Estimation and prediction of state of health of electric vehicle batteries using discrete incremental capacity analysis based on real driving data

Abstract The accuracy of the state of health (SoH) estimation and prediction is of great importance to the operational effectiveness and safety of electric vehicles. Present approaches mostly employ data-driven analysis with laboratory measurements to determine these parameters. Here a novel method is proposed using discrete incremental capacity analysis based on real-life driving data, which enables to estimate the battery SoH without any prior detailed knowledge of battery internal specifics such as current capacity/resistance information. The method accounts for the battery characteristics. It is robust, highly compatible, and has a short computing time and low memory requirement. It’s capable to evaluate the SoH of various type of electric vehicles under different charging strategies. The short computing time and low memory needed for the SoH estimation also demonstrates its potential for practical use. Moreover, the clustering analysis is presented, which provides SoH comparison information of certain EV to that of EVs belonging to same type.

[1]  Lin Chen,et al.  Battery state-of-health estimation based on a metabolic extreme learning machine combining degradation state model and error compensation , 2021 .

[2]  Dirk Uwe Sauer,et al.  Adaptive estimation of the electromotive force of the lithium-ion battery after current interruption for an accurate state-of-charge and capacity determination , 2013 .

[3]  Hongwen He,et al.  Towards a smarter battery management system: A critical review on optimal charging methods of lithium ion batteries , 2019, Energy.

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

[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]  Krishnan S. Hariharan,et al.  Deep Gaussian process regression for lithium-ion battery health prognosis and degradation mode diagnosis , 2020 .

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

[8]  M. Fowler,et al.  Design of a Hybrid Electric Vehicle Powertrain for Performance Optimization Considering Various Powertrain Components and Configurations , 2020, Vehicles.

[9]  Hongbin Ren,et al.  Design and implementation of a battery management system with active charge balance based on the SOC and SOH online estimation , 2019, Energy.

[10]  Xuezhe Wei,et al.  Incremental capacity analysis based adaptive capacity estimation for lithium-ion battery considering charging condition , 2020 .

[11]  Guangzhao Luo,et al.  Lithium-ion battery state of health estimation with short-term current pulse test and support vector machine , 2018, Microelectron. Reliab..

[12]  Chao Hu,et al.  A deep learning method for online capacity estimation of lithium-ion batteries , 2019, Journal of Energy Storage.

[13]  M. Dubarry,et al.  Identifying battery aging mechanisms in large format Li ion cells , 2011 .

[14]  Peter Lund,et al.  Improving the state of charge estimation of reused lithium-ion batteries by abating hysteresis using machine learning technique , 2020, Journal of Energy Storage.

[15]  Michael Buchholz,et al.  Health diagnosis and remaining useful life prognostics of lithium-ion batteries using data-driven methods , 2013 .

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

[17]  Peter Lund,et al.  A novel clustering algorithm for grouping and cascade utilization of retired Li-ion batteries , 2020 .

[18]  Guang Li,et al.  A uniform estimation framework for state of health of lithium-ion batteries considering feature extraction and parameters optimization , 2020 .

[19]  Guangzhao Luo,et al.  Lithium-ion battery state-of-health estimation in electric vehicle using optimized partial charging voltage profiles , 2019, Energy.

[20]  E. S. Gardner.  EXPONENTIAL SMOOTHING: THE STATE OF THE ART, PART II , 2006 .

[21]  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.

[22]  Jun Lu,et al.  State-of-the-art characterization techniques for advanced lithium-ion batteries , 2017, Nature Energy.

[23]  Siamak Farhad,et al.  Modeling and Evaluation of Li-Ion Battery Performance Based on the Electric Vehicle Field Tests , 2014 .

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

[25]  Ibrahim Dincer,et al.  Cycling degradation testing and analysis of a LiFePO4 battery at actual conditions , 2017 .

[26]  Zonghai Chen,et al.  A novel Gaussian process regression model for state-of-health estimation of lithium-ion battery using charging curve , 2018 .

[27]  Chris Manzie,et al.  A Framework for Simplification of PDE-Based Lithium-Ion Battery Models , 2016, IEEE Transactions on Control Systems Technology.

[28]  Michael A. Osborne,et al.  Gaussian process regression for forecasting battery state of health , 2017, 1703.05687.

[29]  I. Villarreal,et al.  Critical review of state of health estimation methods of Li-ion batteries for real applications , 2016 .