Residual range estimation for battery electric vehicle based on radial basis function neural network

Abstract The accurate residual range estimation of a battery electric vehicle (BEV) can alleviate the driver’s range anxiety and improve driving safety. In this study, the residual range estimation is considered a nonlinear system involving battery factors and a variety of vehicle working status factors. Given that the radial basis function neural network (RBF NN) performs well in approximating nonlinear systems, this study proposes a RBF NN method to estimate the residual range of BEV. The contribution analysis method is used to simplify the input layer of RBF NN and enhance the real-time performance of estimation. Then, the residual range is estimated by RBF NN using historical data newly collected at a fixed time in current discharge process. Some experimental data are from operational BEVs in Beijing, China. Experimental results of different types demonstrate that all errors are comparatively small and within the engineering limit.

[1]  Chris Bingham,et al.  Impact of driving characteristics on electric vehicle energy consumption and range , 2012 .

[2]  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.

[3]  Bing-Gang Cao,et al.  Neural network sliding mode control based on on-line identification for electric vehicle with ultracapacitor-battery hybrid power , 2009 .

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

[5]  Jun Bi,et al.  State of charge estimation of Li-ion batteries in an electric vehicle based on a radial-basis-function neural network , 2012 .

[6]  Simon Haykin,et al.  Neural Networks and Learning Machines , 2010 .

[7]  Jorge Moreno,et al.  Energy-management system for a hybrid electric vehicle, using ultracapacitors and neural networks , 2006, IEEE Transactions on Industrial Electronics.

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

[9]  Yang Li,et al.  Technological Developments in Batteries: A Survey of Principal Roles, Types, and Management Needs , 2017, IEEE Power and Energy Magazine.

[10]  Randall Guensler,et al.  Electric vehicles: How much range is required for a day’s driving? , 2011 .

[11]  Dongpu Cao,et al.  Condition Monitoring in Advanced Battery Management Systems: Moving Horizon Estimation Using a Reduced Electrochemical Model , 2018, IEEE/ASME Transactions on Mechatronics.

[12]  M. Gevrey,et al.  Review and comparison of methods to study the contribution of variables in artificial neural network models , 2003 .

[13]  P. P. J. van den Bosch,et al.  On-line battery identification for electric driving range prediction , 2011, 2011 IEEE Vehicle Power and Propulsion Conference.

[14]  Oscar Fontenla-Romero,et al.  A review of adaptive online learning for artificial neural networks , 2016, Artificial Intelligence Review.

[15]  Shengbo Eben Li,et al.  Advanced Machine Learning Approach for Lithium-Ion Battery State Estimation in Electric Vehicles , 2016, IEEE Transactions on Transportation Electrification.

[16]  Kumpati S. Narendra,et al.  Identification and control of dynamical systems using neural networks , 1990, IEEE Trans. Neural Networks.

[17]  Thirumalai Parthiban,et al.  Exploration of artificial neural network [ANN] to predict the electrochemical characteristics of lithium-ion cells , 2007 .

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

[19]  Giovanni Pede,et al.  Techniques for estimating the residual range of an electric vehicle , 2001, IEEE Trans. Veh. Technol..

[20]  J. Douglas Faires,et al.  Numerical Analysis , 1981 .