Lithium-ion battery state of health estimation using the incremental capacity and wavelet neural networks with genetic algorithm

Abstract Accurate state of health (SOH) is a crucial factor for the regular operation of the electric vehicle. Compared with the equivalent circuit methods, the data-driven methods do not rely on the battery model and do not need to measure the open-circuit voltage. This paper proposes an on-line method based on the fusion of incremental capacity (IC) and wavelet neural networks with genetic algorithm (GA-WNN) to estimate SOH under current discharge. Firstly, IC curves are acquired, and the important health feature variables are extracted from IC curves using Pearson correlation coefficient method. Second, The GA is used to optimize the initial connection weights, translation factor and scaling factor of WNN; then, the GA-WNN model is applied to estimate battery's SOH. Third, the established model is verified by battery data. Finally, the experiment results show that the SOH estimation error of this method is less than 3%.

[1]  D. B. Agusdinata,et al.  Socio-environmental impacts of lithium mineral extraction: towards a research agenda , 2018, Environmental Research Letters.

[2]  Manuel Baumann,et al.  The environmental impact of Li-Ion batteries and the role of key parameters – A review , 2017 .

[3]  Ali Emadi,et al.  State-of-charge estimation of Li-ion batteries using deep neural networks: A machine learning approach , 2018, Journal of Power Sources.

[4]  Datong Qin,et al.  Lithium-ion battery modeling and parameter identification based on fractional theory , 2018, Energy.

[5]  Huei Peng,et al.  On-board state of health monitoring of lithium-ion batteries using incremental capacity analysis with support vector regression , 2013 .

[6]  A. Calborean,et al.  Resonance frequency analysis of lead-acid cells: An EIS approach to predict the state-of-health , 2020 .

[7]  Jianqiu Li,et al.  Investigating the error sources of the online state of charge estimation methods for lithium-ion batteries in electric vehicles , 2018 .

[8]  Youngki Kim,et al.  Battery Capacity Fading Estimation Using a Force-Based Incremental Capacity Analysis , 2016 .

[9]  Nigel P. Brandon,et al.  Novel application of differential thermal voltammetry as an in-depth state-of-health diagnosis method for lithium-ion batteries , 2016 .

[10]  Lei Zhang,et al.  State-of-health estimation for Li-ion batteries by combing the incremental capacity analysis method with grey relational analysis , 2019, Journal of Power Sources.

[11]  Zhengyu Lv,et al.  High accuracy state-of-charge online estimation of EV/HEV lithium batteries based on Adaptive Wavelet Neural Network , 2013, 2013 IEEE ECCE Asia Downunder.

[12]  Zhenpo Wang,et al.  Prognostic health condition for lithium battery using the partial incremental capacity and Gaussian process regression , 2019, Journal of Power Sources.

[13]  Giorgio Rizzoni,et al.  A multi time-scale state-of-charge and state-of-health estimation framework using nonlinear predictive filter for lithium-ion battery pack with passive balance control , 2015 .

[14]  Boyang Liu,et al.  Joint estimation of battery state-of-charge and state-of-health based on a simplified pseudo-two-dimensional model , 2020 .

[15]  Didier Dumur,et al.  Improved state of charge estimation for Li-ion batteries using fractional order extended Kalman filter , 2019, Journal of Power Sources.

[16]  Weiqing Wang,et al.  Combination of cuckoo search and wavelet neural network for midterm building energy forecast , 2020 .

[17]  J. Kowal,et al.  On-line state-of-health estimation of Lithium-ion battery cells using frequency excitation , 2020 .

[18]  Chunbo Zhu,et al.  A physics-based fractional order model and state of energy estimation for lithium ion batteries. Part I: Model development and observability analysis , 2017 .

[19]  K. Goebel,et al.  Prognostics in Battery Health Management , 2008, IEEE Instrumentation & Measurement Magazine.

[20]  Jean-Michel Vinassa,et al.  Behavior and state-of-health monitoring of Li-ion batteries using impedance spectroscopy and recurrent neural networks , 2012 .

[21]  Xuezhe Wei,et al.  Joint estimation of lithium-ion battery state of charge and capacity within an adaptive variable multi-timescale framework considering current measurement offset , 2019, Applied Energy.

[22]  Cao Binggang,et al.  State of charge estimation based on evolutionary neural network , 2008 .

[23]  T. Murariu,et al.  Time-dependent analysis of the state-of-health for lead-acid batteries: An EIS study , 2019, Journal of Energy Storage.

[24]  Yi Wang,et al.  Study on wavelet neural network based anomaly detection in ocean observing data series , 2019, Ocean Engineering.

[25]  Xuning Feng,et al.  A control-oriented electrochemical model for lithium-ion battery. Part II: Parameter identification based on reference electrode , 2020 .

[26]  Pan Chaofeng,et al.  On-board state of health estimation of LiFePO4 battery pack through differential voltage analysis , 2016 .

[27]  Daniel-Ioan Stroe,et al.  Lithium-Ion Battery State-of-Health Estimation Using the Incremental Capacity Analysis Technique , 2020, IEEE Transactions on Industry Applications.

[28]  Zetian Fu,et al.  Precision fertilization method of field crops based on the Wavelet-BP neural network in China , 2020 .

[29]  Xin Tang,et al.  A novel deep learning framework for state of health estimation of lithium-ion battery , 2020 .

[30]  A. Izadian,et al.  Electrochemical model parameter identification of a lithium-ion battery using particle swarm optimization method , 2016 .

[31]  Kaike Wang,et al.  Synchronous estimation of state of health and remaining useful lifetime for lithium-ion battery using the incremental capacity and artificial neural networks , 2019 .

[32]  Minggao Ouyang,et al.  Incremental Capacity Analysis on Commercial Lithium-Ion Batteries Using Support Vector Regression: A Parametric Study , 2018, Energies.

[33]  Valner Joao Brusamarello,et al.  Parameter Identification and Analysis of Uncertainties in Measurements of Lead–Acid Batteries , 2014, IEEE Transactions on Instrumentation and Measurement.

[34]  Joeri Van Mierlo,et al.  A quick on-line state of health estimation method for Li-ion battery with incremental capacity curves processed by Gaussian filter , 2018 .

[35]  Hanxu Sun,et al.  Open-Circuit Voltage-Based State of Charge Estimation of Lithium-ion Battery Using Dual Neural Network Fusion Battery Model , 2016 .