oing into 2026 the UK government is pushing more drivers tomake the switch to electric vehicles, with 45% of new car
sales in the UK expected to be EVs by 2030. Despite this, safety is a critical concern in EVs. The high energy density of lithium-ion
batteries poses risks of failure if the operating conditions deviate from those for which the battery has been designed. A Battery Management System (BMS) is therefore critical in preventing negative outcomes, including thermal runaway, an uncontrollable exothermal reaction leading to the destruction of the battery. The primary functions of a BMS include
monitoring current, voltage and temperature, preventing overcharge and over-discharge, balancing the energy across the cells, estimating the battery’s state of charge (SOC) and state of health (SOH), and controlling the temperature of the battery pack. These functions are critical, as they impact the performance, safety, battery lifetime, and user experience of the electric vehicle. By preventing overcharge and discharge beyond voltage limits, the BMS prevents premature aging of the battery, ensuring that the vehicle remains performant over its operational life.
Engineers simulate the battery plant model, environment and BMS algorithms on a desktop computer using behavioural models. They use desktop simulation to explore new design ideas and test multiple systemarchitectures before committing to a hardware prototype. Desktop simulation enables engineers to verify functional aspects of the BMS design. Engineers can, for example, explore different balancing configurations to evaluate suitability and trade-offs between them. Simulation is also instrumental in requirement testing – engineers
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can, for instance, verify correct contactor behaviour in the presence of an isolation fault. Evaluating the system’s behaviour during a fault is another clear example of the use of simulation to replace hardware testing. Once the design is validated using desktop simulation, engineers can automatically generate C or HDL code for rapid prototyping (RP) or hardware-in-the-loop (HIL) testing to further validate the BMS algorithms running as code in real-time. With RP, code is generated from the BMS algorithms model and deployed to a real- time computer that performs the functions of the productionmicrocontroller. With automatic code generation, algorithm changes made in themodel can be tested on real-time hardware in hours rather than days. In the case of HIL testing, code is generated from the battery plant models rather than the BMS algorithm models, providing a virtual real-time environment that represents the battery pack, active and passive circuit elements, loads, charger, and other systemcomponents.
This virtual environment enables engineers to validate the functionality of the BMS controller in real time before developing a hardware prototype. Simulation enables engineers to dramatically
reduce the time fromdesign to code generation, allowing for rapid modelling of various techniques with enhanced speed and efficiency. Altigreen Propulsion Labs engineers used a simulation-based approach to model and iteratively test different techniques for SOC estimation, such as Kalman filtering and Coulomb counting, and designed a comprehensive one for their SOC estimation. Prathamesh Patki, principal engineer and control systems head at Altigreen, said: “Embedded Coder has cut development time in half. Whatever we conceptualise, we can get it running in the shortest amount of time on the real hardware.”
Cell characterisation is the process of fitting a battery model to experimental data. Accurate cell
This supports early-stage motor development by allowing users to move from high-level size
requirements to fully manufacturable motor designs in minutes. It also enables them to validate performance at defined operating points using analytical models developed by Alva Industries. Using TorqStudio, users begin by defining basic motor constraints such as outer diameter
and axial length, and the platform then automatically generates thousands of candidate motor designs and filters them to present only the most efficient solutions. Each design is evaluated based on key performance metrics, including motor constant and motor mass, allowing engineers to select optimal designs for their application. All presented designs represent complete motor concepts that can be manufactured. TorqStudio also provides detailed motor analysis capabilities. Users can define operating
conditions then evaluate how a motor performs at specific operating points.
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