Real-time testing and simulation for battery energy storage systems

Key Takeaways

• Real time testing shifts BMS risk from the field to the lab, so teams can expose timing issues, protection gaps, and integration faults before hardware reaches the site.
  
• A strong battery management system depends on accurate measurement, robust safety limits, and reliable state estimation that are all validated across realistic operating and fault conditions.
  
• Battery simulation and hardware in the loop provide scalable ways to extend BMS testing coverage to ageing effects, temperature extremes, sensor imperfections, and communication faults.
  
• Integrating real time simulation into BMS validation shortens firmware cycles, supports structured regression testing, and helps align engineering work with safety standards and regulatory expectations.
  
• Investment in models, automated benches, and shared data practices builds lasting confidence in the safety, performance, and reliability of battery energy storage systems.

Grid storage only feels safe and reliable when you trust the batteries behind it. If you design or validate large energy storage systems, you probably carry a mental list of failure modes that keep you alert. Voltage excursions, communication glitches, and unexpected temperature swings can all show up after the system is installed, when every mistake costs more time and money. Real-time testing offers a way to move those surprises into the lab, where models, simulators, and hardware work side by side to expose issues before field deployment.

Battery energy storage systems now sit inside many solar parks, industrial sites, and commercial campuses. The battery management system coordinating each rack has to maintain safety, regulate performance, and interact cleanly with power electronics and grid controllers. You face pressure to shorten project timelines while still proving that BMS testing covers faults, edge cases, and long duration cycling. A practical approach to real-time testing, battery simulation, and model based validation helps you meet those expectations without gambling on trial and error.

What is a battery energy storage system and how it works

An industrial battery energy storage system uses rechargeable cells to capture electrical energy for later release when the grid or local loads need support. During charging, the system converts alternating current from the grid or a renewable source into direct current, then stores that energy in electrochemical form inside the cells. During discharge, the power conversion system inverts the direct current back into alternating current with controlled voltage, frequency, and power factor. The result is a controllable power source or sink that can follow a schedule or respond to grid signals in real time. 

Inside the enclosure, individual cells are grouped into modules, then into racks, and finally into full strings that reach the overall system voltage and capacity. A battery management system monitors cells and modules, while a power conversion system, protection devices, and thermal management keep the installation within safe operating limits. An energy management system coordinates charging and discharging based on price signals, grid constraints, or local control strategies. Each layer plays a different role, and all of them must work in sync to avoid unsafe conditions, unexpected trips, or poor performance.

Grid operators and project owners use battery energy storage systems for several purposes such as peak shaving, renewable smoothing, frequency control, and backup power. Storage can absorb excess power when solar or wind generation is high, then discharge during evening peaks or during contingency events. The same installation may participate in multiple services during a day, placing additional stress on cells and the control software. That usage pattern makes strong modelling, clear control strategies, and careful testing even more important for long-term success.

“Real-time testing offers a way to move those surprises into the lab, where models, simulators, and hardware work side by side to expose issues before field deployment.”

How a battery management system (BMS) supports large-scale storage systems

A battery management system monitors and manages the health of every cell, module, and rack within a storage installation. It measures voltage, current, and temperature, calculates state of charge and state of health, and enforces limits that keep the batteries operating safely. In utility scale battery energy storage systems, the BMS usually follows a multi level architecture, with rack controllers, string controllers, and a master unit linked to the power conversion system and energy management system. Engineers rely on this control layer to detect abnormal conditions early, isolate faults, and prevent minor issues from escalating. 

As systems scale from a few racks to hundreds, the BMS layer becomes a central coordination point for safety, lifetime management, and performance. It must balance measurements from thousands of cells, enforce consistent limits across cabinets, and present clear information to plant operators and remote monitoring systems. Design choices in BMS hardware, software, and communication links therefore have direct impact on uptime, serviceability, and warranty costs. Vigorous BMS testing and validation give you confidence that these functions remain reliable under regular operation, grid events, and fault cases.

Measuring cell and pack conditions

Measurement is the foundation of any battery management system, since every control or protection decision depends on accurate data. BMS hardware includes voltage-sense circuits for each cell or group of cells, current sensors at the module or rack level, and temperature sensors distributed across the pack. These devices feed an embedded controller that filters noise, corrects offsets, and checks values against plausibility rules. Good measurement design considers not only accuracy but also sampling rate, channel synchronization, and resilience to electrical interference from the power conversion system.

Scaling up to large energy storage systems introduces new measurement challenges. Long harnesses and high currents can introduce common mode noise, offset errors, and crosstalk that distort readings. Calibration strategies must consider component tolerances, ageing, and the practical limits of production testing on large numbers of racks. Engineers also need strategies for dealing with sensor failures gracefully, such as flagging degraded channels and switching to redundant measurements without creating unnecessary trips.

Enforcing safety limits and protections

A battery management system uses its measurements to enforce safety limits on voltage, current, temperature, and insulation resistance. Firmware compares readings against threshold tables that include normal operating bands, warning levels, and hard-shutdown limits. When values exceed these limits, the BMS can command the power conversion system to ramp down, open contactors to isolate parts of the pack, or trigger alarms for operators. Good designs avoid oscillation and nuisance trips by combining hysteresis, timers, and contextual logic.

Safety functions in large systems also include coordination across racks and strings. For example, a single rack with a hot spot should not silently degrade while neighbouring racks run at full output, since that pattern accelerates ageing and increases risk. Multi level BMS architectures evaluate aggregate risk and may derate the full installation to protect weaker units. Defining and testing these strategies calls for a clear fault tree, detailed requirement sets, and rigorous verification campaigns that cover both normal and rare events.

Estimating state of charge and state of health

State of charge estimation gives operators and higher level controllers a clear picture of how much energy remains in the storage system. The BMS typically combines coulomb counting with voltage based correction, temperature compensation, and occasionally more advanced observer algorithms. State of health adds another layer by estimating remaining capacity and internal resistance based on historical data, duty cycle, and model parameters. These estimates support warranty monitoring, maintenance planning, and decisions about how hard to push the system.

Good estimation design must account for sensor errors, model mismatch, and changing cell characteristics over time. Algorithms that work well in a laboratory over a few weeks may drift in longer projects if they are not robust to ageing. Utility scale deployments also experience a wider variety of ambient conditions, load profiles, and downtime periods, which stress estimation strategies further. That variety makes hardware-in-the-loop (HIL) and other real-time testing techniques useful, since they allow teams to exercise algorithms against many simulated years of data in compressed time.

Coordinating with system-level controllers

Battery management systems do not operate in isolation, since they must coordinate with power conversion systems, plant controllers, and remote monitoring platforms. Interfaces such as CAN, Modbus, and Ethernet carry measurements, status flags, and control commands between these devices. Clear data models and communication protocols avoid misunderstandings, for example by defining how faults propagate, how limits are negotiated, and how control authority changes during abnormal conditions. Integration testing often focuses on these boundaries, since misaligned assumptions can cause trips or slow response even when each device meets its own requirements.

Communication design in large storage plants also touches on cybersecurity, time synchronization, and redundancy. Operators may require secure channels, authenticated firmware updates, and precise separation between safety critical networks and corporate systems. Time aligned data across devices improves root cause analysis after faults, as well as model calibration during early operation. Early investment in simulation and test benches that include communication paths makes integration smoother and reduces surprises during commissioning.

A clear understanding of how the battery management system shapes measurements, protections, estimations, and communication enables better risk control in large energy storage systems. Each function has unique failure modes, boundary conditions, and stress cases that must be considered during design. Close collaboration between pack designers, control engineers, and system integrators helps align BMS capabilities with project objectives. Strong real-time testing practice then closes the loop by verifying that the implemented behaviour matches those shared expectations across many conditions.

Why real-time testing matters for BMS development and energy storage systems

“Real-time testing of BMS functions gives you broader coverage, stronger reliability data, and shorter development cycles without sacrificing safety.”

Development teams have long relied on offline simulation and physical prototypes for validation, but that mix struggles to keep up with modern storage projects. Offline simulation can cover many cases, yet it does not always reveal timing issues, quantization effects, and integration faults that appear only when a controller runs on its target hardware. Purely physical tests on packs and racks are expensive, slow, and harder to repeat, especially when tests involve rare or dangerous faults. Real-time testing fills this gap by combining high fidelity models with hardware controllers under closed loop conditions that match actual behaviour more closely. 

Hardware-in-the-loop testing connects a BMS controller to a simulator that emulates cells, packs, and the surrounding system with realistic dynamics. The simulator responds to controller outputs such as contactor commands and current requests, then feeds back voltages, currents, and temperatures in real time. This setup allows you to observe how control algorithms, protection logic, and communication stacks behave during complex transients such as short circuits, grid faults, or sudden power setpoint changes. You can also pause, repeat, and instrument these scenarios much more easily than in a field test or full scale lab experiment.

Energy storage projects face tight schedules and safety expectations, so catching issues earlier offers direct benefits. Real-time testing lets you validate BMS functions while pack designs and control strategies are still moving, instead of waiting for final hardware. Teams can run overnight campaigns that sweep across temperatures, cell tolerances, and duty cycles that would be impractical on physical battery stacks. This approach shortens feedback loops, improves confidence at each design gate, and frees scarce lab assets for the most critical physical tests.

Key methods of battery simulation and real-time modelling in BMS testing

Engineers have many approaches to battery simulation for BMS testing, and each choice involves trade-offs in fidelity and complexity. Selecting the right model structure helps you match the simulation burden to available computing resources while still capturing the behaviours that matter most. Your team may use simple equivalent circuits during early development, then move to higher fidelity electrothermal models as testing needs grow. A clear modelling strategy keeps both control design and real-time testing aligned with project goals.

• Physics-based electrochemical models in reduced order form: These models approximate diffusion and reaction processes inside cells using simplified equations or lookup tables. They can capture effects such as rate dependent capacity and hysteresis more accurately than basic circuits, which makes them useful for advanced state estimation research and calibration.
• Equivalent circuit models for pack behaviour: Simple resistor-capacitor networks provide a practical representation of open circuit voltage, internal resistance, and dynamic response over relevant time scales. They are easier to identify from test data and often run comfortably in real-time simulators, which makes them a common choice for BMS testing.
• Electrothermal-coupled models: Temperature affects cell voltage, ageing, and safety margins, so many teams pair electrical models with thermal networks that represent modules and racks. These models let you study how current profiles and cooling strategies interact, then adjust BMS limits and derating strategies for high or low temperatures.
• Cell-level emulation using battery cell simulators: Dedicated cell emulator hardware can provide programmable voltage, current, and fault behaviours at each cell tap. This approach is especially useful for validating measurement chains, passive or active balancing circuits, and isolation monitoring without attaching live cells.
• Hardware-in-the-loop simulation for full-pack testing: Digital real-time simulators can model thousands of cells, multiple racks, and power-conversion devices while running the actual BMS controller firmware in closed loop. This method supports automated regression testing, communication checks, and fault campaigns that cover a wide range of operating conditions.
• Fault and degradation modelling: Battery simulation for BMS testing benefits from models that include ageing effects, sensor failures, and wiring faults. Injecting these conditions lets you verify detection logic, fail safe modes, and diagnostic coverage before any destructive tests on physical packs.

Battery simulation choices influence not only what you can test, but also how quickly you can iterate during development. Simpler models grant faster execution and easier parameter identification, while more detailed models reveal subtle effects at the cost of complexity. Many teams maintain several model tiers and move controllers between them as questions change through the project timeline. That flexibility, supported by real-time testing, creates a more efficient and transparent validation flow.

How real-time testing improves BMS testing coverage, reliability and cycle time

Energy storage projects involve a long list of scenarios that concern both engineers and project owners, from routine cycling to fault cases that must never occur in service. Real-time testing gives you a practical way to exercise those scenarios under controlled conditions while keeping the BMS on its target hardware. Coverage improves because you can sweep across temperatures, states of charge, and cell variations without waiting for lengthy physical tests. Reliability and development cycle time improve at the same time, since you catch issues earlier and spend less time debugging in the field.

Expanding BMS testing coverage with virtual scenarios

Coverage starts with the number and variety of conditions you can test within your schedule and budget. Real-time simulation lets you vary grid events, ambient conditions, and load profiles programmatically, which multiplies the scenarios you can examine. Engineers can script sequences such as daily cycling, rare contingency events, or multi day stress periods, then run them overnight or over a weekend. That approach offers a much richer view of BMS behaviour compared with a handful of manual tests on a pack or rack.

Virtual scenarios also help you combine conditions that would be hard to reproduce safely on physical hardware. Examples include simultaneous low temperature, ageing, and sensor offset cases that stress estimation and protections. You can fine tune the severity of each factor, evaluate BMS responses, and refine limits or algorithms accordingly. Over time, these simulated campaigns build a stronger link between requirement sets, test suites, and observed behaviour.

Improving reliability through repeatable, high-fidelity tests

Reliability grows when tests are both faithful to physics and repeatable across labs and engineers. Real-time BMS testing uses validated models, synchronized inputs, and stable timing, so the same test case produces consistent results each time. That repeatability makes it easier to compare firmware versions, confirm bug fixes, and share findings between teams or suppliers. It also provides a stronger basis for correlation with physical test data, since model behaviour can be documented and updated as new measurements arrive.

High fidelity models that run in real-time add another layer of confidence. When models accurately capture key electrothermal behaviours and controller interfaces, small firmware changes are clearly reflected in the results. Engineers spend less effort chasing artefacts from numerical approximations or poorly configured test setups. The focus remains on BMS performance and safety, where engineering effort delivers the most excellent value for grid storage projects.

Shortening cycle time from model to validated controller

Development cycles shrink when you can close the loop between design, implementation, and feedback more quickly. Real-time testing lets control engineers try new algorithms, deploy them to prototype controllers, and evaluate them against rich scenario sets in a short period. Issues in timing, scheduling, and communication show up early, while requirements are still flexible. That pattern reduces the number of trips from desk to lab and back, helping teams converge on stable designs sooner.

Once a baseline design works well, the same real-time testing framework supports regression testing during every firmware update. Automated suites can run before each release, compare key metrics with reference runs, and flag unexpected behaviour. Engineers avoid manual, ad hoc testing cycles that would otherwise slow progress or miss corner cases. The result is a steadier path from first prototype to certified production controller.

Supporting safety standards and regulatory expectations

Grid scale energy storage systems must align with safety standards, grid codes, and local permitting expectations. Real-time BMS testing contributes to that goal by providing traceable evidence that protection functions, alarms, and fault responses behave as specified. Teams can map requirements from standards or customer specifications to explicit test cases, then store logs and reports for audits. The same infrastructure also simplifies re testing when requirements change or new firmware versions appear.

Regulators and independent reviewers often feel more comfortable when they see structured validation backed by both simulation and physical tests. Real-time testing supports that narrative with data across a broad range of scenarios, including some that cannot be tested on live hardware for safety reasons. That combination reduces uncertainty during permitting and stakeholder reviews. It also builds trust between utilities, project owners, and suppliers who all share responsibility for safe operation.

Real-time testing of BMS functions gives you broader coverage, stronger reliability data, and shorter development cycles without sacrificing safety. The approach complements offline analysis and physical pack testing rather than replacing them, which keeps the validation strategy balanced. Early investment in models, test automation, and hardware benches pays back through fewer redesigns and smoother commissioning. Over time, these practices become a regular part of how teams design, validate, and maintain storage assets.

Common pitfalls in BMS testing for energy storage systems and how to avoid them

Engineering teams rarely set out to cut corners on validation, yet common patterns appear in many storage projects. Some of these patterns come from schedule pressure, while others arise from unclear ownership between pack suppliers, system integrators, and plant operators. Understanding these pitfalls helps you ask sharper questions and set expectations early in a project. Clear awareness also supports better planning for real-time testing, lab infrastructure, and model development.

• Relying only on offline simulation: Offline models are useful for early design work, but they miss timing and integration issues that appear once firmware runs on the target controller. Introducing real-time testing and BMS hardware-in-the-loop setups during development closes this gap and reveals problems before commissioning.
• Underestimating measurement and calibration issues: Many test plans assume ideal sensors, yet production units may have offsets, noise, and ageing effects. Including sensor models and calibration variations in your battery simulation exposes BMS sensitivity to these imperfections and encourages better strategies for detection and compensation.
• Ignoring temperature and thermal gradients: Validation that focuses only on room temperature operation misses behaviours at low or high ambient conditions and within modules that see uneven cooling. Electrothermal models and climatic test campaigns, combined with real-time testing, help refine derating, limits, and protection logic across the full operating range.
• Limited fault and abuse testing: Some projects perform only a small set of fault tests because they are expensive or risky on physical packs. Simulated fault injection in real-time, plus targeted destructive tests for correlation, gives better insight into how the BMS responds to shorts, contactor failures, or internal pack issues.
• Inadequate coverage of communication failures: Communication faults between the BMS, power conversion system, and higher level controllers can lead to confusing plant behaviour or unsafe states. Test plans should include message loss, delays, and protocol errors in both simulation and hardware benches so that fail safe strategies and alarms are thoroughly verified.
• Treating HIL as a one-time sign-off activity: Some teams view BMS hardware-in-the-loop testing as a final step before release instead of a continuous tool across the project. Using HIL rigs through concept studies, development, integration, and maintenance yields better insight and spreads the cost over many activities.

Avoiding these pitfalls requires a mix of planning, ownership, and practical tools that match team capacity. Clear validation strategies treat BMS testing as a shared responsibility across suppliers and project partners, with explicit expectations at each stage. Real-time testing and good battery simulation practice make it easier to apply that strategy consistently across projects. As experience grows, it becomes simpler to spot early signs of risk and adjust plans before they affect schedules or budgets.

Integrating real-time simulation into your energy storage BMS validation process

Introducing real-time simulation into an existing validation flow can feel like a large change, especially for teams already juggling hardware, software, and site work. A structured approach makes that change manageable and helps stakeholders see clear benefits at each step. Success starts with aligning on objectives, then builds through careful model preparation, test bench design, and automation. When handled this way, real-time testing becomes a natural extension of existing BMS testing practice rather than a separate activity.

Defining objectives and success metrics with project stakeholders

Clear objectives give real-time testing efforts direction and help justify the required investment in tools and skills. Stakeholders from pack design, controls, safety, and operations can identify the scenarios that concern them most, such as thermal runaway risk, communication faults, or grid events. These concerns translate into specific test objectives, for example improving fault coverage, reducing integration issues, or shortening firmware release cycles. Metrics such as the number of test cases automated, defect discovery rates, or cycle time per firmware iteration then provide feedback during implementation.

Early agreement on scope also prevents BMS testing from becoming a catch all for broader project issues. When objectives are explicit, teams know which questions the real-time bench must answer and which belong to other activities such as protection studies or site acceptance tests. This clarity guides decisions on model fidelity, hardware selection, and staffing. It also improves communication with management, since progress can be reported against concrete targets rather than general aspirations.

Building and refining battery models for real-time execution

Battery models for real-time testing must balance fidelity with computation limits and integration complexity. Engineers often start from offline models used in design work, then reduce complexity through model order reduction, lookup tables, or simplified thermal representations. Parameter identification from lab data remains important, since inaccurate parameter values diminish the value of any model structure. Once a candidate model runs in real time, correlation steps against pack measurements validate that behaviour is accurate enough for intended tests.

Model maturity improves as projects progress and more data become available from prototype packs, cycling tests, and early field operation. Updating models with this information keeps BMS testing aligned with actual hardware and usage patterns, which maintains credibility with stakeholders. Documented workflows for versioning and validating models reduce confusion and avoid regressions in fidelity. Over time, the organisation builds a library of models that can be reused or adapted for new chemistries, pack designs, and duty cycles.

Designing BMS hardware-in-the-loop architectures

A BMS hardware-in-the-loop architecture connects real controllers to simulation models in a way that preserves timing, signal integrity, and safety. Typical setups include a digital real-time simulator, a cell or pack interface that emulates voltages and currents, and communication links that mirror those used in the final installation. Careful attention to signal conditioning, isolation, and failure modes keeps both the simulator and BMS hardware protected during aggressive tests. Engineers also need clear procedures for starting, stopping, and reconfiguring test setups as projects evolve.

Architecture decisions should reflect both current project needs and likely future test requirements. For example, a bench designed for one rack voltage and communication standard may later need to support different string configurations or additional protocols. Modular designs, spare channels, and flexible software interfaces reduce the effort of adapting benches to new projects. That flexibility increases the long term value of real-time testing infrastructure and helps justify its cost across many programmes.

Automating test sequences and data handling

Manual execution of real-time tests quickly becomes a bottleneck as the number of scenarios increases. Automation frameworks allow engineers to describe test sequences in scripts or configuration files, then execute them without constant supervision. These sequences can cover firmware download, model configuration, test execution, and result logging. Automated checks against pass-or-fail criteria further reduce manual analysis effort and make regression testing practical after every firmware change.

Data handling is equally important, since real-time benches generate large volumes of measurements, status flags, and event logs. Structured storage, naming conventions, and dashboards make it easier to retrieve results, compare runs, and support audits. Shared access for design, validation, and operations teams builds a common understanding of BMS behaviour. As usage grows, this structured dataset becomes a valuable resource for improving models, refining tests, and guiding future projects.

A well integrated real-time simulation setup for BMS validation does not appear overnight, but grows through focused steps aligned with project objectives. Early work on model and architecture design lays the foundation for later automation and reuse. Cooperation between control engineers, test specialists, and managers keeps the effort grounded in practical needs and measurable outcomes. The result is a validation process that scales with project complexity while supporting safe and efficient storage deployments.

How OPAL-RT can help you accelerate simulation and real-time testing for battery energy storage systems

OPAL-RT focuses on real-time digital simulation platforms that support high fidelity battery models, power electronics, and grid interactions in one integrated setup. Engineers working on battery management system projects can connect controllers directly to OPAL-RT simulators and run hardware-in-the-loop campaigns that mimic cell, module, and pack behaviour with consistent timing. The same tools support battery simulation across a range of model types, from equivalent circuits to more advanced electrothermal approaches, helping you align fidelity with your project questions. These capabilities shorten the time between model development, firmware changes, and validation runs, which keeps teams moving without waiting for scarce lab assets.

For BMS testing of large energy storage systems, OPAL-RT provides platforms that integrate with cell-emulation hardware, grid models, and automation frameworks used in many power system labs. Test engineers can build repeatable suites that cover everyday cycling, communication faults, and aggressive fault cases, then reuse them across projects with parameter changes instead of rewrites. Open interfaces support integration with common toolchains for modelling, scripting, and data analysis, making it easier to slot real-time testing into existing workflows. These practical strengths, proven across many industries, allow teams to treat OPAL-RT equipment as a core part of their validation strategy rather than a one off experiment. That long term fit is why many engineering groups regard OPAL-RT as a trusted partner for BMS and storage system verification.

Common questions

Engineers who work with battery energy storage systems often have similar questions about control, safety, and validation. The mix of electrochemistry, power electronics, and embedded software creates many possible paths through a project, and not all of them are obvious at first. Clear answers help teams decide where to invest effort in modelling, test infrastructure, and skills. Good guidance also supports conversations with managers and stakeholders who do not live inside the technical details every day.

What is a battery energy storage system in simple terms?

A battery energy storage system is a collection of rechargeable cells, power electronics, and control equipment that stores electrical energy for later use. The system absorbs power when generation exceeds local needs or when prices are low, then sends power back to the grid or local loads when helpful. Inside the system, the battery management system, power conversion system, and energy management system coordinate charging, discharging, and safety. You can think of it as a flexible energy buffer that helps keep supply and load aligned without relying only on traditional generation sources.

How does real-time testing improve BMS development?

Real-time testing improves BMS development by placing the controller in a realistic feedback loop long before full hardware is available. The controller runs on its target processor, controls simulated cells and packs, and experiences the same timing and data rates it will see in service. That setup reveals issues in scheduling, protection logic, and communication behaviour that offline models might hide. When teams fix those issues early, they spend less time debugging during integration, commissioning, and field operation.

What kinds of battery simulation models are most useful for BMS testing?

For many BMS testing tasks, equivalent circuit models with temperature dependence provide a good balance between fidelity and execution speed. These models capture open circuit voltage, internal resistance, and dynamic response well enough for many control and protection checks. When projects need more detail, engineers may add electrothermal coupling, ageing effects, or simplified electrochemical behaviour. The most useful approach is usually a small family of models tuned for specific questions, all sharing consistent parameters and validation data.

How early should real-time BMS testing start in a project?

Real-time BMS testing brings value as soon as a basic control strategy and preliminary battery model exist. Starting early lets teams catch architectural issues, such as missing signals or insufficient computation headroom, while they can still adjust designs. Over time, the same test rigs can support more detailed models, advanced algorithms, and regression suites for production firmware. Treating real-time testing as an ongoing activity rather than a late stage hurdle spreads cost and effort sensibly across the project timeline.

What skills and tools does a team need to adopt real-time BMS testing?

Teams that adopt real-time BMS testing benefit from a mix of control engineering, modelling, and test automation skills. People who understand battery behaviour, embedded software, and power electronics can define test objectives and interpret results. Knowledge of real-time simulation platforms, scripting languages, and data-handling tools supports practical implementation. With that combination in place, the group can design benches, maintain models, and keep validation campaigns aligned with project needs.