State of energy estimation for a series-connected lithium-ion battery pack based on an adaptive weighted strategy

Abstract Due to the inconsistency among battery cells, it is very difficult to estimate the state of energy (SOE) of a battery pack online. In this paper, an adaptive SOE estimation method for a series-connected lithium-ion battery pack based on representative cells is proposed. The dynamic characteristics of a battery are modeled by a first-order resistor-capacitor model. The key parameters and the SOEs of the representative cells are estimated by the recursive least squares algorithm and an adaptive cubature Kalman filter, respectively. The SOE of the series-connected battery pack is obtained by weighting the SOEs of the representative cells based on an adaptive strategy. Experimental results indicate that the SOE estimation result of the series-connected battery pack is close to the SOE of the “strongest” representative cell at the fully charged state, while it is close to the SOE of the “weakest” representative cell at the ending point of discharging. Even with a large initial error, the estimated SOE can quickly track the reference value. The root-mean square errors of the SOE estimation results at 25 °C, 50 °C and 0 °C are 1.3%, 2.2% and 1.7%, respectively.

[1]  Zonghai Chen,et al.  A method for the estimation of the battery pack state of charge based on in-pack cells uniformity analysis , 2014 .

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

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

[4]  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 .

[5]  Hongwen He,et al.  A Double-Scale, Particle-Filtering, Energy State Prediction Algorithm for Lithium-Ion Batteries , 2018, IEEE Transactions on Industrial Electronics.

[6]  Zonghai Chen,et al.  A method for state of energy estimation of lithium-ion batteries at dynamic currents and temperatures , 2014 .

[7]  Yujie Wang,et al.  Consistency evaluation and cluster analysis for lithium-ion battery pack in electric vehicles , 2020 .

[8]  Hongwen He,et al.  Online estimation of model parameters and state-of-charge of LiFePO4 batteries in electric vehicles , 2012 .

[9]  Wei Shi,et al.  Adaptive unscented Kalman filter based state of energy and power capability estimation approach for lithium-ion battery , 2015 .

[10]  Chunbo Zhu,et al.  A physics-based fractional order model and state of energy estimation for lithium ion batteries. Part II: Parameter identification and state of energy estimation for LiFePO 4 battery , 2017 .

[11]  Mariesa L. Crow,et al.  Battery Energy Storage System (BESS) and Battery Management System (BMS) for Grid-Scale Applications , 2014, Proceedings of the IEEE.

[12]  Yang Gao,et al.  A Copula-based battery pack consistency modeling method and its application on the energy utilization efficiency estimation , 2019 .

[13]  Daniel-Ioan Stroe,et al.  A novel charged state prediction method of the lithium ion battery packs based on the composite equivalent modeling and improved splice Kalman filtering algorithm , 2020, Journal of Power Sources.

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

[15]  Mario Huemer,et al.  Battery Internal State Estimation: A Comparative Study of Non-Linear State Estimation Algorithms , 2013, 2013 IEEE Vehicle Power and Propulsion Conference (VPPC).

[16]  S. Haykin,et al.  Cubature Kalman Filters , 2009, IEEE Transactions on Automatic Control.

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

[18]  Yunhong Che,et al.  Data-driven state of charge estimation for lithium-ion battery packs based on Gaussian process regression , 2020, Energy.

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

[20]  Hongwen He,et al.  Adaptive state of charge estimator for lithium-ion cells series battery pack in electric vehicles , 2013 .

[21]  Bo-Hyung Cho,et al.  Screening process-based modeling of the multi-cell battery string in series and parallel connections for high accuracy state-of-charge estimation , 2013 .

[22]  Hongwen He,et al.  Research on an Online Identification Algorithm for a Thevenin Battery Model by an Experimental Approach , 2015 .

[23]  Dong Li,et al.  State of charge estimation of lithium-ion batteries using an optimal adaptive gain nonlinear observer , 2017 .

[24]  Xuning Feng,et al.  Battery remaining discharge energy estimation based on prediction of future operating conditions , 2019, Journal of Energy Storage.

[25]  Haiqing Wang,et al.  State of Charge Estimation of Lithium-Ion Batteries Using an Adaptive Cubature Kalman Filter , 2015 .

[26]  Zonghai Chen,et al.  A novel approach of remaining discharge energy prediction for large format lithium-ion battery pack , 2017 .

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

[28]  Wei Sun,et al.  A modified model based state of charge estimation of power lithium-ion batteries using unscented Kalman filter , 2014 .

[29]  Zheng Chen,et al.  A Novel State of Charge Estimation Algorithm for Lithium-Ion Battery Packs of Electric Vehicles , 2016 .

[30]  Bo-Hyung Cho,et al.  Screening process of Li-Ion series battery pack for improved voltage/SOC balancing , 2010, The 2010 International Power Electronics Conference - ECCE ASIA -.

[31]  Chenbin Zhang,et al.  A method for joint estimation of state-of-charge and available energy of LiFePO4 batteries , 2014 .

[32]  Hongwen He,et al.  A systematic state-of-charge estimation framework for multi-cell battery pack in electric vehicles using bias correction technique , 2016 .

[33]  Jianqiu Li,et al.  Cell state-of-charge inconsistency estimation for LiFePO4 battery pack in hybrid electric vehicles using mean-difference model , 2013 .