船电技术2026，Vol.46Issue(5)：7-11,5.
基于物理信息神经网络的电池组SoC估计的研究
Research on Battery Pack SoC Estimation Based on Physics-Informed Neural Networks
张山 1黄振 1徐伟 2唐旭 1赵晨阳1
作者信息
- 1. 深海技术科学太湖实验室连云港中心,江苏 连云港 222000
- 2. 中国船舶科学研究中心上海分部,上海 200003
- 折叠
摘要
Abstract
State of Charge(SoC)estimation for battery packs is a critical issue in Battery Management Systems(BMS),especially in multi-cell battery packs where traditional SoC estimation methods exhibit significant errors due to differences among individual cells.To improve the accuracy of SoC estimation,this paper proposes a method based on Physics-Informed Neural Networks(PINNs)for estimating the SoC of battery packs.By integrating classical physical models of battery SoC with deep learning,this method utilizes information such as the voltage and temperature of each cell to estimate the battery pack's SoC.Experimental verification on a real vehicle datasets shows that the average absolute error of the proposed method on the test set is 0.89%,and the root mean square error is 1.12%,which are reduced by 72.0%and 53.3%respectively compared with traditional voltage-based methods and ablation models.The experimental results demonstrate that a strategy combining physical constraints with data-driven approaches can significantly enhance SoC estimation accuracy,providing a more precise and reliable SoC estimation method for BMS.关键词
物理信息神经网络/SOC估算/电池管理系统/电池组/深度学习Key words
Physics-Informed Neural Networks/SoC Estimation/Battery Management System/Battery Pack/Deep Learning分类
信息技术与安全科学引用本文复制引用
张山,黄振,徐伟,唐旭,赵晨阳..基于物理信息神经网络的电池组SoC估计的研究[J].船电技术,2026,46(5):7-11,5.
