State of charge estimation by multi-innovation unscented Kalman filter for vehicular applications

Abstract Vehicle to grid technology is no longer a fiction but rather a reality. Due to recent technological advances in bidirectional power transfer, EVs could serve not just as transportation tools but also as electric storage units for the grid. As a result, battery management is now more crucial than ever to control the energy transfer to and from the battery pack, while keeping all battery parameters within a safe and optimal region. In order to do so, accurate knowledge of SOC is of significant importance, since it reflects the inner state of the battery. This paper proposes, a multi-innovation theory to enhance the estimation accuracy of the unscented Kalman filter. By expanding a single innovation voltage value to multi-innovations consisting of the previous and current values of the battery's output, the accuracy of the UKF is drastically improved. All the design aspects of the proposed MI-UKF are detailed. Moreover, a hybrid Levenberg Marquardt approach is implemented for battery internal parameter identification. Finally, the experimental results indicate that the proposed MI-UKF is robust against unpredicted operational conditions, and it can enhance the accuracy of the UKF with more than 1%.

[1]  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 .

[2]  Fatima Errahimi,et al.  Comparative study of ANN/KF for on-board SOC estimation for vehicular applications , 2019, Journal of Energy Storage.

[3]  Chen Lu,et al.  A review of stochastic battery models and health management , 2017 .

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

[5]  Bin Wang,et al.  Adaptive sliding mode observers for lithium-ion battery state estimation based on parameters identified online , 2018, Energy.

[6]  Dong Li,et al.  An Online State of Charge Estimation Algorithm for Lithium-Ion Batteries Using an Improved Adaptive Cubature Kalman Filter , 2018 .

[7]  Mohd Yamani Idna Idris,et al.  Overview of model-based online state-of-charge estimation using Kalman filter family for lithium-ion batteries , 2019, Renewable and Sustainable Energy Reviews.

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

[9]  Barry W. Johnson,et al.  A battery state-of-charge indicator for electric wheelchairs , 1992, IEEE Trans. Ind. Electron..

[10]  Habibur Rehman,et al.  UAS based Li-ion battery model parameters estimation , 2017 .

[11]  Feng Ding,et al.  A multi-innovation state and parameter estimation algorithm for a state space system with d-step state-delay , 2017, Signal Process..

[12]  Pritpal Singh,et al.  Design and implementation of a fuzzy logic-based state-of-charge meter for Li-ion batteries used in portable defibrillators , 2006 .

[13]  Weixiang Shen,et al.  Sliding Mode Observer for State of Charge Estimation Based on Battery Equivalent Circuit in Electric Vehicles , 2012 .

[14]  Jun Xu,et al.  The Adaptive Fading Extended Kalman Filter SOC Estimation Method for Lithium-ion Batteries , 2018, Energy Procedia.

[15]  Hongwen He,et al.  An improved state of charge estimation method based on cubature Kalman filter for lithium-ion batteries , 2019, Applied Energy.

[16]  Matthieu Dubarry,et al.  The viability of vehicle-to-grid operations from a battery technology and policy perspective , 2018 .

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

[18]  Simon Haykin,et al.  Cubature Kalman Filtering for Continuous-Discrete Systems: Theory and Simulations , 2010, IEEE Transactions on Signal Processing.

[19]  Feng Ding,et al.  Performance analysis of multi-innovation gradient type identification methods , 2007, Autom..

[20]  Xiaoyu Li,et al.  A combined method for state-of-charge estimation for lithium-ion batteries using a long short-term memory network and an adaptive cubature Kalman filter , 2020 .

[21]  Fatima Errahimi,et al.  Battery State of Charge Estimation using An Adaptive Unscented kalman Filter for Photovoltaics Applications. , 2017 .

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

[23]  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 .

[24]  Rudolph van der Merwe,et al.  The square-root unscented Kalman filter for state and parameter-estimation , 2001, 2001 IEEE International Conference on Acoustics, Speech, and Signal Processing. Proceedings (Cat. No.01CH37221).

[25]  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 .

[26]  Ming Xin,et al.  Hypersonic entry vehicle state estimation using nonlinearity-based adaptive cubature Kalman filters , 2017 .

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

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

[29]  Song-Yul Choe,et al.  An adaptive sigma-point Kalman filter with state equality constraints for online state-of-charge estimation of a Li(NiMnCo)O2/Carbon battery using a reduced-order electrochemical model , 2020 .

[30]  Ming Liu,et al.  Estimation for state-of-charge of lithium-ion battery based on an adaptive high-degree cubature Kalman filter , 2019 .

[31]  Michael Pecht,et al.  Battery Management Systems in Electric and Hybrid Vehicles , 2011 .

[32]  Peide Liu,et al.  The Design and Implementation of Smart Battery Management System Balance Technology , 2011 .

[33]  Jian Ma,et al.  A new neural network model for the state-of-charge estimation in the battery degradation process , 2014 .

[34]  Jiong Jin,et al.  Robust Adaptive Sliding-Mode Observer Using RBF Neural Network for Lithium-Ion Battery State of Charge Estimation in Electric Vehicles , 2016, IEEE Transactions on Vehicular Technology.

[35]  Kai Zhao,et al.  Evaluation on State of Charge Estimation of Batteries With Adaptive Extended Kalman Filter by Experiment Approach , 2013, IEEE Transactions on Vehicular Technology.

[36]  Jae Wan Park,et al.  Battery state of charge estimation using a load-classifying neural network , 2016 .

[37]  Christopher D. Rahn,et al.  Model-Based Electrochemical Estimation and Constraint Management for Pulse Operation of Lithium Ion Batteries , 2010, IEEE Transactions on Control Systems Technology.

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

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

[40]  Fatima Errahimi,et al.  State of charge estimation using extended kalman filter , 2019, 2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS).

[41]  Kai Ding,et al.  Battery-Management System (BMS) and SOC Development for Electrical Vehicles , 2011, IEEE Transactions on Vehicular Technology.

[42]  Chi Nguyen Van,et al.  Soc Estimation of the Lithium-Ion Battery Pack using a Sigma Point Kalman Filter Based on a Cell’s Second Order Dynamic Model , 2020, Applied Sciences.

[43]  Chao Wang,et al.  A multi-model probability SOC fusion estimation approach using an improved adaptive unscented Kalman filter technique , 2017 .

[44]  Weiqun Liu,et al.  A state of charge estimation method for lithium-ion batteries based on fractional order adaptive extended kalman filter , 2019, Energy.

[45]  Fatima Errahimi,et al.  V2G and Wireless V2G concepts: State of the Art and Current Challenges , 2019, 2019 International Conference on Wireless Technologies, Embedded and Intelligent Systems (WITS).

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

[47]  Rajesh Kumar,et al.  State-of-charge estimation for li-ion battery using extended Kalman filter (EKF) and central difference Kalman filter (CDKF) , 2017, 2017 IEEE Industry Applications Society Annual Meeting.

[48]  Fatima Errahimi,et al.  A comparative study of kalman filtering based observer and sliding mode observer for state of charge estimation , 2018 .

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

[50]  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 .

[51]  Hongjie Wu,et al.  State of Charge Estimation Using the Extended Kalman Filter for Battery Management Systems Based on the ARX Battery Model , 2013 .

[52]  Jeffrey K. Uhlmann,et al.  New extension of the Kalman filter to nonlinear systems , 1997, Defense, Security, and Sensing.