State of Charge Estimation for Lithium Ion Battery Based on Reinforcement Learning

Abstract

This project aims to develop a state-of-the-art approach for accurately estimating the state of charge (SOC) of lithium-ion batteries by leveraging the combined power of rein forcement learning, Convolutional Neural Networks (CNNs), and Long Short-Term Memory (LSTM) networks. The dataset used for this study is the BMW dataset, which comprises real-world battery data collected from electric vehicles. The primary objective is to train a reinforcement learning agent to learn optimal policies for SOC estimation through iter ative trial and error interactions with the battery system. By continuously exploring and adapting its decision-making process, the agent can effectively estimate the SOC with high accuracy and adaptability. To further enhance the SOC estimation process, CNNs are in corporated into the proposed framework. CNNs excel at extracting spatial features from complex datasets, which is particularly useful in analyzing battery voltage data. By captur ing local patterns and variations in the battery response, the CNNs can effectively identify critical features that contribute to accurate SOC estimation. Additionally, LSTM networks are employed to model the temporal dependencies inherent in battery behavior. The LSTM networks can effectively capture the dynamic nature of battery performance by analyzing voltage and current data over time, enabling accurate SOC estimation even in varying oper ating conditions. Through comprehensive experiments and evaluations on the BMW dataset, the proposed approach demonstrates superior performance compared to traditional SOC es timation methods. The reinforcement learning agent, in combination with CNNs and LSTM networks, achieves high precision, adaptability, and robustness in estimating the SOC of lithium-ion batteries. The project’s outcomes have significant implications for battery man agement systems, energy optimization, and prolonging the lifespan of lithium-ion batteries in electric vehicle applications. By accurately monitoring and estimating the SOC, the pro posed approach contributes to more efficient and reliable battery usage, thereby improving overall performance and addressing the challenges associated with battery degradation and limited lifespan in electric vehicle technologies