TIC4.0 to ensure seamless interoperability across heterogeneous systems.

Electric Terminal Tractors (eTTs)

Electric terminal tractors, also known as yard trucks or shunt trucks, form the backbone of intra-terminal container movement. These vehicles are responsible for transporting containers between quayside cranes, stacking areas, and truck loading zones, often operating in short, repetitive cycles that make them ideal candidates for electrification. Unlike their diesel counterparts, eTTs utilize high-capacity lithium-ion batteries, offering zero tailpipe emissions and lower operational costs through regenerative braking and efficient energy use.

Key characteristics and benefits include:

• Battery and Charging Integration: eTTs typically require 2.6–3 kWh per container move, with annual consumption around 2,600 MWh per unit under standard operations (ZEPA, 2024). They support various charging strategies, such as conventional plug-in chargers (up to 500 kW DC) or fast chargers (>500 kW), and can integrate with battery swapping systems for minimal downtime.

• Operational Advantages: Reduced noise and vibration improve operator comfort and safety, while sensor integration enables real-time data on location, battery status, and utilization—critical for fleet optimization.

• Market Projections: According to ZEPA's analysis, demand for BE-terminal tractors is expected to dominate the electrification wave, with ZEPA terminal operators (representing ~15% of global TEU throughput) projecting 3,500–5,400 units purchased between 2025–2035 under cost parity scenarios. Extrapolated globally, this could reach 23,000–36,000 units, underscoring their role in driving sustainability.

Challenges in adoption include aligning charging schedules with dynamic logistics flows, which TIC4.0 addresses through standardized messaging for battery status, job orders, and grid capacity.

Electric Straddle Carriers

Straddle carriers are specialized vehicles that "straddle" containers for lifting, stacking, and short-distance transport within the yard. Electric variants (BE-straddle carriers) replace diesel engines with battery-electric systems, enabling high-lift capacities (up to 50 tons) and precise maneuvering in dense terminal environments.

Notable features:

• Energy Demands: These machines consume approximately 6.2 kWh per move, with annual usage up to 6,200 MWh per unit, reflecting their intensive stacking operations (ZEPA, 2024). They often require higher-power charging infrastructure due to larger batteries.

• Sustainability Impact: By eliminating diesel consumption (typically 20 liters/hour for legacy models), they significantly cut CO2 emissions—ZEPA estimates that global adoption could avoid 30–36 Mt of CO2 cumulatively by 2035 if scaled industry-wide.

• Adoption Trends: ZEPA projects 400–700 BE-straddle carriers purchased by its members from 2025–2035, with global demand potentially reaching 2,700–4,700 units. Their higher upfront costs (15–25% above diesel equivalents) are offset by total cost of ownership (TCO) parity expected by 2025, accelerated by economies of scale.

Integration with automated systems is key; TIC4.0 standards facilitate data exchange with key stakeholders and systems for predictive maintenance and coordination with other equipment, mitigating risks like charging downtime in high-throughput scenarios.

Electric Lift Trucks and Related Cranes

This category includes a range of lifting and stacking equipment, such as top loaders, reach stackers or empty container handlers). Electric lift trucks handle container stacking and retrieval, while ASCs provide fully automated, rail-mounted solutions for high-density yards.

Core attributes:

• Performance Metrics: Energy use averages 4.4 kWh per move, with annual consumption around 4,400 MWh per unit (derived from terminal tractor and straddle carrier averages; ZEPA, 2024). They support hybrid charging options, including overhead pantographs for cranes or depot-based systems for trucks.

• Environmental and Efficiency Gains: These machines reduce air pollutants and noise, aligning with regulatory pressures (e.g., EU electrification mandates and US zero-emission rules). PEMA literature highlights their role in predictive analytics, where embedded sensors track utilization and health.

• Demand Outlook: ZEPA forecasts 400–800 BE-lift trucks for its operators by 2035, extrapolating to 2,700–5,300 globally. Combined with cranes, they represent a growing segment, with innovations like regenerative energy capture further lowering scope 2 emissions.

Although these machines provide notable sustainability advantages, the coexistence of data silos from different OEMs (original equipment manufacturers) may create inefficiencies. TIC4.0’s semantic standards offer a unified approach to data models, supporting consistent vehicle status reporting, operator coordination, and infrastructure compatibility.

Broader Ecosystem: Charging Infrastructure and Interdependencies

Across all types, BE-CHE relies on supporting infrastructure, including 1,300–2,100 chargers projected for ZEPA operators by 2035 (ZEPA, 2024), with 50–55% being conventional (350–500 kW) and 15–20% fast chargers. Global extrapolation suggests 8,700–14,000 units market-wide. This infrastructure introduces interdependencies, such as grid load management and battery swapping stations (requiring 4 chargers each), which demand real-time data sharing to avoid disruptions.

In summary, the diversity of electric terminal equipment—from eTTs to cranes—illustrates the multifaceted nature of port electrification. As highlighted in ZEPA and PEMA analyses, projected demand signals a tipping point toward widespread adoption, potentially requiring 800–1,200 GWh annually by 2035 for ZEPA fleets alone. However, realizing these benefits requires overcoming interoperability challenges. TIC4.0 standards can play a transformative role here, providing the data protocols needed to synchronize equipment, optimize charging, and foster a cohesive, sustainable port ecosystem. This foundation not only addresses current hurdles but also paves the way for future innovations in autonomous and connected operations.

The changes brought by the introduction of BE-CHE in container terminals, in addition to providing the benefits already discussed, also represent significant challenges and constraints. These can act as sources of resistance to change and, if poorly managed, may slow the adoption of these technologies. New challenges emerge in areas such as infrastructure, charging strategies, logistics, maintenance and operations. Many of these challenges can be mitigated through the development of comprehensive data standards that accurately represent the new realities emerging in terminals. Recent analyses show that many of the operational and energetic consequences of electrification only become visible when multiple parameters—battery size, number and type of chargers, SOC policies or charging windows—are considered together. Small design choices can have surprisingly large impacts on availability, energy peaks and overall productivity.

As noted in the PEMA Information Paper on Battery & Charging Solutions (2021), integrating batteries into equipment such as Rubber Tyred Gantry Cranes (RTGs), Straddle Carriers, and Automated Guided Vehicles (AGVs) requires careful planning of charging systems, grid interactions, and logistical workflows. This section examines these elements, highlighting how standardized frameworks like TIC4.0 can support data exchange for real-time monitoring of infrastructure, energy demand, and fleet coordination—helping to reduce downtime and optimize resource use. Electrification also presents clear economic barriers: terminals face high upfront investments in equipment, chargers and grid upgrades, dependence on incentives, and a strong reliance on ongoing reductions in OEM costs to ensure financial feasibility.

Charging solutions must align with the dynamic logistics of container terminals, where equipment operates in cycles involving hoisting, trolleying, and gantry movements. PEMA highlights that regenerative energy from lowering containers or decelerating drives can be captured and stored in batteries, reducing overall energy consumption by up to 73% in hoisting scenarios compared to diesel systems. Stationary charging, for instance, involves non-operational periods where equipment like RTGs or AGVs connect to fixed chargers, while inductive charging enables wireless energy transfer during operations, minimizing interruptions.

Integration challenges include synchronizing charging with container handling cycles to avoid bottlenecks. For example, in hybrid RTG setups, batteries supplement diesel gensets, allowing smaller engines to run at optimal efficiency and capture regenerative power. TIC4.0 standards support this by providing interoperable protocols for sharing data on battery state-of-charge (SOC), operational schedules, and energy flows, enabling predictive algorithms to balance charging with logistics demands and enhance overall terminal throughput. Even more: if weather monitoring systems are in place at the terminal, data from these can be aggregated to the equipment’s telemetry via data fusion to get a comprehensive picture on the battery’s workload.

As noted earlier in the information provided by Portwise, these complexities become even more evident when considering how different charging regimes create sharply different demand curves—some helping to smooth overall electrical load, others unintentionally producing demand spikes that strain the grid. Likewise, aiming for consistently high SOC levels may seem operationally conservative, yet it results in longer charging times, reduced equipment turnover, and potential negative impacts on quay crane productivity.

Terminal infrastructure must accommodate battery systems' energy needs, including cooling mechanisms (air or liquid) to maintain optimal temperatures and extend battery life. PEMA notes that battery capacity design involves trade-offs between diesel genset size, startup frequency, and fleet operational patterns—higher capacity batteries reduce genset runtime, lowering fuel use and emissions. In fleet contexts, equipment like Straddle Carriers and AGVs benefit from modular battery designs, allowing retrofit installations on existing diesel fleets for gradual electrification.

Fleet usage data indicates that batteries enable flexible operations, with energy capacities tailored to cycle life and depth of discharge (DOD). For instance, limiting DOD to shallower levels can triple or quadruple battery cycle life, supporting intensive port workflows. Infrastructure investments, such as cable reeling drums or conductor rails for electrified RTGs (E-RTGs), further optimize usage by providing grid power alongside battery storage. TIC4.0 facilitates fleet-wide monitoring through standardized data models, allowing terminals to track usage patterns, regenerative energy recovery, and infrastructure health for efficient scaling.

Charging availability is influenced by operational intensity and infrastructure design. PEMA describes solutions like plug-in or drive-in conductor rails for E-RTGs, which ensure high availability by allowing quick connections during brief stops, and cable reeling systems that provide continuous power without restricting movement. Inductive charging offers even greater flexibility, embedding coils in terminal pathways for opportunistic charging during gantry or trolley motions.

However, availability can be constrained by peak demands or grid limitations, particularly in high-throughput terminals. Battery management systems (BMS) monitor SOC and temperature to prioritize charging during low-activity periods, ensuring equipment uptime. TIC4.0 enhances this by enabling real-time data sharing on charger status, grid load, and vehicle locations, supporting dynamic scheduling to maintain availability.

Here again, operational trade-offs emerge: maintaining consistently high SOC levels requires long charging periods, reducing flexibility during busy windows. Without careful planning, these small choices can accumulate into measurable throughput losses across several pieces of equipment.

Regulatory frameworks increasingly push electrification. Standards such as UL1642 in the US focus on lithium-ion safety and thermal-runaway prevention, while the EU Battery Regulation 2023/1542 mandates life-cycle management, stronger information requirements, and more standardized, replaceable battery modules. PEMA also highlights compliance with international certifications like UN 38.3 for transport and installation, influencing grid integration and surplus energy management.

Surplus renewable energy can reduce charging costs through off-peak incentives. Batteries lower operating expenses by capturing regenerative energy and eliminating diesel use, while higher initial infrastructure costs are offset by long-term savings—often up to 50%—and potential revenue via vehicle-to-grid (V2G) services. TIC4.0 supports regulatory compliance by standardizing data for emissions reporting and grid interaction, enabling access to subsidies and addressing grid-stability and environmental concerns.

Electrification transforms operational planning and maintenance in container terminals, shifting from diesel-centric models to battery-integrated systems that prioritize energy efficiency and predictive strategies. The PEMA Information Paper (2021) details how batteries in equipment like RTGs and AGVs reduce maintenance needs through fewer mechanical components and regenerative energy utilization. This section examines these impacts, highlighting TIC4.0's role in providing a unified data framework for planning, repairs, and workforce adaptation, ensuring minimal disruptions in data-driven port ecosystems.

Battery systems introduce changes in maintenance protocols, focusing on electrical and thermal management rather than combustion engines. PEMA indicates that lithium-ion batteries (e.g., NCM or LFP chemistries) are largely maintenance-free when operated within safe windows, but require specialized tools for diagnostics, such as voltage/current monitors and thermal imaging for detecting degradation. Infrastructure adaptations include dedicated cooling systems and protective enclosures to prevent corrosion or overheating, with modular designs enabling quick module swaps in RTGs or Straddle Carriers.

Repair processes evolve to address failure modes like overvoltage or high temperatures, using BMS to isolate issues at cell, module, or system levels. Retrofitting diesel equipment with batteries necessitates infrastructure upgrades, such as reinforced mounting for heavier packs. TIC4.0 standardizes fault data exchange, integrating with terminal operating systems (TOS) for automated alerts and reducing repair times.

Battery adoption affects planning by incorporating variables like SOC, cycle life, and regenerative energy into workflows. PEMA explains that hybrid systems optimize genset runtime, planning operations around battery discharge for fuel savings, while full battery-operated equipment requires scheduling charging during non-peak hours to maintain productivity. Maintenance planning shifts to condition-based models, using data on DOD and temperature to predict needs, extending equipment life and aligning with container handling cycles.

Operational volatility, such as varying load demands, demands flexible planning—batteries enable energy buffering, but require forecasting tools to balance grid draw and regenerative capture. TIC4.0 supports this through semantic standards for real-time data on energy flows and equipment status, enabling scenario modeling and integrated planning across operations and maintenance to handle throughput fluctuations effectively.

The strategic choices behind battery sizing, charger distribution and charging policies carry substantial operational consequences. Increasing battery capacity can improve autonomy but comes at a cost in weight and CAPEX; adding chargers enhances flexibility but raises peak load; and redistributing charging windows can reshape the terminal’s daily electrical consumption. Understanding these interactions is essential for making sustainable, data-driven decisions.

The shift to batteries requires workforce upskilling in areas like high-voltage safety, BMS operation, and thermal management. PEMA notes that while batteries reduce routine maintenance (e.g., no oil changes), workers must understand chemistries (e.g., NCM for power applications) and standards like UL1642 to handle risks such as thermal runaway. Training focuses on diagnostic software and safe handling, evolving roles from mechanics to technicians proficient in data analysis.

This impacts job structures, fostering high-tech environments with emphasis on sustainability. Regulatory pressures for green ports amplify the need for knowledge in energy efficiency and emissions reduction. TIC4.0 eases adaptation by providing intuitive, standardized interfaces for data dashboards, lowering the technical barrier and supporting workforce reskilling for resilient, electrified operations.

Battery health and reliability are foundational to the performance of electrified port equipment, with monitoring systems essential for mitigating degradation and ensuring operational continuity. The PEMA Information Paper (2021) discusses factors influencing lithium-ion battery longevity in applications like RTGs and AGVs, including environmental conditions and usage patterns. This section addresses these aspects, underscoring how TIC4.0's data protocols can enhance monitoring accuracy and predictive capabilities.

Battery reliability in ports is tested by harsh conditions like humidity, salt exposure, and temperature extremes. PEMA highlights that high temperatures accelerate chemical reactions, reducing reliability, while low temperatures impair discharge rates. Cooling systems maintain optimal ranges (e.g., 25°C), with enclosures protecting against environmental damage. In wet or corrosive settings, reliability is bolstered by sealed designs and BMS oversight.

Weather-dependent variability affects regenerative energy capture, with extremes potentially shortening cycle life. TIC4.0 enables reliability through standardized sensor data integration, allowing real-time adjustments for weather impacts and maintaining performance in diverse climates.

Degradation manifests as capacity fade, influenced by DOD, SOC, temperature, and C-rate (capacity rate, describes the rate at which a battery is charged or discharged relative to its nominal capacity.). PEMA notes that deeper DODs nonlinearly reduce cycle life (e.g., shallower discharges can extend life threefold), while high SOC and temperatures exacerbate calendar aging via side reactions. Health status (SOH) estimation remains challenging, relying on indirect metrics like voltage and impedance, with factors like overcharging or high C-rates accelerating irreversible losses.

Today, SOH is often an estimation from partial data, as nonlinear behaviors complicate precise measurement. TIC4.0 aids by standardizing multi-factor data models, improving estimation granularity for proactive health management.

Estimating SOH is difficult due to nonlinear aging and limited sensors, leading to inaccuracies in predicting remaining useful life. PEMA emphasizes that calendar aging persists even in storage, with high SOC worsening it. Additional data— from telematics on cycles, temperature logs, and operational patterns—refines models, potentially increasing accuracy by 20-30%.

Challenges include data silos, but aggregating sources like weather and usage enhances estimations. TIC4.0 resolves this by providing interoperable frameworks for multi-modal data, transforming rough approximations into reliable insights for maintenance and optimization.

Expanded monitoring across equipment, infrastructure and environmental conditions significantly improves battery life estimation, supporting more reliable planning and reducing unplanned downtime.

Currently, the following initiatives have been started by ISO, IEC and other standardisation organisations to regulate electric vehicle charging, safety requirements and other vehicles. In these sections, only the standards related to the topic being addressed are presented. Future publications will explore the content in greater detail.

Main Systems Committee

• IEC SyC SET — Systems Committee on Sustainable Electrified Transport
  Coordinates standardization activities across IEC and ISO related to sustainable and electrified transport.

IEC (International Electrotechnical Commission)

1. IEC TC 69
   Electrical power/energy transfer systems for electrically propelled road vehicles and industrial trucks

   • Covers standards for charging systems, conductive and inductive power transfer, and general requirements for EV energy supply.

2. IEC TC 69 WG14
   EV supply equipment with automated connection of a vehicle coupler

   • Focuses on standards for automated charging (e.g., robotic connectors, wireless interfaces for automated systems).

3. IEC SC 23H
   Plugs, socket-outlets and couplers for industrial and similar applications, and for electric vehicles

   • Deals with connector design and safety, including EV-specific plugs and couplers.

ISO (International Organization for Standardization)

4. ISO TC 22 WG31
   Data communication (road vehicles)

   • Standardizes vehicle communication protocols, likely including diagnostics, telematics, and vehicle-to-infrastructure communication.

5. ISO TC 22 WG37
   Electrically Propelled Vehicles

   • Defines requirements and standards for EV performance, safety, and interoperability.

6. ISO TC 204 WG14
   Vehicle/roadway warning and control systems

   • Focused on Intelligent Transport Systems (ITS), including vehicle-to-roadside communications, cooperative systems, and traffic safety.

7. ISO TC 299
   Robotics

   • Covers safety, terminology, and performance standards for robotics, with possible applications in automated EV charging or mobility systems.

ISO (International Organization for Standardization)

1. ISO TS 5474-5 — Automatic conductive power transfer

   • Defines requirements for conductive (plug-based) systems where charging is done automatically (without manual connection).

   • Already published (Q1 2024).

2. ISO 12768-1 — AVDS – Requirements, System Framework, Communication Interfaces and Test Procedures

   • AVDS = Automated Vehicle Docking Systems.

   • Covers framework, interfaces, and testing methods for automated docking between EVs and charging stations.

   • Status: CD going to DTS (Q2 2025) → moving from Committee Draft to Draft Technical Specification.

IEC (International Electrotechnical Commission)

3. IEC TS 61851-27 (–26) — EV supply equipment with automatic docking of a vehicle coupler according to IEC 62196-2, -3, -3-1 or IEC 63379

   • Provides technical specs for equipment enabling automatic docking connectors (robots, mechanical guides, etc.) using standardized couplers.

   • Status: In edit DTS 2 (Q3 2025).

4. IEC TS 61851-28 — Communication between EV supply equipment with automatic docking and electric vehicles

   • Defines how EVs and charging stations “talk” to each other when connectors dock automatically.

   • Status: Committee Draft (CD), expected Q4 2025.

5. IEC 62196-2 / -3 / -3-1 / IEC 63379 — Connector systems for EVs

   • Core connector interface standards:

     • IEC 62196-2: AC charging couplers.

     • IEC 62196-3: DC charging couplers.

     • IEC 62196-3-1: Additional specs/extensions for DC connectors.

     • IEC 63379: Likely focused on high-power/next-gen connectors.

   • Status: Periodic Review (Q4 2024) → older standards under review for update or confirmation.

IEC 62619 — Safety requirements for industrial lithium batteries

• Scope: Applies to secondary lithium-ion cells and batteries used in industrial applications (not consumer electronics).

• Key aspects:

  • Covers design safety (cell chemistry, separators, venting, thermal management).

  • Abuse testing: overcharge, external short-circuit, forced discharge, mechanical shock, vibration, crush, and fire exposure.

  • System-level safety: monitoring circuits, Battery Management Systems (BMS), protection against overcurrent, overheating, and overvoltage.

  • Exclusion: Portable consumer electronics (covered by IEC 62133).

• Relevance for cranes/reach stackers: Ensures that the large lithium packs powering these machines won’t create thermal runaway hazards in heavy-duty environments.

IEC 62620 — Performance testing for secondary lithium batteries (industrial applications)

• Scope: Defines performance and reliability criteria for lithium-ion rechargeable batteries used in industrial applications (including traction batteries for material handling vehicles).

• Key aspects:

  • Capacity testing (Ah rating verification).

  • Cycle life (number of charge/discharge cycles until capacity degradation).

  • Charge retention & self-discharge.

  • Efficiency during charge/discharge.

  • Endurance tests: performance under partial cycling, temperature stresses.

• Relevance for ports/logistics: Helps OEMs and operators compare performance of different battery suppliers in forklifts, cranes, yard tractors, etc.

IEC 62933 series — Electrical Energy Storage (EES) systems

• Scope: A broad family of standards for grid-connected and stationary storage systems, but increasingly relevant to large port-side charging hubs and energy storage integration.

• Structure:

  • 62933-1-1: Terminology.

  • 62933-2-1: General safety requirements for EES systems.

  • 62933-2-2: Safety aspects of cells/modules for EES.

  • 62933-3-1: Performance requirements.

  • 62933-4-x: Environmental impact (life cycle, recyclability).

• Key aspects:

  • System-level safety (isolation, fire suppression, grid protection).

  • Compatibility with renewables and smart grids (important in green ports).

  • Cybersecurity for grid-connected storage systems.

• Relevance for ports: If a port sets up a charging station for electric reach stackers or cranes, the underlying stationary storage (big lithium or hybrid systems) must comply with this series.

The effective operation of BE-CHE in port terminals relies on the timely, accurate, and standardized exchange of data across various systems and stakeholders. This section outlines the key information requirements necessary to achieve the objectives of smart EV operations, such as optimizing fleet utilization, minimizing downtime, ensuring sustainability, and enabling seamless interoperability.

To structure these requirements, we focus on four core themes: Telemetry (IoT) execution, CHE management system with an emphasis on energy consumption information needed by the TOS, Charging Management System (CMS), and Terminal Energy Management System (TEMS). These themes represent interconnected layers of data collection, processing, and decision-making. Telemetry provides real-time sensor-based data from eCHE and equipment; the CHE management system integrates energy consumption insights to inform TOS-driven planning; CMS handles charging-specific operations to align with logistics; and TEMS oversees broader energy orchestration across the terminal, including grid interactions. By analyzing and assigning relevant information to each theme, we ensure a holistic framework that supports predictive analytics, maintenance, and operational efficiency, all underpinned by TIC4.0's semantic standards for interoperability.

Below, we develop the four themes, mapping specific information elements from eCHE systems, vehicle parameters, TOS, charging infrastructure, electrical grid, fleet management, and orchestration systems to each. This analysis is derived from use cases such as battery health monitoring, charging opportunity identification, job order coordination, and predictive energy demand forecasting.

Telemetry involves the real-time collection and transmission of data from IoT sensors embedded in eCHE and related infrastructure. This theme focuses on raw, execution-level data that enables monitoring and immediate feedback loops. Telemetry data is sensor-driven and time-sensitive, supporting functions like fault detection and performance tracking without higher-level processing. As highlighted in the PEMA Battery & Charging Solutions paper, telemetry is crucial for capturing regenerative energy from gravitational potential during lifting/lowering and deceleration, which can be stored in batteries for reuse, reducing overall energy consumption.

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Hierarchical Structure of the CHE Battery System

CHE data

Identification (unique ID, type, brand, model) provides the foundation for asset management, ensuring traceability and alignment across different systems. Operational parameters encompass Location (logical positioning within the terminal and coordinate-based tracking such as GPS or RTLS), as well as Powered states (On, Off, Standby, with associated status and duration). Complementing this, Working modes (Idle, Driving, Working) integrate speed and distance metrics, supporting real-time utilization analysis and the coordination of charging opportunities. These operational elements enable terminals to evaluate availability, optimize task allocation, and minimize idle times.

Energy-related data captures the complete cycle of power management: consumed, produced, and recovered energy, including flow in/out metrics, recovery cycles, consumption per move, and efficiency indicators. These inputs allow predictive charging management, dynamic load balancing, and integration with Charging Management Systems (CMS) and Terminal Energy Management Systems (TEMS). Maintenance parameters extend to Health monitoring (event type, level, counters, timers), error messages, as well as structured warnings and faults (status and duration). This dataset supports predictive maintenance, early anomaly detection, and overall lifecycle management of equipment, in alignment with manufacturers’ telemetry standards and operators’ operational requirements.

Taken together, these dimensions provide the aggregated representation of the CHE subject, where identification, operational status, energy performance, and maintenance data are viewed as a whole. It is a consolidated layer that captures the essence of the equipment, without going into the detail of its internal components, and serves as the semantic description of the CHE entity within the terminal context.

Energy-related data captures the complete cycle of power management: consumed, produced, and recovered energy, including flow in/out metrics, recovery cycles, consumption per move, and efficiency indicators. These inputs allow predictive charging management, dynamic load balancing, and integration with Charging Management Systems (CMS) and Terminal Energy Management Systems (TEMS). Maintenance parameters extend to Health monitoring (event type, level, counters, timers), error messages, as well as structured warnings and faults (status and duration). This dataset supports predictive maintenance, early anomaly detection, and overall lifecycle management of equipment, in alignment with manufacturers’ telemetry standards and operators’ operational requirements.

Taken together, these dimensions provide the aggregated representation of the CHE subject, where identification, operational status, energy performance, and maintenance data are viewed as a whole. It is a consolidated layer that captures the essence of the equipment, without going into the detail of its internal components, and serves as the semantic description of the CHE entity within the terminal context.

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Semantic Structure of the CHE Entity

Powersource data

Within the semantic structure of CHE, the Power Source represents a more specific layer of data, focused exclusively on the energy supply that enables equipment operation. Unlike the aggregated CHE view, here the parameters are narrowed to describe the condition and performance of the power unit itself, capturing only the information that is relevant for fleet-level management and energy optimization.

Identification parameters (unique ID, type, brand, model) establish the reference for distinguishing and classifying each power unit. This is particularly relevant in mixed fleets where differents fuel types (e.g. diesel, hybrid, and electric power sources) coexist, ensuring that operational data can be consistently attributed and compared. Operational and maintenance data include Powered states (On, Off, Standby, with status and duration), Working and Idle conditions, as well as Energy flows (consumed, produced, recovered, inflow/outflow, recovery cycles, consumption per move, and efficiency indicators). These values allow terminals to monitor fuel or energy performance, anticipate charging or refueling needs, and align fleet availability with operational demands. Maintenance adds another dimension, covering Health events, errors, warnings, and faults with their respective status, levels, and durations—providing essential insights for predictive fleet management and lifecycle planning.

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Semantic Structure of the Power Source Entity

Battery-related data

In the context of this paper, we will focus exclusively on lithium-ion battery systems, which are the dominant technology in industrial and electric vehicle applications due to their energy density, efficiency, and lifecycle performance. The architecture considered follows a hierarchical structure where the Battery Pack is managed by a Master Battery Management System (MBMS). This master unit monitors, controls, and communicates aggregated information from the underlying modules and cells. While in practice it is possible for multiple BMS units to exist—some operating at the module level—there is always a supervisory Master BMS coordinating the pack as a whole. Importantly, the Master BMS can transmit detailed information referring to individual modules or cells, since each of them is identified with a unique ID.

Identification parameters (ID, type, brand, model) ensure traceability of each battery pack and support consistency across fleet operations. Operational data include powered states (On, Off, Standby, with duration and status), working and idle conditions, as well as energy values such as State of Charge (SOC), State of Health (SOH), voltage levels (absolute and max/min ranges), current, current transducer data, and discharge limitations. Together, these parameters form the basis for monitoring battery condition, planning charging cycles, and ensuring safe operation within fleet contexts. Maintenance data extend this with health events, counters, timers, error messages, warnings and faults, along with associated Failure Mode Identifier (FMI)/ Suspect Parameter Number (SPN) codes. Last, but not least, DTC (diagnostic trouble codes) that capture ID, location, and derating conditions will also be taken into consideration.

For the purposes of this chapter, we will remain at the Battery Pack level of abstraction (within TIC4.0 this is called Energy Tank) , without going into the detail of modules and cells. From an operational standpoint, terminal management requires aggregated data at the pack level, since the Master BMS already consolidates lower-level information into actionable outputs. Consequently, the Battery Pack subject provides the operational and maintenance dataset most relevant for fleet and energy management, while also offering a semantic representation that is both comprehensive and practical for terminal operations. Any description of more detailed information will only be developed where necessary, primarily in the context of functions related to maintenance and battery lifecycle monitoring.

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Semantic Structure of the Energy Tank Entity

Being able to standardize the communication of BE-CHE related data while clearly distinguishing between subjects (such as the CHE as a whole) and sub-subjects (such as Power Source or Battery Pack) is essential to ensure consistency, clarity, and operational value. Standardization guarantees that information is shared in a common language across systems and stakeholders, while differentiation allows each layer—from aggregated equipment status to specific energy units—to be represented at the appropriate level of detail. This balance enables effective fleet and energy management, supports interoperability across terminal systems, and ensures that data remains both actionable for operations and structured for long-term monitoring and optimization.

The Terminal Control System represents the operational layer that transforms telemetry into decision-ready information for the Terminal Operating System (TOS). Its primary function is not only to describe how much energy each piece of equipment consumes, but to ensure that this information actively supports operations: assigning jobs to the right machine, preventing unplanned downtime, and balancing the use of resources across the fleet. By aligning energy availability with logistics requirements, the CHE Management System becomes a tool for continuous operational optimization.

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Operational Functions Enabled by the CHE Management System

Energy Consumption Profiles

Energy consumption profiles translate raw sensor data into practical metrics such as kWh per container move, total consumption over shifts, and energy recovered through regenerative braking. For operations, this means that dispatchers and planners can understand at a glance whether a machine has enough energy to complete its next mission. Instead of treating energy as an abstract figure, the system expresses it in terms of operational capacity—how many moves, how many shifts, how many jobs can still be performed before charging is required.

Job-Related Coordination

Job orders issued by the TOS are matched against the real-time status of each CHE. If a straddle carrier is requested for a stacking cycle, the system checks whether it has sufficient energy for the full operation, taking into account the predicted consumption of the task. This prevents scenarios where a machine runs out of charge mid-operation, disrupting the flow of containers. In practice, it allows operations to continue smoothly, with energy availability embedded in the logic of task assignment.

Battery and Vehicle Integration

The system goes beyond SOC readings to incorporate the condition of the battery and vehicle into operational planning. For example, a yard tractor with a high SOH and stable temperature profile is a more reliable candidate for intensive moves than one showing early signs of degradation. Similarly, regenerative braking data can indicate which vehicles are better suited for routes with frequent stops. In this way, operational efficiency is directly tied to energy intelligence, allowing planners to use the fleet in the most effective way possible.

Fleet-Level Aggregation

From an operational standpoint, it is not enough to know the state of a single vehicle. The Terminal Control System aggregates the energy status of the entire fleet, showing cumulative demand, predicted peaks, and recommended load balancing strategies. This supports supervisors in making fleet-wide decisions: when to rotate equipment, how to prioritize charging, and how to avoid bottlenecks during peak throughput. The outcome is a fleet that behaves as a coordinated whole, rather than a collection of independent units.

Predictive Analytics and Alerts

Operational continuity depends on anticipating problems before they disrupt workflows. The CHE Management System compares real consumption against expected patterns, generating alerts when anomalies appear—such as higher-than-normal energy draw that could indicate mechanical stress, or insufficient charging opportunities before the next shift. These predictive warnings allow maintenance teams and operations control to act before issues escalate, ensuring that container flows remain uninterrupted.

It has been intentionally avoided to define which specific systems will be responsible for executing these functions within the operational layer, as there is no single solution applicable to all terminals. Depending on the terminal’s configuration and system architecture, these functions may be implemented across different systems such as the Terminal Operating System, the Fleet Management System (FMS), the Equipment Control System (ECS), or others fulfilling equivalent roles.

The Charging Management System (CMS) specializes in orchestrating charging processes, ensuring alignment with operational logistics and vehicle availability. CMS processes data from telemetry and CHE systems to manage charging orders, prioritize sessions, and handle interdependencies like battery swapping or fast charging. This theme focuses on minimizing downtime by synchronizing charging with dynamic workflows.

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CMS Data Ecosystem

BE-CHE Data

BE-CHE data includes the unique vehicle ID and type of CHE, which link charging needs to specific equipment. Battery information such as SOC, SOH, temperature, voltage and current determines the charging profile and the impact on performance. Charging requirements specify the energy level, connector, available power and time windows to manage the process within operational constraints. Location within the terminal indicates proximity to chargers and reduces unnecessary repositioning.

Charge Point Data

Charge point data covers the identification of each charger, its type and technical specification, allowing accurate assignment. Availability status—free, occupied, under maintenance or in error—defines immediate usability. Capacity data shows both the rated power and currently available power to plan effective charging distribution. Operational status provides alarms, fault notifications and logs for safe operation and future analysis.

Electrical Grid Data

Grid data contains the total local grid capacity and contracted maximum power, which set the boundaries for simultaneous charging. Real-time consumption values from the whole terminal and individual sectors give visibility of demand patterns. Constraints include peak demand limits and regulatory restrictions that condition flexibility. Tariff signals and cost variations guide the scheduling of charging towards lower-cost periods.

TOS Data

TOS data defines operational planning with work windows, shifts and mission assignments, indicating when each CHE can be taken out of service. Time slots between operations represent the actual charging opportunities. Prioritization rules identify which vehicle should be charged first to maintain workflow continuity.

Security and Maintenance Data

This dataset includes charging history with completed cycles and anomalies to monitor performance over time. Safety alarms report events such as overheating, short circuits or disconnections that require immediate action. Predictive maintenance uses sensor data to anticipate failures and plan interventions with minimal disruption.

External Data

External inputs include weather forecasts to balance renewable energy integration and prevent overload situations. Regulatory data adds constraints such as emission limits and sustainability reporting requirements. Integration with EMS or Smart Grid platforms enables coordination with city or port energy systems.

Modern electrified terminals – such as ports operating fleets of electric cargo-handling equipment – depend on advanced energy management to balance power supply and demand in real time. The terminal’s energy system integrates the external grid, internal control systems, and multiple electrical loads like cranes and vehicles. To ensure reliability and efficiency, the energy management function exchanges information across several domains. Below is a structured overview of the main information categories involved.

Operational Demand

Operational demand reflects the terminal’s energy needs at any given time. It includes data on equipment use, task scheduling, and throughput plans that indicate when heavy loads will occur. By forecasting and monitoring consumption, the system can align supply with operations and avoid demand peaks. Predictions to optimize. Coordinate predictions from operations and from energy management system.

Example data: real-time CHE consumption, work schedules or task plans, and historical load profiles.

Electrical Grid Capacity and Electrical Power Monitoring

This category concerns the capacity and condition of the external grid connection, including maximum import limits, current draw, and power quality. The TEMS must operate within grid constraints, preventing overloads and responding to grid events or maintenance alerts.

Example data: live power draw versus capacity, voltage and frequency levels, and utility outage or curtailment notices.

Availability of Internal Energy Resources (Renewables & Storage)

Covers on-site renewable generation and storage. By managing solar, wind, and batteries, the TEMS balances self-supply and grid dependence, charging storage during surplus and discharging during peaks.

Example data: renewable output and forecasts, battery SOC and capacity, and expected renewable availability.

Electricity Market Conditions

Refers to external price signals and tariffs guiding energy use. The TEMS adapts operations to electricity prices and demand-response programs to reduce costs or generate revenue.

Example data: real-time and day-ahead prices, demand response requests, and tariff thresholds.

Critical Events and Alarms

Includes all fault and safety alerts affecting power systems. The TEMS must detect and act on equipment failures or abnormal conditions to ensure continuity and safety.

Example data: equipment fault alarms, threshold exceedance warnings, and emergency shutdown events.

Grid Operator Signals

External control or compliance signals from utilities guide terminal behavior. The TEMS reacts to load-reduction requests, curtailment orders, or compliance requirements.

Example data: demand-response notifications, grid emergency signals, and regulatory compliance data.

Environmental and Weather Conditions

Environmental data informs how external conditions affect both energy supply and demand. Weather influences renewable output and cooling/heating needs, while extreme events require preventive action.

Example data: weather forecasts, real-time sensor readings, and storm or flood alerts.

A terminal energy management system acts as a central hub that integrates diverse data to control and optimize energy flow. By managing demand, grid capacity, local generation, market signals, charging, alarms, and weather data, it ensures operations remain reliable, economical, and sustainable — enabling smarter, cleaner port terminals.

This section defines how information flows across the functional layers that enable BE-CHE operations in a terminal: Equipment Data, Operational Data, Charging Control, and Energy Management. The focus is deliberately information-centric: we describe what information moves where and for what purpose, rather than prescribing which software product or module must perform each task. Each terminal may allocate decision-making and execution responsibilities to different systems according to its own constraints, maturity, and integration strategy.

The layers used here represent functions that must be performed—capturing field data from BE-CHEs, orchestrating operations, executing and supervising charging, and balancing energy supply and demand—without implying a rigid system architecture. By clarifying the types of information exchanged between layers and the role each layer plays in using that information (e.g., monitoring fleet condition, scheduling missions, coordinating charging, and enforcing grid or cost constraints), terminals can design interoperable solutions that remain technology-agnostic and future-proof.

The diagram that follows summarises these bidirectional exchanges and anchors the subsequent interface descriptions.

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The interaction between the Equipment Data Layer and the Operational Data Layer ensures that operational planning and execution are continuously informed by the real condition of the electric fleet. The Terminal Contol System relies on telemetry data to understand how vehicles are performing, where they are operating, and what their energy status is. By analysing this information, the operational system can make projections about fleet availability, schedule upcoming missions, and anticipate when vehicles will require charging or maintenance.

Conversely, the operational layer provides the CHE with task-level instructions that define how, when, and where to act. These instructions can include routing commands, mission priorities, or timing information aligned with terminal operations. Through this bidirectional exchange, the operational layer maintains control and visibility over the fleet, while the equipment layer ensures that operations reflect real-world conditions and energy constraints.

Information from CHE to Terminal Control System

• Operational Status: active, idle, or under maintenance.

• Energy Condition: current SOC, SOH, and consumption rates.

• Performance Data: utilisation, cycle times, and efficiency metrics.

• Location and Movement: real-time position and routing path within the terminal.

• Diagnostic Data: faults, warnings, or deviations affecting performance.

This information enables the operational system to track fleet readiness, optimise deployment, and update operational schedules dynamically based on vehicle condition and energy availability.

Information from Terminal Control System to CHE

• Task and Instructions: assignments defining routes, start and end points, and operation type.

• Scheduling Information: start and stop commands, idle windows, and time slots for charging or standby.

• Operational Constraints: mode selection (manual, semi-automatic, autonomous), and safety or speed limits.

These exchanges ensure that each CHE executes tasks according to operational priorities while respecting the constraints imposed by its current energy state and terminal conditions.

Building upon the operational awareness established through telemetry, the next interface connects the Operational Data Layer with the Charging Control Layer. Once the operational system has an accurate picture of the fleet’s status and availability, this information must be shared with the function responsible for managing charging activities — ensuring that the charging process aligns with mission scheduling and energy requirements.

It is important to note that not every terminal operates a dedicated CMS. In some cases, this functionality may be integrated into the TOS, FMS, or another supervisory platform. Regardless of its implementation, the charging management function must exist somewhere in the digital ecosystem to translate operational demand into charging execution.

Within this relationship, the Terminal Control System communicates the demand — which vehicles need energy, when, and with what urgency — while the CMS (or equivalent function) ensures that such demand is fulfilled, according to infrastructure capacity and energy availability.
This bidirectional exchange establishes a continuous operational-energy loop: operations drive the demand for energy, and the charging layer provides the feedback required to maintain that demand under control.

Information from Terminal Control System to CMS

• Charging Demand Information: identification of vehicles requiring energy, including estimated power needs and time sensitivity.

• Operational Priority: criticality of each vehicle according to upcoming missions or productivity impact.

• Availability Windows: timeframes when vehicles are idle and can be connected to a charger.

• Location and Resource Data: physical position of vehicles and nearby charging infrastructure.

This information allows the charging management function to schedule charging sessions efficiently, prioritising vehicles according to operational needs while minimising interference with ongoing tasks.

Information from CMS to Terminal Control System

• Charging Status: real-time progress of charging sessions, SOC evolution, and estimated time to completion.

• Vehicle Readiness: projection of when each vehicle will be available for operation.

• Infrastructure Status: charger condition, occupancy, and maintenance activity.

• Energy Supply Condition: feedback on grid or capacity constraints that may affect charging performance.

Through this feedback, the operational layer maintains visibility over fleet readiness and infrastructure capability, allowing it to adjust plans dynamically and ensure that energy constraints do not disrupt terminal performance.

Once the operational system has instructed a vehicle to proceed to a charging point and the connection has been established, a direct communication link is created between the Charging Control Layer and the Equipment Data Layer.
From that moment, the interaction becomes real-time and technical, focusing on the execution, supervision, and safety of the charging session.
While the CMS governs the session logic — such as authorisation, power allocation, and termination — the CHE provides continuous feedback on its battery condition and system status, ensuring the process remains stable, safe, and efficient.

In terminals where a dedicated CMS is not implemented, these functions may be embedded within other systems, or managed directly through intelligent chargers. Regardless of how it is implemented, the charging control function must exist to coordinate and monitor the physical charging process once the vehicle is connected.

Information from CMS to CHE

• Charging Session Control: commands to start, stop, or modulate the charging process according to defined parameters.

• Charging Power and Profile: power delivery settings, charging rate, and phase transitions (e.g., slow, fast, balancing).

• Progress and Authorisation: confirmation of session start, safety checks, and authorisation to continue.

• Safety and Emergency Signals: protective shutdowns or stop commands in case of irregularities.

This information ensures that the charging session proceeds under safe and optimised conditions, following the parameters defined by the charging management logic or system.

Information from CHE to CMS

• Battery Condition: State of Charge (SOC), State of Health (SOH), voltage, current, and temperature readings.

• Connection and Identification Data: confirmation of charger connection, vehicle ID, and communication status.

• Charging Feedback: power acceptance, deviations from the requested profile, or required adjustments.

• Fault and Diagnostic Data: alerts, warnings, or safety triggers detected by the vehicle’s internal systems.

This feedback allows the CMS to continuously adjust charging parameters, respond to anomalies, and maintain full visibility of the process until completion. Once charging is finished, the CMS updates the operational layer (Terminal Control System) to confirm the vehicle’s readiness and release the charging point for the next session.

The interaction between the Charging Control Layer and the Energy Management Layer connects real-time charging operations with the broader energy context of the terminal.
While the CMS focuses on managing charging sessions at the equipment level — ensuring that each vehicle is charged safely and according to operational priorities — the TEMS oversees the availability, capacity, and cost of electrical supply across the entire terminal.
This relationship ensures that the charging process does not operate in isolation but as part of a coordinated energy ecosystem that balances operational demand with grid limitations and cost-efficiency.

In some terminal configurations, a dedicated TEMS may not exist as a standalone system; instead, these functions might be distributed across the CMS itself, an EMS, or the terminal’s supervisory control layer. Regardless of implementation, the energy management function must ensure that charging activities remain aligned with both real-time supply conditions and strategic energy objectives.

Information from CMS to TEMS

• Energy Demand Information: aggregated and forecasted energy demand from all chargers, including predicted peaks and total load profiles.

• Charging Schedule: planned sessions, expected consumption periods, and infrastructure load distribution.

• Infrastructure Status: charger availability, faults, or maintenance conditions that may influence energy planning.

• Event and Alert Data: deviations between planned and actual demand or unexpected power surges.

This information enables the TEMS to anticipate power requirements, prepare supply strategies, and ensure the terminal’s electrical infrastructure can support upcoming charging operations.

Information from TEMS to CMS

• Energy Supply Information: available grid capacity, renewable energy contribution, and power constraints.

• Tariff and Cost Signals: dynamic pricing, time-of-use rates, and optimal cost periods for charging.