Research And Implementation of Data Augmentation Method for Lithium Battery Based on Reinforcement Learning

Abstract

With the rapid development of intelligent power systems and electric vehicle technology, high-performance lithium batteries have become a research hotspot. Monitoring the health status and predicting the remaining life of lithium batteries are crucial for ensuring the safe operation of batteries and the reliability of maintenance systems. However, the high cost of collecting lithium battery data and the relatively limited amount of data pose challenges to data-driven battery management systems (BMS). To address this issue, this study proposes a reinforcement learning-based data augmentation framework for lithium batteries, using Generative Adversarial Networks (GAN) to enhance the quality of lithium battery data. This paper employs Dynamic Time Warping (DTW) as the core algorithm to evaluate the similarity of synthetic data to real data in terms of time series, ensuring that the temporal characteristics of the augmented data are consistent with the original battery data. In this way, we guide the GAN generator to produce highly similar and diverse data, thereby expanding the training set and improving the accuracy of the battery status prediction model.

This study first comprehensively analyzes the characteristics of data in the field of lithium battery applications, clarifying the importance of data augmentation technology in improving model performance. We designed and implemented a GAN framework based on reinforcement learning, where the discriminator not only evaluates the authenticity of the data but also supervises the temporal characteristics of the generator's output data. In the reinforcement learning environment, the generator continuously optimizes its strategy to pass the DTW evaluation metric, generating more accurate battery usage data. Experimental results show that compared to unenhanced data, the data generated by this framework exhibits higher accuracy and generalization ability in the battery performance prediction model.

Furthermore, this study also explores the potential applications of the proposed framework in other energy storage systems and similar sequential data processing tasks, demonstrating its broad applicability and flexibility. This research not only provides new research ideas for lithium battery data augmentation technology but also offers valuable technical support for the development of data-driven battery management systems.