Remaining useful life and state of health prediction for lithium batteries based on empirical mode decomposition and a long and short memory neural network

Abstract Accurate estimation and prediction of the state of health (SOH) and remaining useful life (RUL) are crucial for battery management systems, which have an important role in the field of new energy. This work combined the empirical mode decomposition (EMD) method and backpropagation long-short-term memory (B-LSTM) neural network (NN) to develop SOH estimation and RUL prediction models. The B-LSTM NN of the many-to-one structure uses easily available battery parameters, such as current and voltage, to estimate the SOH. SOH data are processed through the EMD method—to reduce the impact of capacity regeneration and other situations—after which the backpropagation of the one-to-one structure NN performs a RUL prediction. Compared with the current data-driven forecasting model, the model has a simple structure and high accuracy. For SOH estimation, the average root mean square error was 0.02, which was nearly four times lower than that of a simple recurrent NN. For the RUL prediction model, EMD effectively removed noise signals and improved prediction accuracy. The prediction results of the model for different batteries showed good accuracy, indicating that this combined model has high robustness, good accuracy, and applicability.

[1]  Huajing Fang,et al.  A new hybrid method for the prediction of the remaining useful life of a lithium-ion battery , 2017 .

[2]  Wei Liang,et al.  Remaining useful life prediction of lithium-ion battery with unscented particle filter technique , 2013, Microelectron. Reliab..

[3]  Weihua Li,et al.  State-of-charge estimation of lithium-ion batteries using LSTM and UKF , 2020, Energy.

[4]  Yigang He,et al.  Remaining Useful Life Prediction and State of Health Diagnosis of Lithium-Ion Battery Based on Second-Order Central Difference Particle Filter , 2020, IEEE Access.

[5]  Azah Mohamed,et al.  A review of lithium-ion battery state of charge estimation and management system in electric vehicle applications: Challenges and recommendations , 2017 .

[6]  Guangzhao Luo,et al.  Multiobjective Optimization of Data-Driven Model for Lithium-Ion Battery SOH Estimation With Short-Term Feature , 2020, IEEE Transactions on Power Electronics.

[7]  Sung-Bae Cho,et al.  Predicting residential energy consumption using CNN-LSTM neural networks , 2019, Energy.

[8]  Ji Wu,et al.  A Novel Estimation Method for the State of Health of Lithium-Ion Battery Using Prior Knowledge-Based Neural Network and Markov Chain , 2019, IEEE Transactions on Industrial Electronics.

[9]  Penghua Li,et al.  State-of-health estimation and remaining useful life prediction for the lithium-ion battery based on a variant long short term memory neural network , 2020, Journal of Power Sources.

[10]  Yao Lei,et al.  An Intelligent Fault Diagnosis Method for Lithium Battery Systems Based on Grid Search Support Vector Machine , 2021, Energy.

[11]  Ming Zhang,et al.  Research on variational mode decomposition in rolling bearings fault diagnosis of the multistage centrifugal pump , 2017 .

[12]  Xiaosong Hu,et al.  State estimation for advanced battery management: Key challenges and future trends , 2019, Renewable and Sustainable Energy Reviews.

[13]  Aldo Romero-Becerril,et al.  Comparison of discretization methods applied to the single-particle model of lithium-ion batteries , 2011 .

[14]  Beitong Zhou,et al.  Remaining useful life prediction of lithium-ion batteries based on false nearest neighbors and a hybrid neural network , 2019, Applied Energy.

[15]  Hee-Yeon Ryu,et al.  LSTM-Based Battery Remaining Useful Life Prediction With Multi-Channel Charging Profiles , 2020, IEEE Access.

[16]  N. Huang,et al.  The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis , 1998, Proceedings of the Royal Society of London. Series A: Mathematical, Physical and Engineering Sciences.

[17]  Guangzhao Luo,et al.  An evolutionary framework for lithium-ion battery state of health estimation , 2019, Journal of Power Sources.

[18]  Simona Onori,et al.  A control-oriented cycle-life model for hybrid electric vehicle lithium- ion batteries , 2016 .

[19]  T. O'Donovan,et al.  Novel experimental approach for the characterisation of Lithium-Ion cells performance in isothermal conditions , 2021, Energy.

[20]  Erik Frisk,et al.  Data-Driven Battery Lifetime Prediction and Confidence Estimation for Heavy-Duty Trucks , 2018, IEEE Transactions on Reliability.

[21]  Zonghai Chen,et al.  State-of-health estimation for the lithium-ion battery based on support vector regression , 2017, Applied Energy.

[22]  S. Choe,et al.  Online state of health and aging parameter estimation using a physics-based life model with a particle filter , 2020 .

[23]  Jinpeng Tian,et al.  Towards a smarter battery management system: A critical review on battery state of health monitoring methods , 2018, Journal of Power Sources.

[24]  Yonggang Liu,et al.  State of Health Estimation for Lithium-ion Batteries Based on Fusion of Autoregressive Moving Average Model and Elman Neural Network , 2019, IEEE Access.

[25]  Ramesh K. Agarwal,et al.  Extraction of battery parameters of the equivalent circuit model using a multi-objective genetic algorithm , 2014 .

[26]  Jun Lu,et al.  Batteries and fuel cells for emerging electric vehicle markets , 2018 .

[27]  Jonghyun Park,et al.  A Single Particle Model with Chemical/Mechanical Degradation Physics for Lithium Ion Battery State of Health (SOH) Estimation , 2018 .

[28]  Zonghai Chen,et al.  Modeling and state-of-charge prediction of lithium-ion battery and ultracapacitor hybrids with a co-estimator , 2017 .

[29]  Ali Emadi,et al.  Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries , 2018, IEEE Transactions on Industrial Electronics.

[30]  Kwok-Leung Tsui,et al.  Early prediction of battery lifetime via a machine learning based framework , 2021, Energy.

[31]  Jianhua Xu,et al.  State of energy estimation for a series-connected lithium-ion battery pack based on an adaptive weighted strategy , 2021 .

[32]  I A Basheer,et al.  Artificial neural networks: fundamentals, computing, design, and application. , 2000, Journal of microbiological methods.

[33]  José Antonio Lozano,et al.  Sensitivity Analysis of k-Fold Cross Validation in Prediction Error Estimation , 2010, IEEE Transactions on Pattern Analysis and Machine Intelligence.

[34]  Yigang He,et al.  Capacity Prognostics of Lithium-Ion Batteries using EMD Denoising and Multiple Kernel RVM , 2017, IEEE Access.

[35]  Zhongwei Deng,et al.  Online identification of lithium-ion battery state-of-health based on fast wavelet transform and cross D-Markov machine , 2018 .

[36]  Zonghai Chen,et al.  A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems , 2020 .

[37]  Jürgen Schmidhuber,et al.  LSTM: A Search Space Odyssey , 2015, IEEE Transactions on Neural Networks and Learning Systems.

[38]  Hongwen He,et al.  An improved vehicle to the grid method with battery longevity management in a microgrid application , 2020 .

[39]  Jianqiu Li,et al.  Simplification of physics-based electrochemical model for lithium ion battery on electric vehicle. Part I: Diffusion simplification and single particle model , 2015 .

[40]  Dong Gao,et al.  Prediction of Remaining Useful Life of Lithium-ion Battery based on Multi-kernel Support Vector Machine with Particle Swarm Optimization , 2017 .

[41]  Zonghai Chen,et al.  Degradation model and cycle life prediction for lithium-ion battery used in hybrid energy storage system , 2019, Energy.

[42]  Kexiang Wei,et al.  Feature parameter extraction and intelligent estimation of the State-of-Health of lithium-ion batteries , 2019, Energy.

[43]  Yunlong Shang,et al.  A Data-Driven Approach With Uncertainty Quantification for Predicting Future Capacities and Remaining Useful Life of Lithium-ion Battery , 2021, IEEE Transactions on Industrial Electronics.

[44]  Guangzhong Dong,et al.  Remaining Useful Life Prediction and State of Health Diagnosis for Lithium-Ion Batteries Using Particle Filter and Support Vector Regression , 2018, IEEE Transactions on Industrial Electronics.

[45]  Guangzhao Luo,et al.  An optimized ensemble learning framework for lithium-ion Battery State of Health estimation in energy storage system , 2020 .

[46]  Lifeng Wu,et al.  Prognostics of battery cycle life in the early-cycle stage based on hybrid model , 2021 .

[47]  Zhenpo Wang,et al.  State of health estimation for Li-ion battery via partial incremental capacity analysis based on support vector regression , 2020, Energy.

[48]  Lei Zhang,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 , 2019, Journal of Energy Storage.

[49]  Weige Zhang,et al.  A Hybrid Method for the Prediction of the Remaining Useful Life of Lithium-Ion Batteries With Accelerated Capacity Degradation , 2020, IEEE Transactions on Vehicular Technology.