机电工程技术2025，Vol.54Issue(5)：148-151,4.DOI:10.3969/j.issn.1009-9492.2025.05.027
基于正则化随机扰动优化LSTM的动力电池SOC估计研究
Research on Power Battery SOC Estimation Based on Regularized Stochastic Perturbation Method Optimized LSTM
摘要
Abstract
The evolving global automotive market imposes new requirements and challenges for the industrial upgrading of new energy vehicles.Precise SOC monitoring holds significant importance for optimizing BMS performance.Targeting at the limitations of existing SOC estimation methods in simultaneously meeting real-time monitoring and precision demands,a regularized stochastic perturbation method is proposed to optimize LSTM models.The methodology introduces Gaussian noise through stochastic perturbations as dynamic disturbances,establishes spatial boundaries via regularization-based parameter configuration,and integrates multidimensional parameter space construction with an adaptive noise attenuation mechanism.This synergistic approach enables collaborative optimization of gated units,allowing the enhanced LSTM model to conduct targeted parameter space exploration within regularization-constrained safety boundaries.Experimental results demonstrate that the proposed model maintains a stable MAE metric of approximately 1.1%under complex dynamic operating conditions.Compared with conventional SOC estimation methods,the model exhibits significantly reduced error margins and enhanced robustness against interference factors such as current transients,providing a novel pathway for advancing BMS performance optimization.关键词
SOC/LSTM网络/随机扰动Key words
SOC/LSTM networks/regularized stochastic perturbation分类
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邢云祥,王玥琦,易吉良,刘路..基于正则化随机扰动优化LSTM的动力电池SOC估计研究[J].机电工程技术,2025,54(5):148-151,4.基金项目
广西自然科学基金项目(2024JJA160324) （2024JJA160324）
桂林航天工业学院2023年度本科教改项目(2023JA02) （2023JA02）
