AI-Driven Adaptive Control for Battery Management Systems in Electric Vehicles
DOI:
https://doi.org/10.63282/3050-9262.IJAIDSML-V6I2P106Keywords:
Battery Management System (BMS), Electric Vehicles (EV), Artificial Intelligence (AI), Adaptive Control, Machine Learning (ML), Deep Learning (DL), State of Charge (SoC), State of Health (SoH), Reinforcement Learning (RL), Edge-AIAbstract
With increasing global demand for green transportation, the urge to innovate and implement electric vehicles (EVs) has also picked up steam. Behind the performance, safety, and reliability of EV stands the Battery Management System (BMS) that optimizes battery performance through monitoring, management, and protection of the battery pack. Traditional BMS designs have rule-based or model-based approaches with static parameters, which are not sufficiently adaptive to deal with real usage patterns and battery aging effects. The presented paper proposes a scheme of adaptive control based on AI for BMS utilizing the ML and DL algorithms to enhance system robustness, extend the life of the battery, and optimize charging/discharge operations. The proposed system utilizes neural networks and reinforcement algorithms to estimate dynamic state of charge (SoC), state of health (SoH), and temperature control against varying loads and ambient conditions. We compare the operation of our framework with traditional BMS controllers through HIL tests and simulation. Our findings indicate that AI-based BMS delivers more accurate predictions, fault tolerance, better energy efficiency and battery life improvements. This paper presents a critical survey of common AI use cases employed in BMS, proposes a novel hybrid AI architecture for adaptive real-time control, and proposes deployment strategies that can be deployed on the latest EV platforms. Future work opportunities involve integrating edge-AI for real-time inferencing and federated learning for privacy-respecting data analysis in distributed EV fleets
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