DeepBMS · Deep Reinforcement Learning-Based Battery Management System for Electric Vehicles

Deep Reinforcement Learning-Based Battery Management System for Electric Vehicles

The project addresses key deficiencies in electric vehicle (EV) battery systems, particularly the challenges that arise as batteries age. Aging introduces uncertainties in battery behavior, which in turn increases the uncertainty of driving range, contributes to range anxiety, and can accelerate further battery degradation. To address these issues, this project focuses on next-generation Battery Management Systems (BMSs), with a particular emphasis on their software components. Specifically, it targets State-of-X estimation algorithms, where X can represent either the State of Charge (SoC) or the State of Health (SoH). SoC indicates the remaining charge of the battery, while SoH reflects the battery’s health and provides insight into its remaining useful life. Both metrics are critical to the safety and performance of EVs. The proposed system, DeepBMS, adopts a hybrid approach that combines model-based and data-driven methods using reinforcement learning. This aims to develop algorithms that are both accurate and data-efficient. Traditional model-based approaches often suffer from low fidelity due to dynamically changing battery operating conditions—such as temperature fluctuations, driving patterns, and aging effects. On the other hand, purely data-driven approaches are typically data-intensive, requiring extensive lab testing, and often lack interpretability. DeepBMS uses reinforcement learning to augment physical battery models with a streamlined data-driven component that compensates for model uncertainties. This enables accurate SoC and SoH estimation with significantly reduced data requirements. Moreover, the system is designed to be adaptable, capable of learning from new battery conditions and incorporating this knowledge into the BMS software to maintain estimation accuracy, even as the battery ages. The work carried out in DeepBMS to develop these state estimators include the whole design cycle from battery cell testing and dataset generation, designing and training the AI agents, optimizing the AI, to deployment of the trained agents in embedded systems.

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