基于CNN-LSTM-AM模型的储能锂离子电池荷电状态预测

doi:10.19595/j.cnki.1000-6753.tces.240706

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摘要准确地预测电池荷电状态（SOC）对于提高电池的利用效率及安全性能具有重要意义。传统电池SOC预测方法高度依赖机理模型和统计模型,存在异常值敏感、实践精度受限等问题。该文提出一种卷积神经网络-长短时记忆神经网络-注意力机制（CNN-LSTM-AM）模型,通过电池可测变量预测SOC变化趋势。该模型首先利用一维卷积神经网络（CNN）提取可测变量的空间特征;然后将其送至长短时记忆（LSTM）进行时间序列分析;最后引入注意力机制（AM）筛选关键特征,并降低特征数据冗余程度。此外,CNN-LSTM-AM模型在训练过程中采用雾凇优化算法（RIME）进行超参数寻优,有效地提高训练效率、降低训练成本。在磷酸铁锂公开数据集上开展实践测评,结果表明,基于CNN-LSTM-AM模型的电池SOC预测性能良好,优于传统时间序列预测方法,其方均根误差为0.64%、平均绝对误差为0.52%（25℃）。此外,该模型适用于动态工况下的电池状态预测,具有较高的预测精度和鲁棒性。

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关键词 ：储能锂离子电池, 荷电状态, 长短时记忆, 注意力机制, 雾凇优化算法

Abstract：Accurate prediction of the battery state of charge (SOC) is of great significance to improve the utilization efficiency and safety performance of the battery, and the monitoring of the battery state of charge is very important to help prevent overcharge and overdischarge accidents. The traditional SOC prediction methods are highly dependent on the mechanism model and statistical model, and have problems such as sensitive outliers and limited practical accuracy. In this study, a CNN-LSTM-AM (convolutional neural network - long short term memory neural network - attention mechanism) model is proposed to predict SOC variation trend through battery measurable variables.
The model first uses a one-dimensional convolutional neural network to extract spatial features of measurable variables, including battery current, voltage, temperature and average voltage, and then sends them to bidirectional long and short time memory for time series analysis. Finally, the attention mechanism is introduced to screen key features, reduce the redundancy of feature data, and improve the accuracy and generalization of the model. In addition, CNN-LSTM-AM model adopts rime optimization algorithm to optimize the hyperparameters in the training process, which effectively improves the training efficiency and reduces the training cost.
The actual evaluation on CALCE (Center for Advanced Life Cycle Engineering) data set of lithium iron phosphate shows that the attention mechanism can effectively improve the training performance of the prediction model, and the rime optimization algorithm adopted can help reduce the model hyperparameters, so as to obtain higher prediction accuracy. The performance of CNN-LSTM-AM model was tested under different temperature conditions, and both RMSE and MAE were less than 1%, which was sufficient to confirm the feasibility of the model to predict SOC. In addition, even if the initial SOC is uncertain, the proposed CNN-LSTM-AM model can still accurately track SOC trend changes, and the overall prediction accuracy reaches RMSE<1.5% and MAE<1.5%. The RMSE and MAE results of the network proposed in this study are smaller than those of CNN-LSTM and CNN-LSTM-AM. It shows strong robustness and generalization ability. Finally, in order to comprehensively compare the performance of different SOC prediction methods, the CNN-LSTM-AM model proposed in this study is compared with other experimental results. It can be seen that the method proposed in this study has significantly lower RMSE compared with AT-CNN-LSTM. At the same time, considering that the proposed method uses less training set data, we can also see the advantages of the designed network. Compared with EI-LSTM-CO(extended input-LSTM-constrained output), it can be found that the error is close. In addition, EI-LSTM-CO performs some post-processing on the predicted SOC, which can also reflect the superiority of the proposed method.
The following conclusions are drawn from the simulation analysis: (1) A CNN-LSTM-AM model is proposed and applied to the SOC prediction task of battery, which can effectively capture important input features and improve the prediction accuracy. (2) Design a rime optimization algorithm, which can automatically search the optimal solution of CNN-LSTM-AM model, effectively reduce the time cost of hyperparameter optimization. (3) The influence of different ambient temperatures and initial SOC values on the prediction accuracy of CNN-LSTM-AM was studied, and the performance of CNN-LSTM-AM was compared with that of traditional prediction models to verify its strong robustness and high generalization ability.

Key words： Energy storage battery state of charge long and short-term memory attention module RIME

收稿日期: 2024-05-06

PACS:

TM912

基金资助:国家重点研发计划（2023YFB4102904）和低碳智能燃煤发电与超净排放全国重点实验室开放课题（D2022FK080）资助项目

通讯作者: 许传龙男,1978年生,教授,博士生导师,研究方向为智能传感与检测、能源动力测试、能源系统工程。E-mail：chuanlongxu@seu.edu.cn

作者简介: 杜伟男,1998年生,硕士研究生,研究方向为储能电池的状态估计。E-mail：220210465@seu.edu.cn

引用本文:

杜伟, 王圣, 李健, 韩哲哲, 许传龙. 基于CNN-LSTM-AM模型的储能锂离子电池荷电状态预测[J]. 电工技术学报, 2025, 40(9): 2982-2995. Du Wei, Wang Sheng, Li Jian, Han Zhezhe, Xu Chuanlong. Prediction of State of Charge for Energy Storage Lithium-Ion Batteries Based on CNN-LSTM-AM Model. Transactions of China Electrotechnical Society, 2025, 40(9): 2982-2995.

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[1] 武龙星, 庞辉, 晋佳敏, 等. 基于电化学模型的锂离子电池荷电状态估计方法综述[J]. 电工技术学报, 2022, 37(7): 1703-1725.
Wu Longxing, Pang Hui, Jin Jiamin, et al.A review of SOC estimation methods for lithium-ion batteries based on electrochemical model[J]. Transactions of China Electrotechnical Society, 2022, 37(7): 1703-1725.
[2] 刘旖琦, 雷万钧, 刘茜, 等. 基于双自适应扩展粒子滤波器的锂离子电池状态联合估计[J]. 电工技术学报, 2024, 39(2): 607-616.
Liu Yiqi, Lei Wanjun, Liu Qian, et al.Joint state estimation of lithium-ion battery based on dual adaptive extended particle filter[J]. Transactions of China Electrotechnical Society, 2024, 39(2): 607-616.
[3] 刘萍, 李泽文, 蔡雨思, 等. 基于等效电路模型和数据驱动模型融合的SOC和SOH联合估计方法[J]. 电工技术学报, 2024, 39(10): 3232-3243.
Liu Ping, Li Zewen, Cai Yusi, et al.Joint estimation method of SOC and SOH based on fusion of equivalent circuit model and data-driven model[J]. Transactions of China Electrotechnical Society, 2024, 39(10): 3232-3243.
[4] 王义军, 左雪. 锂离子电池荷电状态估算方法及其应用场景综述[J]. 电力系统自动化, 2022, 46(14): 193-207.
Wang Yijun, Zuo Xue.Review on estimation methods for state of charge of lithium-ion battery and their application scenarios[J]. Automation of Electric Power Systems, 2022, 46(14): 193-207.
[5] 李卓昊, 石琼林, 王康丽, 等. 锂离子电池健康状态估计方法研究现状与展望[J]. 电力系统自动化, 2024, 48(20): 109-129.
Li Zhuohao, Shi Qionglin, Wang Kangli, et al.Research status and prospects of state of health estimation methods for lithium-ion batteries[J]. Automation of Electric Power Systems, 2024, 48(20): 109-129.
[6] 方斯顿, 刘龙真, 孔赖强, 等. 基于双向长短期记忆网络含间接健康指标的锂电池SOH估计[J]. 电力系统自动化, 2024, 48(4): 160-168.
Fang Sidun, Liu Longzhen, Kong Laiqiang, et al.State-of-health estimation for lithium-ion batteries incorporating indirect health indicators based on bi-directional long short-term memory networks[J]. Automation of Electric Power Systems, 2024, 48(4): 160-168.
[7] Navas S J, Cabello González G M, Pino F J, et al. Modelling Li-ion batteries using equivalent circuits for renewable energy applications[J]. Energy Reports, 2023, 9(12): 4456-4465.
[8] Wang Jingrong, Meng Jinhao, Peng Qiao, et al.An electrochemical-thermal coupling model for lithium-ion battery state-of-charge estimation with improve dual particle filter framework[J]. Journal of Energy Storage, 2024, 87(5): 111473.
[9] 黄聪生. 一种用于准确库仑计数充电状态估计的锂离子电池故障诊断方法[J]. 电气工程学报, 2023, 19(1): 433-442.
Huang Congsheng.A lithium-ion batteries fault diagnosis method for accurate coulomb counting state-of-charge estimation[J]. Journal of Electrical Engineering & Technology, 2023, 19(1): 433-442.
[10] Gismero A, Schaltz E, Stroe D I.Recursive state of charge and state of health estimation method for lithium-ion batteries based on coulomb counting and open circuit voltage[J]. Energies, 2020, 13(7): 1811.
[11] Salkind A J, Fennie C, Singh P, et al.Determination of state-of-charge and state-of-health of batteries by fuzzy logic methodology[J]. Journal of Power Sources, 1999, 80(1/2): 293-300.
[12] Zheng Yuejiu, Lu Languang, Han Xuebing, et al.LiFePO₄ battery pack capacity estimation for electric vehicles based on charging cell voltage curve transformation[J]. Journal of Power Sources, 2013, 226(3): 33-41.
[13] Bobobee E D, Wang Shunli, Takyi-Aninakwa P, et al.Improved particle swarm optimization-long short-term memory model with temperature compensation ability for the accurate state of charge estimation of lithium-ion batteries[J]. Journal of Energy Storage, 2024, 84(4): 110871.
[14] Prakash V, Vigneswaran T.State of charge estimation of lithium batteries in electric vehicles using IndRNN[J]. IETE Journal of Research, 2023, 69(5): 2886-2896.
[15] 李宁, 何复兴, 马文涛, 等. 基于经验模态分解的门控循环单元神经网络的锂离子电池荷电状态估计[J]. 电工技术学报, 2022, 37(17): 4528-4536.
Li Ning, He Fuxing, Ma Wentao, et al.State-of-charge estimation of lithium-ion battery based on gated recurrent unit using empirical mode decomposition[J]. Transactions of China Electrotechnical Society, 2022, 37(17): 4528-4536.
[16] 刘素贞, 陈云龙, 张闯, 等. 融合多维超声时频域特征的锂离子电池荷电状态估计[J]. 电工技术学报, 2023, 38(17): 4539-4550, 4563.
Liu Suzhen, Chen Yunlong, Zhang Chuang, et al.State of charge estimation of lithium-ion batteries fused with multi dimensional ultrasonic time-frequency domain features[J]. Transactions of China Electrotechnical Society, 2023, 38(17): 4539-4550, 4563.
[17] Zhao Fen, Li Yinguo, Wang Xinheng, et al.Lithium-ion batteries state of charge prediction of electric vehicles using RNNs-CNNs neural networks[J]. IEEE Access, 2020, 8(5): 98168-98180.
[18] Chen Junxiong, Zhang Yu, Wu Ji, et al.SOC estimation for lithium-ion battery using the LSTM-RNN with extended input and constrained output[J]. Energy, 2023, 262(1): 125375.
[19] Le Cun Y, Boser B, Denker J S, et al.Backpropagation applied to handwritten zip code recognition[J]. Neural Computation, 1989, 1(4): 541-551.
[20] 顾久祥, 王振华, 马连阳, 等. 卷积神经网络的最新进展[J]. 模式识别, 2018, 77(3): 354-377.
Gu Jiuxiang, Wang Zhenhua, Ma lianyang, et al. Recent advances in convolutional neural networks[J]. Pattern Recognition, 2018, 77(3): 354-377.
[21] Hochreiter S, Schmidhuber J.Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1735.
[22] Bengio Y, Simard P, Frasconi P.Learning long-term dependencies with gradient descent is difficult[J]. IEEE Transactions on Neural Networks, 1994, 5(2): 157-166.
[23] Lu Wenjie, Li Jiazheng, Wang Jingyang, et al.A CNN-BiLSTM-AM method for stock price prediction[J]. Neural Computing and Applications, 2021, 33(10): 4741-4753.
[24] 苏杭, 赵东, 刘磊, 等. RIME:一种基于物理的优化[J]. 神经计算, 2023, 532(5): 183-214.
Su Hang, Zhao Dong, Liu Lei, et al.RIME:A physics based optimization[J]. Neurocomputing, 2023, 532(5): 183-214.
[25] 刑银娇, 何伟, Pecht Michael, 等. 不同环境温度下基于开路电压的锂离子电池电荷状态估计[J]. 应用能源, 2014, 113(1): 106-115.
Xing Yinjiao, He Wei, Michael P, et al.State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures[J]. Applied Energy, 2014, 113(1): 106-115.
[26] 张帅涛, 蒋品群, 宋树祥, 等. 基于注意力机制和CNN-LSTM融合模型的锂电池SOC预测[J]. 电源学报, 2022, 21(2): 1-15.
Zhang Shuaitao, Jiang Pinqun, Song Shuxiang, et al.SOC prediction for lithium battery based on fusion model of attention mechanism and CNN-LSTM[J]. Journal of Power Supply, 2022, 21(2): 1-15.