Calibration efficiency improvement of rule-based energy management system for a plug-in hybrid electric vehicle

This paper presents a calibration method of a rule-based energy management strategy designed for a plug-in hybrid electric vehicle, which aims to find the optimal set of control parameters to compromise within the conflicting calibration requirements (e.g. emissions and economy). A comprehensive evaluating indicator covering emissions and economy performance is constructed by the method of radar chart. Moreover, a radial basis functions (RBFs) neural network model is proposed to establish a precise model within the control parameters and the comprehensive evaluation indicator. The best set of control parameters under offline calibration is gained by the multi-island genetic algorithm. Finally, the offline calibration results are compared with the experimental results using a chassis dynamometer. The comparison results validate the effectiveness of the proposed offline calibrating approach, which is based on the radar chart method and the RBF neural network model on vehicle performance improvement and calibrating efficiency.

[1]  J.-H. Zhou,et al.  Calibration experiments on engine emission behaviour during the stopping-and-restarting process in a hybrid electric vehicle application , 2011 .

[2]  Min Jun Song,et al.  A comparative study on the power characteristics and control strategies for plug-in hybrid electric vehicles , 2012 .

[3]  Jian Wang,et al.  Hybrid electric vehicle modeling accuracy verification and global optimal control algorithm research , 2015 .

[4]  Junmin Wang,et al.  A Parallel Hybrid Electric Vehicle Energy Management Strategy Using Stochastic Model Predictive Control With Road Grade Preview , 2015, IEEE Transactions on Control Systems Technology.

[5]  Xiaohua Zeng,et al.  Multi-objective optimization of drive gears for power split device using surrogate models , 2014 .

[6]  Dong Sun,et al.  Combined power management/design optimization for a fuel cell/battery plug-in hybrid electric vehicle using multi-objective particle swarm optimization , 2014 .

[7]  Jianqiu Li,et al.  Optimal sizing of plug-in fuel cell electric vehicles using models of vehicle performance and system cost , 2013 .

[8]  Pierluigi Pisu,et al.  Hierarchical control strategies for energy management of connected hybrid electric vehicles in urban roads , 2016 .

[9]  K Khorasani,et al.  An ensemble of dynamic neural network identifiers for fault detection and isolation of gas turbine engines , 2016, Neural Networks.

[10]  M. Jannati,et al.  Rule-based supervisory control of split-parallel hybrid electric vehicle , 2014, 2014 IEEE Conference on Energy Conversion (CENCON).

[11]  Yeong-il Park,et al.  Component sizing and engine optimal operation line analysis for a plug-in hybrid electric transit bus , 2013 .

[12]  Sarah M. Ryan,et al.  Hybrid robust and stochastic optimization for closed-loop supply chain network design using accelerated Benders decomposition , 2016, Eur. J. Oper. Res..

[13]  Sheizaf Rafaeli,et al.  Off the Radar: Comparative Evaluation of Radial Visualization Solutions for Composite Indicators , 2016, IEEE Transactions on Visualization and Computer Graphics.

[14]  Fei Hu,et al.  Development of Hybrid Electric Vehicle Control System Based on V-Cycle , 2010 .

[15]  Tomas McKelvey,et al.  Automated Engine Calibration of Hybrid Electric Vehicles , 2015, IEEE Transactions on Control Systems Technology.

[16]  Chao Ma,et al.  Design methodology of component design environment for PHEV , 2013 .

[17]  Marco Sorrentino,et al.  An integrated mathematical tool aimed at developing highly performing and cost-effective fuel cell hybrid vehicles , 2013 .

[18]  Sajjad Fekri,et al.  The design and development of multivariable controls with the application for energy management of hybrid electric vehicles , 2012 .

[19]  Tae Soo Kim,et al.  Automatic calibration of a resolver offset of permanent magnet synchronous motors for hybrid electric vehicles , 2015, 2015 American Control Conference (ACC).

[20]  Chao Cheng,et al.  Multi-objective optimization for lightweight design of twist beam suspension , 2015 .

[21]  V. T. Long,et al.  Bees-algorithm-based optimization of component size and control strategy parameters for parallel hybrid electric vehicles , 2012 .

[22]  Álvar Arnaiz-González,et al.  Using artificial neural networks for the prediction of dimensional error on inclined surfaces manufactured by ball-end milling , 2015, The International Journal of Advanced Manufacturing Technology.

[23]  Bo Geng,et al.  Energy Management Control of Microturbine-Powered Plug-In Hybrid Electric Vehicles Using the Telemetry Equivalent Consumption Minimization Strategy , 2011, IEEE Transactions on Vehicular Technology.