Parameter Identification and State of Charge Estimation of NMC Cells Based on Improved Ant Lion Optimizer

For lithium battery, which is widely utilized as energy storage system in electric vehicles (EVs), accurate estimating of the battery parameters and state of charge (SOC) has a significant effect on the prediction of energy power, the estimation of remaining mileage, and the extension of usage life. This paper develops an improved ant lion optimizer (IALO) which introduces the chaotic mapping theory into the initialization and random walk processes to improve the population homogeneity and ergodicity. After the elite (best) individual is obtained, the individual mutant operator is conducted on the elite individual to further exploit the area around elite and avoid local optimum. Then the battery model parameters are optimized by IALO algorithm. As for the SOC estimation, unscented Kalman filter (UKF) is a common algorithm for SOC estimation. However, a disadvantage of UKF is that the noise information is always unknown, and it is usually tuned manually by “trial-and-error” method which is irregular and time-consuming. In this paper, noise information is optimized by IALO algorithm. The singular value decomposition (SVD) which is utilized in the process of unscented transformation to solve the problem of the covariance matrix may lose positive definiteness. The experiment results verify that the developed IALO algorithm has superior performance of battery model parameters estimation. After the noise information is optimized by IALO, the UKF can estimate the SOC accurately and the maximum errors rate is less than 1%.

[1]  Yan Wu,et al.  Developing an electric vehicle urban driving cycle to study differences in energy consumption , 2018, Environmental Science and Pollution Research.

[2]  Pradeep Jangir,et al.  Multi-objective ant lion optimizer: a multi-objective optimization algorithm for solving engineering problems , 2016, Applied Intelligence.

[3]  Xingyuan Wang,et al.  A novel image encryption algorithm using chaos and reversible cellular automata , 2013, Commun. Nonlinear Sci. Numer. Simul..

[4]  E. Ott.  Chaos in Dynamical Systems: Contents , 2002 .

[5]  A. Kaveh,et al.  Chaotic swarming of particles: A new method for size optimization of truss structures , 2014, Adv. Eng. Softw..

[6]  Alibakhsh Kasaeian,et al.  Optimizing a hybrid wind-PV-battery system using GA-PSO and MOPSO for reducing cost and increasing reliability , 2017, Energy.

[7]  Almoataz Y. Abdelaziz,et al.  Ant Lion Optimization Algorithm for optimal location and sizing of renewable distributed generations , 2017 .

[8]  Zhiming Chen,et al.  A robust ant colony optimization for continuous functions , 2017, Expert Syst. Appl..

[9]  P. Singh,et al.  Fuzzy logic-based state-of-health determination of lead acid batteries , 2002, 24th Annual International Telecommunications Energy Conference.

[10]  Le Yi Wang,et al.  Robust and Adaptive Estimation of State of Charge for Lithium-Ion Batteries , 2015, IEEE Transactions on Industrial Electronics.

[11]  Xiaoling Huang,et al.  An efficient symmetric image encryption algorithm based on an intertwining logistic map , 2017, Neurocomputing.

[12]  Tien Q. Duong,et al.  USABC and PNGV test procedures , 2000 .

[13]  Yuan Zou,et al.  Reinforcement learning-based real-time energy management for a hybrid tracked vehicle , 2016 .

[14]  Seongjun Lee,et al.  Discrete wavelet transform-based denoising technique for advanced state-of-charge estimator of a lithium-ion battery in electric vehicles , 2015 .

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

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

[17]  Xiaosong Hu,et al.  Fuzzy Clustering Based Multi-model Support Vector Regression State of Charge Estimator for Lithium-ion Battery of Electric Vehicle , 2009, 2009 International Conference on Intelligent Human-Machine Systems and Cybernetics.

[18]  Guangjun Liu,et al.  An Adaptive Unscented Kalman Filtering Approach for Online Estimation of Model Parameters and State-of-Charge of Lithium-Ion Batteries for Autonomous Mobile Robots , 2015, IEEE Transactions on Control Systems Technology.

[19]  Seyed Mohammad Mirjalili,et al.  The Ant Lion Optimizer , 2015, Adv. Eng. Softw..

[20]  Marc A. Rosen,et al.  Optimization with a simulated annealing algorithm of a hybrid system for renewable energy including battery and hydrogen storage , 2018, Energy.

[21]  Huei Peng,et al.  A unified open-circuit-voltage model of lithium-ion batteries for state-of-charge estimation and state-of-health monitoring , 2014 .

[22]  E. Ott.  Chaos in Dynamical Systems: Contents , 1993 .

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

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

[25]  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.

[26]  Jian Ma,et al.  Development of a Representative EV Urban Driving Cycle Based on a k-Means and SVM Hybrid Clustering Algorithm , 2018, Journal of Advanced Transportation.

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

[28]  Hongwen He,et al.  Model-based state of charge and peak power capability joint estimation of lithium-ion battery in plug-in hybrid electric vehicles , 2013 .

[29]  Der-fa Chen,et al.  Design and implementation of a battery charger with a state-of-charge estimator , 2000 .

[30]  Qiang Gao,et al.  Identification of driver’s braking intention based on a hybrid model of GHMM and GGAP-RBFNN , 2018, Neural Computing and Applications.

[31]  Myoungho Sunwoo,et al.  State-of-charge estimation of lead-acid batteries using an adaptive extended Kalman filter , 2009 .

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

[33]  Hemanshu R. Pota,et al.  Modified PSO algorithm for real-time energy management in grid-connected microgrids , 2018, Renewable Energy.

[34]  Yi Lu Murphey,et al.  Battery state of charge estimation based on a combined model of Extended Kalman Filter and neural networks , 2011, The 2011 International Joint Conference on Neural Networks.

[35]  Richard E. Chase,et al.  GASOLINE VEHICLE PARTICLE SIZE DISTRIBUTIONS: COMPARISON OF STEADY STATE, FTP, AND US06 MEASUREMENTS , 1999 .

[36]  S. M. Abd-Elazim,et al.  Load frequency controller design of a two-area system composing of PV grid and thermal generator via firefly algorithm , 2016, Neural Computing and Applications.

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

[38]  Michael Pecht,et al.  State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation , 2014 .

[39]  S. M. Abd-Elazim,et al.  PI controller design for MPPT of photovoltaic system supplying SRM via BAT search algorithm , 2017, Neural Computing and Applications.

[40]  Yanqing Shen,et al.  Adaptive online state-of-charge determination based on neuro-controller and neural network , 2010 .

[41]  Jonghoon Kim,et al.  Influence of different open circuit voltage tests on state of charge online estimation for lithium-ion batteries , 2016 .

[42]  Lin Chen,et al.  State of charge estimation of lithium-ion batteries using a grey extended Kalman filter and a novel open-circuit voltage model , 2017 .

[43]  Andrew Lewis,et al.  The Whale Optimization Algorithm , 2016, Adv. Eng. Softw..

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

[45]  Zenonas Navickas,et al.  Algebraic approach for the exploration of the onset of chaos in discrete nonlinear dynamical systems , 2012 .

[46]  Wei Li,et al.  An Improved Ant Lion Optimization Algorithm and Its Application in Hydraulic Turbine Governing System Parameter Identification , 2018 .

[47]  Cao Binggang,et al.  State of charge estimation based on evolutionary neural network , 2008 .

[48]  Zhong-qiang Wu,et al.  Parameter identification of photovoltaic cell model based on improved ant lion optimizer , 2017 .

[49]  Hao Chen,et al.  NOx emission of biodiesel compared to diesel: Higher or lower? , 2018, Applied Thermal Engineering.

[50]  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 .

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

[52]  Jiuchun Jiang,et al.  Investigation of path dependence in commercial lithium-ion cells for pure electric bus applications: Aging mechanism identification , 2015 .

[53]  Han Wang,et al.  A method for SOC estimation based on simplified mechanistic model for LiFePO4 battery , 2016 .

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

[55]  Michael Pecht,et al.  State of charge estimation based on a simplified electrochemical model for a single LiCoO2 battery and battery pack , 2017 .

[56]  Xuan Zhao,et al.  Research on an equal power allocation electronic differential system for electric vehicle with dual-wheeled-motor front drive based on a wavelet controller , 2018 .

[57]  J. Hattel,et al.  Multiphysics modelling of manufacturing processes: A review , 2018 .