Wie entwirft und testet man ein robustes Batteriemanagementsystem auf Sicherheit und Zuverlässigkeit?

Choose between centralized (single master unit) or distributed (multiple slave units) architecture. Consider cost, complexity, scalability, modularity, reliability, and fault tolerance. Centralized: simpler wiring, lower component count, but single point of failure. Distributed: more complex communication, but higher reliability and easier maintenance. Modular designs enable mix-and-match of components. Redundant controllers and communication paths enhance fault tolerance.

The Total Cell count and Pack Assembly Process mandates if you can use one centralized BMS Unit. usually you deal with decentralized BMS. Main Unit as Gateway between the System and Cell Stack as well as Pack Voltage Monitoring and Pack Current Monitoring. This Unit will also take care for the Pack Configuration like total Stack Voltage, max Current and max Power. The distributed Sub Units will cover Cell Modules or Groups of Cell Modules. This enables more flexibility with Pack and Module design as well as Capacity and Power Options.

At a high level, emerging approaches to distributed BMS designs and integrations may transform our relationship to energy, via dynamic network creation, AI-informed battery optimizations, and a broad array of new of new energy products, services and experiences.

Select sensors, circuits, switches, fuses, balancing, microcontrollers, memory, interfaces, and power supplies. Ensure specs like accuracy, resolution, speed, noise, isolation, protection, and durability are met. Proper interfacing is crucial. Key components: voltage, current, temperature sensors; analog front-end; balancing circuits; main controller; isolated communication; power supplies. Choose automotive-grade parts for reliability. Consider integration level (discrete, AFE, ASIC) based on cost and customization needs.

BMS software must accurately estimate SoC (State of Charge) and SoH (State of Health) for effective charge-discharge control, diagnostics, and, in electric vehicles, range estimation. Various algorithms, including Linear and Extended Kalman Filters and Neural Networks, differ in computation and accuracy. Some BMS systems support remote monitoring and algorithm tuning based on user behavior and usage data. Developing precise, robust BMS software requires knowledge of battery chemistry, thermal characteristics, and voltage curves. ICs such as the BQ769 family from TI allow for accurate and reliable data acquisition and their reference code is a good place to start on understanding BMS software design.

Develop modular software for monitoring and control. Implement algorithms for state estimation (SoC, SoH), charge control, balancing, protection, diagnostics. Handle data acquisition, processing, storage, transmission, and user interface. Test for functionality and robustness. Use Kalman filters, neural networks for SoC/SoH. Start with reference code from chip vendors. Ensure real-time performance, fault detection, and recovery. Implement battery models and adaptive algorithms. Support firmware updates and parameterization for flexibility.

The key to building software to ensure safety and reliability of BMS is to ensure functional safety guidelines(ISO 26262) are being obeyed. Once this has been taken care of, developing sophisticated estimators be it physics or machine learning based take precedence.

During the testing phase, rigorous validation is essential. This includes software-in-the-loop (SIL) and hardware-in-the-loop (HIL) testing to simulate various operating conditions and potential faults. SIL testing involves testing the BMS software in a simulated environment. The software is executed on a computer, where virtual models of the battery and other system components mimic real-world conditions (without the need for physical hardware). HIL testing involves connecting the BMS software to real hardware components. The software interacts with physical hardware through a test bench that simulates battery behavior and other system conditions. HIL testing provides a more realistic validation environment.

Test hardware, software, and system rigorously under normal and fault conditions. Perform bench, lab, field, and accelerated tests. Validate performance, efficiency, durability, stability, and compatibility. Identify and resolve issues. Use software- and hardware-in-loop testing for realistic simulation. Develop comprehensive test plans covering cell, module, pack, and vehicle levels. Include environmental, mechanical, and EMC tests. Perform failure mode and effects analysis (FMEA). Aim for high test coverage and automate regression tests. Comply with safety and performance standards.

The testing phase is very crucial for safety and reliability of high voltage device like a battery. Various testing phases involving MiL, HiL, integration testing with other microprocessors is needed to have full validation on software. Having a systems point of view is crucial here. Even a delay in system (due to interaction with other components or comms) can result in catastrophic failures.

Analyze test data and tune BMS parameters to optimize capacity, lifespan, safety, cost, weight, and complexity. Use modeling, simulation, experimentation, and machine learning. Develop high-fidelity models of cells, modules, and packs. Perform sensitivity analysis and design of experiments. Use multi-objective optimization for trade-offs. Consider cell balancing strategy, charge/discharge profiles, thermal limits, and degradation. Optimize at component and system levels. Validate optimized design through rigorous testing. Continuously improve based on field data and user feedback.