Transfer Learning Denoising Autoencoder-Long Short Term Memory for Remaining Useful Life Prediction of Li-Ion Batteries
Yin Jie1, Liu Bo1, Sun guobing2, Qian xiangwei1
1. School of Measurement and Control Technology and Communication Engineering Harbin University of Science and Technology Harbin 150080 China; 2. Electronic Engineering College Heilongjiang University Harbin 150006 China
Abstract：Degradation data of battery capacity can be used to predict the battery remaining using life(RUL), but there exist numerous noise data in the battery degradation process caused by factors such as ambient temperature, charge/discharge process and capacity recovery phenomenon. It makes prediction of data-driven life lithium-ion battery challenging. To improve the prediction accuracy and generalization ability of batteries RUL, we proposed a method based on transfer learning and denoising autoencoder-long short term memory (DAE-LSTM). Firstly, the variational autoencoder - generative adversarial network (VAE-GAN) method was constructed designed. Encoding network was used to estimate the distribution of input data, and generating network and discriminant network were used for data regeneration. It improved the reliability of generated data by VAE method, and solved the problem that GAN method had been difficult to train. Secondly, the DAE-LSTM method was constructed for data denoising and capacity prediction. The DAE can reconstruct the input data and its encoder improved the robustness of the method by adding Gaussian noise. LSTM layer can analyze the temporal characteristics of data for capacity prediction. Due to the small amount of data, the network layer of the overall method was less to avoid overfitting. To reduce the parameters, the same loss function was used in both data denoising and capacity prediction. Finally, the optimal training scheme was determined through the different experiment: The data generated by VAE-GAN was used for method pre-training, then all network layers of the basic method were fine-tuned by actual data. This would effectively improve the prediction accuracy of the method, and ensure the reliability of the prediction results. Experimental results showed that the proposed method has better predictive performance, and degradation trend of most batteries can be well predicted. MAE and RMSE were controlled within 2.46% and 3.76% respectively, and the lowest was 0.95% and 1.06%. Experimental results with different prediction starting points showed that the prediction were more accurate when the prediction starting point was closer to the failure threshold. This indicates that the method can accurately predict the degradation trend in later stages of battery life. Experimental results with other datasets showed that the proposed method has strong adaptability and generalization ability. It can effectively predict the lithium-ion battery RUL in small data samples. The 90% confidence interval of the prediction results with NASA dataset is narrow, indicating that the method has strong robustness. In addition, we counted the time taken to complete the RUL prediction for different datasets. As the RUL prediction of batteries is offline prediction with low real-time requirement, the training and testing time of the method meets the offline prediction requirement. The following conclusions can be drawn from the simulation results: (1) The DAE-LSTM method can effectively denoising the degradation data of lithium-ion batteries, and making the prediction result more accurate. (2) VAE-GAN method can generate multiple groups of degradation data conforming to the real degradation to achieve the purpose of data enhancement. (3) Transfer Learning can ensure that the effective information of generated data and real data is fully utilized, so that the prediction model has higher accuracy and better generalization ability. By comparing the prediction results of other literatures, it is proved the proposed method has higher Pre and can be used to predict the RUL of lithium-ion batteries.
尹杰, 刘博, 孙国兵, 钱湘伟. 基于迁移学习和降噪自编码器-长短时间记忆的锂离子电池剩余寿命预测[J]. 电工技术学报, 2024, 39(1): 289-302.
Yin Jie, Liu Bo, Sun guobing, Qian xiangwei. Transfer Learning Denoising Autoencoder-Long Short Term Memory for Remaining Useful Life Prediction of Li-Ion Batteries. Transactions of China Electrotechnical Society, 2024, 39(1): 289-302.
[1] 黄凯, 丁恒, 郭永芳, 等. 基于数据预处理和长短期记忆神经网络的锂离子电池寿命预测[J]. 电工技术学报, 2022, 37(15): 3753-3766. Huang Kai, Ding Heng, Guo Yongfang, et al.Prediction of remaining useful life of lithium-ion battery based on adaptive data preprocessing and long short-term memory network[J]. Transactions of China Electrotechnical Society, 2022, 37(15): 3753-3766. [2] 肖迁, 焦志鹏, 穆云飞, 等. 基于LightGBM的电动汽车行驶工况下电池剩余使用寿命预测[J]. 电工技术学报, 2021, 36(24): 5176-5185. Xiao Qian, Jiao Zhipeng, Mu Yunfei, et al.LightGBM based remaining useful life prediction of electric vehicle lithium-ion battery under driving conditions[J]. Transactions of China Electrotechnical Society, 2021, 36(24): 5176-5185. [3] 徐佳宁, 倪裕隆, 朱春波. 基于改进支持向量回归的锂电池剩余寿命预测[J]. 电工技术学报, 2021, 36(17): 3693-3704. Xu Jianing, Ni Yulong, Zhu Chunbo.Remaining useful life prediction for lithium-ion batteries based on improved support vector regression[J]. Transactions of China Electrotechnical Society, 2021, 36(17): 3693-3704. [4] 牛志远, 姜欣, 谢镔, 等. 电动汽车过充燃爆事故模拟及安全防护研究[J]. 电工技术学报, 2022, 37(1): 36-47, 57. Niu Zhiyuan, Jiang Xin, Xie Bin, et al.Study on simulation and safety protection of electric vehicle overcharge and explosion accident[J]. Transactions of China Electrotechnical Society, 2022, 37(1):36-47, 57. [5] Ahmad Rahmoun,Helmuth Biechl.Modelling of li-ion batteries usingequivalent circuit diagrams[J]. Przeglad Elektrotechniczny, 2012, 88(7): 152-156. [6] Quentin Badey, Guillaume Cherouvrier,Yvan Reynier, et al.Ageing forecast of li-ion batteries for electric and hybridvehicles[J]. Curr. Top. Electrochem, 2011, 16: 65-79. [7] 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. [8] Shahid F, Zameer A, Muneeb M.A novel genetic LSTM model for wind power forecast[J]. Energy, 2021, 223: 120069. [9] Zhang Yongzhi, Xiong Rui, He Hongwen, et al.Long short-term memory recurrent neural network for remaining useful life prediction of lithium-ion batteries[J]. IEEE Transactions on Vehicular Technology, 2018, 67(7): 5695-5705. [10] Khumprom P, Yodo N.A data-driven predictive prognostic model for lithium-ion batteries based on a deep learning algorithm[J]. Energies, 2019, 12(4): 660. [11] 李超然, 肖飞, 樊亚翔, 等. 基于卷积神经网络的锂离子电池SOH估算[J]. 电工技术学报, 2020, 35(19): 4106-4119. Li Chaoran, Xiao Fei, Fan Yaxiang, et al.An approach to lithium-ion battery SOH estimation based on convolutional neural network[J]. Transactions of China Electrotechnical Society, 2020, 35(19): 4106-4119. [12] Yu Yong, Hu Changhua, Si Xiaosheng, et al.Averaged Bi-LSTM networks for RUL prognostics with non-life-cycle labeled dataset[J]. Neurocomputing, 2020, 402: 134-147. [13] Qiao Jianshu, Liu Xiaofeng, Chen Zehua.Prediction of the remaining useful life of lithium-ion batteries based on empirical mode decomposition and deep neural networks[J]. IEEE Access, 2020, 8: 42760-42767. [14] Wang Zhuqing, Liu Ning, Guo Yangming.Adaptive sliding window LSTM NN based RUL prediction for lithium-ion batteries integrating LTSA feature reconstruction[J]. Neurocomputing, 2021, 466: 178-189. [15] He Wei, Williard N, Osterman M, et al.Prognostics of lithium-ion batteries based on Dempster-Shafer theory and the Bayesian Monte Carlo method[J]. Journal of Power Sources, 2011, 196(23): 10314-10321. [16] Wang Haiyang, Song Wanqing, Zio E, et al.Remaining useful life prediction for Lithium-ion batteries using fractional Brownian motion and Fruit-fly Optimization Algorithm[J]. Measurement, 2020, 161: 107904. [17] Saha B, Goebel K. Battery Data Set, NASA Ames Prognostics Data Repository[OL]. NASA Ames Research Center, Moffett Field, CA. 2007. Available: http://ti.arc.nasa.gov/project/prognostic-data-repository. [18] Dong Hancheng, Jin Xiaoning, Lou Yangbing, et al.Lithium-ion battery state of health monitoring and remaining useful life prediction based on support vector regression-particle filter[J]. Journal of Power Sources, 2014, 271: 114-123. [19] Kingma D P, Welling M. Auto-encoding variational Bayes[C]//International Conference in Learning Representations, Banff, Canada, 2014, 10.48550/ arXiv.1312.6144. [20] Goodfellow I J, Pouget-Abadie Jran, Mirza M, et al.Generative adversarial nets[C]// Neural Information Processing Systems, 2014: 2672-2680. [21] 郑华林, 王超, 潘盛湖, 等. 基于EEMD和分层阈值的磁记忆信号降噪方法研究[J]. 工程设计学报, 2020, 27(4): 433-440. Zheng Hualin, Wang Chao, Pan Shenghu, et al.Research on noise reduction method of magnetic memory signal based on EEMD and layered threshold[J]. Chinese Journal of Engineering Design, 2020, 27(4): 433-440. [22] Frangopol D M, Lin K Y, Estes A C.Life-cycle cost design of deteriorating structures[J]. Journal of Structural Engineering, 1997, 123(10): 1390-1401. [23] Vincent P, Larochelle H, Bengio Y, et al.Extracting and composing robust features with denoising autoencoders[C]//Proceedings of the 25th international conference on Machine learning, Helsinki, Finland, 2008: 1096-1103. [24] Hochreiter S, Schmidhuber J.Long short-term memory[J]. Neural Computation, 1997, 9(8): 1735-1780. [25] Yang Jinsong, Fang Weiguang, Chen Jiayu, et al.A lithium-ion battery remaining useful life prediction method based on unscented particle filter and optimal combination strategy[J]. Journal of Energy Storage, 2022, 55: 105648. [26] Zhang Yong, Chen L, Li Yi, et al.A hybrid approach for remaining useful life prediction of lithium-ion battery with adaptive levy flight optimized particle filter and long short-term memory network[J]. Journal of Energy Storage, 2021, 44: 103245. [27] Xue Zhiwei, Zhang Yong, Cheng Cheng, et al.Remaining useful life prediction of lithium-ion batteries with adaptive unscented Kalman filter and optimized support vector regression[J]. Neurocomputing, 2020, 376: 95-102. [28] Pang Xiaoqiong, Liu Xiaoyan, Jia Jianfang, et al.A lithium-ion battery remaining useful life prediction method based on the incremental capacity analysis and Gaussian process regression[J]. Microelectronics Reliability, 2021, 127: 114405.
2024, Vol. 39 
