State of Health Prediction for Lithium-ion Batteries Based on Empirical Mode Decomposition

doi:10.3901/JME.2024.14.272

Abstract

Abstract: The prediction of battery state of health(SOH) is a key factor to ensure the reliability and safety of electronic system operation. In order to accurately predict the overall degradation trend and local capacity regeneration of lithium-ion battery SOH, a lithium-ion battery SOH prediction method combining empirical mode decomposition(EMD), gated recurrent unit(GRU) and differential autoregressive integrated moving average model(ARIMA) is proposed. First, the original SOH sequence of the battery is decomposed at multiple scales using EMD, and the high and low frequency demarcation points are found by calculating the continuous mean square error of the decomposed subsequences; then, GRU is used to predict high-frequency subsequences with strong data fluctuations, and ARIMA is used to predict the remaining low-frequency subsequences and residuals; finally, the prediction results of each subsequence are superimposed to obtain the final prediction result. The experimental results show that, compared with the prediction methods in other literatures, the fusion model based on empirical mode decomposition has higher prediction accuracy and can better capture the overall degradation trend and local capacity regeneration characteristics of battery SOH.

Key words: lithium-ion battery, empirical mode decomposition, state of health prediction, capacity regeneration, fusion model

CLC Number:

TM912

LIU Zhengyu, ZHANG Zheng, GUO Lekai, MENG Hui, LIU Xiang. State of Health Prediction for Lithium-ion Batteries Based on Empirical Mode Decomposition[J]. Journal of Mechanical Engineering, 2024, 60(14): 272-281.

References

[1] Yang S，Zhang C，Jiang J，et al. Review on state-of-health of lithium-ion batteries：Characterizations, estimations and applications[J]. Journal of Cleaner Production，2021，314：128015.
[2] 刘大同，周建宝，郭力萌，等. 锂离子电池健康评估和寿命预测综述[J]. 仪器仪表学报，2015，36(1)：1-16. Liu Datong，Zhou Jianbao，Guo Limeng，et al. Survey on lithium-ion battery health assessment and cycle life estimation[J]. Chinese Journal of Scientific Instrument，2015，36(1)：1-16.
[3] Tian Huixin，Qin Pengliang，Li Kun，et al. A review of the state of health for lithium-ion batteries：Research status and suggestions[J]. Journal of Cleaner Production，2020，261(1)：120813.
[4] Ge Mingfeng，Liu Yiben，Jiang Xingxing，et al. A review on state of health estimations and remaining useful life prognostics of lithium-ion batteries[J]. Measurement，2021，174：109057.
[5] Liu Fang，Liu Xinyi，Su Weixing，et al. An online state of health estimation method based on battery management system monitoring data[J]. International Journal of Energy Research，2020，44(8)：6338-6349.
[6] Fang L，Li J，Peng B. Online estimation and error analysis of both SOC and SOH of Lithium-ion battery based on DEKF method[J]. Energy Procedia，2019，158：3008-3013.
[7] 徐兴，徐琪凌，王峰，等. 车用锂离子动力电池电化学模型修正方法[J]. 机械工程学报，2019，55(12)：128-136. XU Xing，XU Qiling，WANG Feng，et al. Modification method of electrochemical model for vehicular lithium-ion power battery[J]. Journal of Mechanical Engineering，2019，55(12)：128-136.
[8] Singh P，Chen C，Tan C M，et al. Semi-empirical capacity fading model for SOH estimation of Lithium-ion batteries[J]. Applied Sciences，2019，9(15)：3012.
[9] 陈峥，李磊磊，舒星，等. 基于特征处理与径向基神经网络的锂电池剩余容量估算方法[J]. 储能科学与技术，2021，10(1)：261-270. CHEN Zheng，LI Leilei，SHU Xing，et al. Efficient remaining capacity estimation method for LIB based on feature processing and the RBF neural network[J]. Energy Storage Science and Technology，2021，10(1)：261-270.
[10] Liu D，Pang J，Zhou J，et al. Prognostics for state of health estimation of lithium-ion batteries based on combination Gaussian process functional regression[J]. Microelectronics Reliability，2013，53(6)：832-839.
[11] Qu J，Liu F，Ma Y，et al. A neural-network-based method for RUL prediction and SOH monitoring of lithium-ion battery[J]. IEEE Access，2019，7(99)：87178-87191.
[12] Li Y，Liu K，Foley A M，et al. Data-driven health estimation and lifetime prediction of lithium-ion batteries：A review[J]. Renewable and Sustainable Energy Reviews，2019，113：109254.
[13] 张任，胥芳，陈教料，等. 基于PSO-RBF神经网络的锂离子电池健康状态预测[J]. 中国机械工程，2016，27(21)：2975-2981. Zhang Ren，Xu Fang，Chen Jiaoliao，et al Battery SOH prediction based on PSO-RBF neural network[J]. China Mechanical Engineering，2016，27(21)：2975-2981.
[14] 李超然，肖飞，樊亚翔，等. 基于深度学习的锂离子电池SOC和SOH联合估算[J]. 中国电机工程学报，2021，41(2)：681-692. LI Chaoran，XIAO Fei，FAN Yaxiang，et al. Joint estimation of the state of charge and the state of health based on deep learning for lithium-ion batteries[J]. Proceedings of the CSEE，2021，41(2)：681-692.
[15] Chang Yang，Fang Huajing. A hybrid prognostic method for system degradation based on particle filter and relevance vector machine[J]. Reliability Engineering and System Safety，2019，186：51-63.
[16] Spotnitz R. Simulation of capacity fade in lithium-ion batteries[J]. Journal of Power Sources，2003，113(1)：72-80.
[17] Zhao L，Wang Y，Cheng J. A hybrid method for remaining useful life estimation of lithium-ion battery with regeneration phenomena[J]. Applied Sciences，2019，9(9)：1890.
[18] He Y，Shen J，Shen J，et al. State of health estimation of lithium-ion batteries：A multiscale Gaussian process regression modeling approach[J]. Aiche Journal，2015，61(5)：1589-1600.
[19] Zhou Y，Huang M. Lithium-ion batteries remaining useful life prediction based on a mixture of empirical mode decomposition and ARIMA model[J]. Microelectronics Reliability，2016，65：265-273.
[20] Yu J. State of health prediction of lithium-ion batteries：Multiscale logic regression and Gaussian process regression ensemble[J]. Reliability Engineering & System Safety，2018，174：82-95.
[21] Li X，Zhang L，Wang Z，et al. Remaining useful life prediction for lithium-ion batteries based on a hybrid model combining the long short-term memory and Elman neural networks[J]. Journal of Energy Storage，2019，21：510-518.
[22] Cho K，Merrienboer B V，Gulcehre C，et al. Learning phrase representations using rnn encoder-decoder for statistical machine translation[J]. Computer Science，2014：1724-1734.
[23] Saha B，Goebel K. Battery data set[R]. NASA AMES Prognostics Data Repository，CA：Moffett Field，2007.
[24] Xia F，Wang K，Chen J. State of health prediction for lithium-ion batteries based on complete ensemble empirical mode decomposition with adaptive noise—Gate recurrent unit fusion model[J]. Energy Technology，2022，10(4)：2100767.
[25] Qin T，Zeng S，Guo J，et al. A rest time-based prognostic framework for state of health estimation of lithium-ion batteries with regeneration phenomena[J]. Energies，2016，9(11)：896.
[26] Qin T，Zeng S，Guo J. Robust prognostics for state of health estimation of lithium-ion batteries based on an improved. PSO-SVR model[J]. Microelectronics Reliability，2015，55(9-10)：1280-1284.
[27] 苏福清，匡洪海，钟浩，等. 基于柯西变异改进粒子群算法的无功优化[J]. 电气工程学报，2021，16(1)：55-61. SU Fuqing，KUANG Honghai，ZHONG Hao，et al. Reactive power optimization based on cauchy mutation and improved adaptive particle swarm optimization[J]. Journal of Electrical Engineering，2021，16(1)：55-61.
[28] 李彦梅，刘惠汉，张朝龙，等. 基于双高斯模型的锂电池剩余使用寿命预测方法[J]. 电气工程学报，2022，17(4)：32-40. LI Yanmei，LIU Huihan，ZHANG Chaolong，et al. Lithium-ion battery RUL prediction method based on double Gaussian model[J]. Journal of Electrical Engineering，2022，17(4)：32-40.
[29] 刘王泽宇，李青，庾甜甜，等. 锂离子电池过放电状态的阻抗特性研究[J]. 电气工程学报，2022，17(4)：51-60. LIU Wangzeyu，LI Qing，YU Tiantian，et al. Study on impedance characteristics of lithium-ion battery in over discharge state[J]. Journal of Electrical Engineering，2022，17(4)：51-60.