State of charge estimation based on a simplified electrochemical model for a single LiCoO2 battery and battery pack

Accurate battery state of charge (SOC) estimation can contribute to a reasonable charging/discharging strategy for battery management systems (BMSs). It can also prevent severe damage to the battery (pack) caused by over-charging or over-discharging. This work develops a battery SOC estimation method based on a simplified electrochemical model. Simulated validation under dynamic current loads at room temperature showed a maximum SOC error of less than 2.37% within the whole range for a single cell. The developed method can reach a balance between estimation accuracy and computational cost, with average iterative calculation time of about 0.05 ms. A charging/discharging control strategy for battery packs with deep charging depth and fast speed has also been developed, and it can help identify the “weakest’’ cell according to the definition of battery pack SOC. Statistical results show that the SOC average absolute error (AAE) at two constant discharge C-rates ranged from 0.44% to 1.65%. Analysis and assessment of the accuracy and robustness of the developed method for single cells and battery packs indicate that the SOC estimation accuracy is acceptable and shows potential for applications in BMSs.

[1]  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 .

[2]  M. Pecht,et al.  Cycle life testing and modeling of graphite/LiCoO 2 cells under different state of charge ranges , 2016 .

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

[4]  Ibrahim Dincer,et al.  Smart energy systems for a sustainable future , 2017 .

[5]  Wei Sun,et al.  State of charge estimation of lithium-ion batteries based on an improved parameter identification method , 2015 .

[6]  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 .

[7]  Seongjun Lee,et al.  State-of-charge and capacity estimation of lithium-ion battery using a new open-circuit voltage versus state-of-charge , 2008 .

[8]  Stephen Duncan,et al.  Lithium-ion battery thermal-electrochemical model-based state estimation using orthogonal collocation and a modified extended Kalman filter , 2015, ArXiv.

[9]  Liu Xuan,et al.  New method for parameter estimation of an electrochemical-thermal coupling model for LiCoO2 battery , 2016 .

[10]  Zonghai Chen,et al.  A new model for State-of-Charge (SOC) estimation for high-power Li-ion batteries , 2013 .

[11]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 1. Background , 2004 .

[12]  Bor Yann Liaw,et al.  Improved extended Kalman filter for state of charge estimation of battery pack , 2014 .

[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]  Ralph E. White,et al.  Extension of Physics-Based single Particle Model for Higher Charge-Discharge Rates , 2013 .

[15]  Yimin Zhou,et al.  Sequential Monte Carlo based technique for SOC estimation of LiFePO4 battery pack for electric vehicles , 2016, 2016 IEEE International Conference on Information and Automation (ICIA).

[16]  Lixin Wang,et al.  Model-based method for estimating LiCoO2 battery state of health and behaviors , 2016, 2016 IEEE International Conference on Prognostics and Health Management (ICPHM).

[17]  Wei He,et al.  State of charge estimation for electric vehicle batteries using unscented kalman filtering , 2013, Microelectron. Reliab..

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

[19]  Hongwen He,et al.  Lithium-Ion Battery Pack State of Charge and State of Energy Estimation Algorithms Using a Hardware-in-the-Loop Validation , 2017, IEEE Transactions on Power Electronics.

[20]  Simon Schwunk,et al.  Particle filter for state of charge and state of health estimation for lithium–iron phosphate batteries , 2013 .

[21]  Zhang Jian,et al.  State-of-the-art of Designs Studies for Batteries Packs of Electric Vehicles , 2016 .

[22]  Shengbo Eben Li,et al.  Combined State of Charge and State of Health estimation over lithium-ion battery cell cycle lifespan for electric vehicles , 2015 .

[23]  Rui Xiong,et al.  A data-driven based adaptive state of charge estimator of lithium-ion polymer battery used in electric vehicles , 2014 .

[24]  Limei Wang,et al.  A LiFePO4 battery pack capacity estimation approach considering in-parallel cell safety in electric vehicles , 2015 .

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

[26]  Stephen Yurkovich,et al.  Electro-thermal battery model identification for automotive applications , 2011 .