Probability based remaining capacity estimation using data-driven and neural network model

Abstract Since large numbers of lithium-ion batteries are composed in pack and the batteries are complex electrochemical devices, their monitoring and safety concerns are key issues for the applications of battery technology. An accurate estimation of battery remaining capacity is crucial for optimization of the vehicle control, preventing battery from over-charging and over-discharging and ensuring the safety during its service life. The remaining capacity estimation of a battery includes the estimation of state-of-charge (SOC) and state-of-energy (SOE). In this work, a probability based adaptive estimator is presented to obtain accurate and reliable estimation results for both SOC and SOE. For the SOC estimation, an n ordered RC equivalent circuit model is employed by combining an electrochemical model to obtain more accurate voltage prediction results. For the SOE estimation, a sliding window neural network model is proposed to investigate the relationship between the terminal voltage and the model inputs. To verify the accuracy and robustness of the proposed model and estimation algorithm, experiments under different dynamic operation current profiles are performed on the commercial 1665130-type lithium-ion batteries. The results illustrate that accurate and robust estimation can be obtained by the proposed method.

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

[2]  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 .

[3]  Wei He,et al.  State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures , 2014 .

[4]  Gregory L. Plett,et al.  Sigma-point Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 1: Introduction and state estimation , 2006 .

[5]  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.

[6]  Ching Chuen Chan,et al.  Adaptive neuro-fuzzy modeling of battery residual capacity for electric vehicles , 2002, IEEE Trans. Ind. Electron..

[7]  C. Mi,et al.  A robust state-of-charge estimator for multiple types of lithium-ion batteries using adaptive extended Kalman filter , 2013 .

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

[9]  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 .

[10]  Zhenwei Cao,et al.  A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles , 2014 .

[11]  C. Moo,et al.  Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries , 2009 .

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

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

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

[15]  Xiaosong Hu,et al.  Estimation of State of Charge of a Lithium-Ion Battery Pack for Electric Vehicles Using an Adaptive Luenberger Observer , 2010 .

[16]  K. T. Chau,et al.  A new battery capacity indicator for lithium-ion battery powered electric vehicles using adaptive neuro-fuzzy inference system , 2004 .

[17]  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 .

[18]  Yuang-Shung Lee,et al.  A Merged Fuzzy Neural Network and Its Applications in Battery State-of-Charge Estimation , 2007, IEEE Transactions on Energy Conversion.

[19]  Andreas Jossen,et al.  Methods for state-of-charge determination and their applications , 2001 .

[20]  Guangzhong Dong,et al.  Online state of charge estimation and open circuit voltage hysteresis modeling of LiFePO4 battery using invariant imbedding method , 2016 .

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

[22]  Zonghai Chen,et al.  A method for state-of-charge estimation of LiFePO4 batteries based on a dual-circuit state observer , 2015 .

[23]  Hongwen He,et al.  State-of-Charge Estimation of the Lithium-Ion Battery Using an Adaptive Extended Kalman Filter Based on an Improved Thevenin Model , 2011, IEEE Transactions on Vehicular Technology.

[24]  Mohammad Farrokhi,et al.  State-of-Charge Estimation for Lithium-Ion Batteries Using Neural Networks and EKF , 2010, IEEE Transactions on Industrial Electronics.

[25]  Long Xu,et al.  Kalman filtering state of charge estimation for battery management system based on a stochastic fuzzy neural network battery model , 2012 .

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

[27]  Yuanyuan Liu,et al.  Adaptive State of Charge Estimation for Li-Ion Batteries Based on an Unscented Kalman Filter with an Enhanced Battery Model , 2013 .

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

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

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

[31]  Il-Song Kim,et al.  Nonlinear State of Charge Estimator for Hybrid Electric Vehicle Battery , 2008, IEEE Transactions on Power Electronics.

[32]  Guangzhong Dong,et al.  A method for state of energy estimation of lithium-ion batteries based on neural network model , 2015 .

[33]  Chenbin Zhang,et al.  A method for state-of-charge estimation of Li-ion batteries based on multi-model switching strategy , 2015 .

[34]  David A. Stone,et al.  Nonlinear observers for predicting state-of-charge and state-of-health of lead-acid batteries for hybrid-electric vehicles , 2005, IEEE Transactions on Vehicular Technology.

[35]  D. T. Lee,et al.  State-of-Charge Estimation for Electric Scooters by Using Learning Mechanisms , 2007, IEEE Transactions on Vehicular Technology.

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

[37]  Y. Bultel,et al.  Definition of a State-of-Energy Indicator (SoE) for Electrochemical Storage Devices: Application for Energetic Availability Forecasting , 2012 .

[38]  Hongwen He,et al.  Comparison study on the battery models used for the energy management of batteries in electric vehicles , 2012 .

[39]  Il-Song Kim,et al.  The novel state of charge estimation method for lithium battery using sliding mode observer , 2006 .

[40]  Hossein Gholizade-Narm,et al.  Lithium-ion battery state of charge estimation based on square-root unscented Kalman filter , 2013 .

[41]  Gregory L. Plett,et al.  Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 2. Modeling and identification , 2004 .

[42]  Hongwen He,et al.  A novel Gaussian model based battery state estimation approach: State-of-Energy , 2015 .

[43]  Caiping Zhang,et al.  Fundamentals and Applications of Lithium-ion Batteries in Electric Drive Vehicles , 2015 .