[1]彭自然,王顺豪,肖伸平.基于SDAE-DCPInformer的电动汽车电池SOC和SOH估算方法[J].智能系统学报,2025,20(4):969-983.[doi:10.11992/tis.202408010]
PENG Ziran,WANG Shunhao,XIAO Shenping.SOC and SOH estimation method of electric vehicle battery based on SDAE-DCPInformer[J].CAAI Transactions on Intelligent Systems,2025,20(4):969-983.[doi:10.11992/tis.202408010]
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PENG Ziran,WANG Shunhao,XIAO Shenping.SOC and SOH estimation method of electric vehicle battery based on SDAE-DCPInformer[J].CAAI Transactions on Intelligent Systems,2025,20(4):969-983.[doi:10.11992/tis.202408010]
基于SDAE-DCPInformer的电动汽车电池SOC和SOH估算方法
《智能系统学报》[ISSN 1673-4785/CN 23-1538/TP] 卷:
20
期数:
2025年第4期
页码:
969-983
栏目:
学术论文—机器学习
出版日期:
2025-08-05
- Title:
- SOC and SOH estimation method of electric vehicle battery based on SDAE-DCPInformer
- Keywords:
- electric vehicle; power battery; state of health; state of charge; stacked denoising autoencoder; data cleaning; dynamical channel pruning; enhanced informer
- 分类号:
- TP274; U469.72; TM912
- 文献标志码:
- 2025-4-23
- 摘要:
- 针对现有电动汽车电池状态估计方法存在运算效率低和估算准确率低的问题，提出一种模型以估算电动汽车电池荷电状态 (state of charge, SOC) 和健康状态 (state of health, SOH)。采用堆叠降噪自编码器 (stacked denosing auto encoder，SDAE) 清洗电压、电流和温度数据中的异常数据和空缺数据，减小对估算精度的影响。引入动态通道剪枝 (dynamical channel pruning，DCP) 技术对Informer模型进行稀疏化处理，提高剪枝后模型的性能和稳定性。将清洗过的数据输入DCPInformer模型实现SOC和SOH的精确估计。实验结果表明，所提出的SDAE-DCPInformer模型估计SOC的平均绝对误差和均方根误差分别达到0.25%和0.38%，估计SOH的平均绝对误差和均方根误差分别达到了0.51%和0.64%。与传统Transformer等模型相比，所提模型预测SOC和SOH的速度更快，估算准确度有效提升，拥有的更好稳定性和泛化性。
- Abstract:
- To address the problems of low computing efficiency and estimation accuracy of existing electric vehicle power battery state of charge (SOC) and state of health (SOH) estimation methods, a model is proposed to estimate SOC and SOH. First, a stacked denoising automatic encoder (SDAE) is used to clean the anomalies and vacancies in the voltage, current, and temperature data to reduce the effect on the estimation accuracy. Second, the dynamic channel pruning (DCP) technique is introduced to address the sparsity of the Informer model to improve the performance and stability of the model. Finally, the cleaned data are input into the DCPInformer network model to estimate SOC and SOH. Experiments reveal that the mean absolute error (MAE) and root mean square error (RMSE) of the proposed SDAE-DCPInformer model reach 0.25% and 0.38% for estimating SOC, respectively, and the MAE and RMSE for estimating SOH reach 0.51% and 0.64%, respectively. Compared with traditional models such as the Transformer, the proposed model predicts faster, and the estimation accuracy is improved with better stability and generalization.
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备注/Memo
收稿日期:2024-8-20。
基金项目:国家重点研发计划基金项目（2019YFE0122600）；湖南省教育厅重点科研项目（22A0423）；湖南省自然科学基金项目（2023JJ60267, 2022JJ50073）.
作者简介:彭自然，副教授，主要研究方向为人工智能、信号处理和智能检测仪表。主持湖南省自然基金项目、湖南省教育厅科学研究项目10余项，获发明专利授权2项，出版学术专著2部，发表学术论文20余篇。E-mail：pengziran@hut.edu.cn。;王顺豪，硕士研究生，主要研究方向为电动汽车动力电池健康状态和荷电状态估算。E-mail：3130669501@qq.com。;肖伸平，教授，主要研究方向智能控制、时延系统鲁棒控制理论和时延系统稳定性分析。主持国家自然科学基金项目、国家火炬计划和国家重点研发计划项目子课题10余项，获发明专利授权5项、实用新型专利授权5项。发表学术论文80余篇，ESI 1%高 被引论文5篇。E-mail：xsp@hut.edu.cn。
通讯作者:彭自然. E-mail：pengziran@hut.edu.cn
更新日期/Last Update:
1900-01-01