Differential voltage analysis based state of charge estimation methods for lithium-ion batteries using extended Kalman filter and particle filter

Accurate battery state of charge (SOC) estimation can contribute to safe and reliable utilization of the battery. However, commonly used battery model-based SOC estimation methods suffer from the lack of a universal battery model for cells in a battery pack since the model parameters of each cell are inevitably different from each other and variable with battery aging, leading to difficulties in promoting the model-based methods for real applications. To solve this problem, a differential voltage (DV) analysis based universal battery model and two associated SOC estimation algorithms using extended Kalman filter (EKF) and particle filter (PF), respectively, are proposed in this paper. By means of a natural cubic interpolation approach, a battery SOC-DV model is firstly derived from the SOC based DV curves of various cells at different aging levels. A novel battery model-based scheme is then proposed to incorporate the SOC-DV model for the estimation. The robustness of the proposed approaches against different cell aging levels is evaluated, and the promising SOC estimates with the maximum absolute error of 1.75% and the root mean square error of less than 1.10% can be achieved.

[1]  Xiaosong Hu,et al.  A comparative study of equivalent circuit models for Li-ion batteries , 2012 .

[2]  Petar M. Djuric,et al.  Resampling Methods for Particle Filtering: Classification, implementation, and strategies , 2015, IEEE Signal Processing Magazine.

[3]  IL-Song Kim,et al.  A Technique for Estimating the State of Health of Lithium Batteries Through a Dual-Sliding-Mode Observer , 2010, IEEE Transactions on Power Electronics.

[4]  Maitane Berecibar,et al.  State of health estimation algorithm of LiFePO4 battery packs based on differential voltage curves for battery management system application , 2016 .

[5]  Han Wang,et al.  A method for SOC estimation based on simplified mechanistic model for LiFePO4 battery , 2016 .

[6]  Jiuchun Jiang,et al.  Butler-Volmer equation-based model and its implementation on state of power prediction of high-power lithium titanate batteries considering temperature effects , 2016 .

[7]  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 .

[8]  Binggang Cao,et al.  The State of Charge Estimation of Lithium-Ion Batteries Based on a Proportional-Integral Observer , 2014, IEEE Transactions on Vehicular Technology.

[9]  Fredrik Gustafsson,et al.  On Resampling Algorithms for Particle Filters , 2006, 2006 IEEE Nonlinear Statistical Signal Processing Workshop.

[10]  Jiahao Li,et al.  A comparative study and validation of state estimation algorithms for Li-ion batteries in battery management systems , 2015 .

[11]  I. Bloom,et al.  Differential voltage analyses of high-power, lithium-ion cells: 1. Technique and application , 2005 .

[12]  Miroslav Krstic,et al.  Adaptive PDE Observer for Battery SOC/SOH Estimation , 2012 .

[13]  Chris Manzie,et al.  Multi-time-scale observer design for state-of-charge and state-of-health of a lithium-ion battery , 2016 .

[14]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation , 2004 .

[15]  Dylan Dah-Chuan Lu,et al.  Incremental capacity analysis and differential voltage analysis based state of charge and capacity estimation for lithium-ion batteries , 2018 .

[16]  Nando de Freitas,et al.  An Introduction to Sequential Monte Carlo Methods , 2001, Sequential Monte Carlo Methods in Practice.

[17]  Taedong Goh,et al.  Capacity estimation algorithm with a second-order differential voltage curve for Li-ion batteries with NMC cathodes , 2017 .

[18]  Jiahao Li,et al.  A comparative study of state of charge estimation algorithms for LiFePO4 batteries used in electric vehicles , 2013 .

[19]  Chenbin Zhang,et al.  A method for state-of-charge estimation of LiFePO4 batteries at dynamic currents and temperatures using particle filter , 2015 .

[20]  Jianguo Zhu,et al.  Novel methods for estimating lithium-ion battery state of energy and maximum available energy , 2016 .

[21]  Xiaosong Hu,et al.  Adaptive unscented Kalman filtering for state of charge estimation of a lithium-ion battery for elec , 2011 .

[22]  Marcos E. Orchard,et al.  Particle-filtering-based estimation of maximum available power state in Lithium-Ion batteries , 2016 .

[23]  Zhongbao Wei,et al.  Online Model Identification and State-of-Charge Estimate for Lithium-Ion Battery With a Recursive Total Least Squares-Based Observer , 2018, IEEE Transactions on Industrial Electronics.

[24]  Binyu Xiong,et al.  Enhanced online model identification and state of charge estimation for lithium-ion battery with a FBCRLS based observer , 2016 .

[25]  Christian Fleischer,et al.  Critical review of the methods for monitoring of lithium-ion batteries in electric and hybrid vehicles , 2014 .

[26]  Lei Zhang,et al.  Co-estimation of state-of-charge, capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical model , 2016 .

[27]  Jae Sik Chung,et al.  A Multiscale Framework with Extended Kalman Filter for Lithium-Ion Battery SOC and Capacity Estimation , 2010 .

[28]  Lin Chen,et al.  State of charge estimation of lithium-ion batteries using a grey extended Kalman filter and a novel open-circuit voltage model , 2017 .

[29]  Hongwen He,et al.  Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles , 2012 .

[30]  Tiancheng Li,et al.  A fast resampling scheme for particle filters , 2013 .

[31]  Yang Li,et al.  Technological Developments in Batteries: A Survey of Principal Roles, Types, and Management Needs , 2017, IEEE Power and Energy Magazine.

[32]  Zhongwei Deng,et al.  Online available capacity prediction and state of charge estimation based on advanced data-driven algorithms for lithium iron phosphate battery , 2016 .

[33]  Hongwen He,et al.  A data-driven multi-scale extended Kalman filtering based parameter and state estimation approach of lithium-ion olymer battery in electric vehicles , 2014 .

[34]  Bo-Hyung Cho,et al.  Li-Ion Battery SOC Estimation Method based on the Reduced Order Extended Kalman Filtering , 2006 .

[35]  Michael Pecht,et al.  A generic model-free approach for lithium-ion battery health management , 2014 .

[36]  Jianqiu Li,et al.  A review on the key issues for lithium-ion battery management in electric vehicles , 2013 .

[37]  Bor Yann Liaw,et al.  On state-of-charge determination for lithium-ion batteries , 2017 .

[38]  Michael Pecht,et al.  Temperature dependent power capability estimation of lithium-ion batteries for hybrid electric vehicles , 2016 .

[39]  Richard D. Braatz,et al.  State-of-charge estimation in lithium-ion batteries: A particle filter approach , 2016 .

[40]  Jasim Ahmed,et al.  Algorithms for Advanced Battery-Management Systems , 2010, IEEE Control Systems.

[41]  Hongwen He,et al.  Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach , 2011 .

[42]  William H. Press,et al.  Numerical recipes in C , 2002 .

[43]  Ralph E. White,et al.  Review of Models for Predicting the Cycling Performance of Lithium Ion Batteries , 2006 .

[44]  Hao Mu,et al.  A novel multi-model probability battery state of charge estimation approach for electric vehicles using H-infinity algorithm , 2016 .

[45]  Guangzhong Dong,et al.  An online model-based method for state of energy estimation of lithium-ion batteries using dual filters , 2016 .