Online estimation of battery equivalent circuit model parameters and state of charge using decoupled least squares technique

Battery equivalent circuit models (ECMs) are widely employed in online battery management applications. The model parameters are known to vary according to the operating conditions, such as the battery state of charge (SOC). Therefore, online recursive ECM parameter estimation is one means that may help to improve the modelling accuracy. Because a battery system consists of both fast and slow dynamics, the classical least squares (LS) method, that estimates together all the model parameters, is known to suffer from numerical problems and poor accuracy. The aim of this paper is to overcome this problem by proposing a new decoupled weighted recursive least squares (DWRLS) method, which estimates separately the parameters of the battery fast and slow dynamics. Battery SOC estimation is also achieved based on the parameter estimation results. This circumvents an additional full-order observer for SOC estimation, leading to a reduced complexity. An extensive simulation study is conducted to compare the proposed method against the LS technique. Experimental data are collected using a Li ion cell. Finally, both the simulation and experimental results have demonstrated that the proposed DWRLS approach can improve not only the modelling accuracy but also the SOC estimation performance compared with the LS algorithm.

[1]  Zhongwei Deng,et al.  State of charge estimation based on a new dual-polarization-resistance model for electric vehicles , 2017 .

[2]  Lennart Ljung,et al.  System identification (2nd ed.): theory for the user , 1999 .

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

[4]  James Marco,et al.  Modelling and experimental evaluation of parallel connected lithium ion cells for an electric vehicle battery system , 2016 .

[5]  Andrew McGordon,et al.  Design and use of multisine signals for Li-ion battery equivalent circuit modelling. Part 1: Signal design , 2016 .

[6]  M. Verbrugge,et al.  Adaptive state of charge algorithm for nickel metal hydride batteries including hysteresis phenomena , 2004 .

[7]  K. C. Divya,et al.  Battery Energy Storage Technology for power systems-An overview , 2009 .

[8]  Federico Baronti,et al.  Online Adaptive Parameter Identification and State-of-Charge Coestimation for Lithium-Polymer Battery Cells , 2014, IEEE Transactions on Industrial Electronics.

[9]  P. Young,et al.  Refined instrumental variable methods of recursive time-series analysis Part I. Single input, single output systems , 1979 .

[10]  Maria Skyllas-Kazacos,et al.  Online state of charge and model parameter co-estimation based on a novel multi-timescale estimator for vanadium redox flow battery , 2016 .

[11]  Chao Wang,et al.  A combination Kalman filter approach for State of Charge estimation of lithium-ion battery considering model uncertainty , 2016 .

[12]  Min Chen,et al.  Accurate electrical battery model capable of predicting runtime and I-V performance , 2006, IEEE Transactions on Energy Conversion.

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

[14]  Cheng Zhang,et al.  Online Battery Equivalent Circuit Model Estimation on Continuous-Time Domain Using Linear Integral Filter Method , 2017 .

[15]  Gregory L. Plett,et al.  Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs Part 2: Simultaneous state and parameter estimation , 2006 .

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

[17]  Andrew McGordon,et al.  A study of the open circuit voltage characterization technique and hysteresis assessment of lithium-ion cells , 2015 .

[18]  Mark W. Verbrugge,et al.  Adaptive, multi-parameter battery state estimator with optimized time-weighting factors , 2007 .

[19]  Andrew McGordon,et al.  Design and use of multisine signals for Li-ion battery equivalent circuit modelling. Part 2 : model estimation , 2016 .

[20]  Yue-Yun Wang,et al.  Two Time-Scaled Battery Model Identification With Application to Battery State Estimation , 2015, IEEE Transactions on Control Systems Technology.

[21]  Yanwen Li,et al.  A wavelet transform‐adaptive unscented Kalman filter approach for state of charge estimation of LiFePo4 battery , 2018 .

[22]  Peter C. Young,et al.  Direct Identification of Continuous-time Models from Sampled Data: Issues, Basic Solutions and Relevance , 2008 .

[23]  Zechang Sun,et al.  Adaptive model parameter identification for large capacity Li-ion batteries on separated time scales , 2016 .

[24]  W. D. Widanage,et al.  A Study of Cell-to-Cell Interactions and Degradation in Parallel Strings: Implications for the Battery Management System , 2016 .

[25]  Christopher M Wolverton,et al.  Electrical energy storage for transportation—approaching the limits of, and going beyond, lithium-ion batteries , 2012 .

[26]  Van-Huan Duong,et al.  Online state of charge and model parameters estimation of the LiFePO4 battery in electric vehicles using multiple adaptive forgetting factors recursive least-squares , 2015 .

[27]  Cheng Zhang,et al.  Constrained generalized predictive control of battery charging process based on a coupled thermoelectric model , 2017 .

[28]  Matthieu Dubarry,et al.  Synthesize battery degradation modes via a diagnostic and prognostic model , 2012 .

[29]  Hugues Garnier,et al.  Continuous-time model identification from sampled data: Implementation issues and performance evaluation , 2003 .

[30]  Longyun Kang,et al.  Joint Estimation of the Electric Vehicle Power Battery State of Charge Based on the Least Squares Method and the Kalman Filter Algorithm , 2016 .

[31]  Han-Pang Huang,et al.  A New State of Charge Estimation Method for LiFePO4 Battery Packs Used in Robots , 2013 .

[32]  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 .

[33]  Zonghai Chen,et al.  An on-line estimation of battery pack parameters and state-of-charge using dual filters based on pack model , 2016 .

[34]  W. D. Widanage,et al.  Online Battery Electric Circuit Model Estimation on Continuous-Time Domain Using Linear Integral Filter Method , 2017 .

[35]  Zhile Yang,et al.  Battery modelling methods for electric vehicles - A review , 2014, 2014 European Control Conference (ECC).

[36]  Maria Skyllas-Kazacos,et al.  Adaptive estimation of state of charge and capacity with online identified battery model for vanadium redox flow battery , 2016 .

[37]  Cheng Zhang,et al.  Improved Realtime State-of-Charge Estimation of LiFePO $_{\boldsymbol 4}$ Battery Based on a Novel Thermoelectric Model , 2017, IEEE Transactions on Industrial Electronics.

[38]  Jing Deng,et al.  An advanced Lithium-ion battery optimal charging strategy based on a coupled thermoelectric model , 2017 .

[39]  P. Young.  Some observations on instrumental variable methods of time-series analysis , 1976 .

[40]  Cheng Zhang,et al.  An integrated approach for real-time model-based state-of-charge estimation of lithium-ion batteries , 2015 .

[41]  Truong Q. Nguyen,et al.  Accurate Lithium-ion battery parameter estimation with continuous-time system identification methods , 2016, 2016 IEEE Energy Conversion Congress and Exposition (ECCE).

[42]  Chunbo Zhu,et al.  Capacity-loss diagnostic and life-time prediction in lithium-ion batteries: Part 1. Development of a capacity-loss diagnostic method based on open-circuit voltage analysis , 2016 .