Title: Development of driving condition classification based adaptive optimal control strategy for PHEV

Authors: Chao Ma; Kun Yang; Lidong Miao; Meiqi Chen; Song Gao

Addresses: School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China ' School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China ' School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China ' School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China ' School of Transportation and Vehicle Engineering, Shandong University of Technology, Zibo 255000, China

Abstract: In this study, driving condition classification and recognition based adaptive optimal control strategy is developed for new type four wheel drive plug-in hybrid electric vehicle (PHEV). First, power characteristics of the proposed PHEV are analysed. The basic rule based and adaptive optimal control strategies are developed. According to the support vector machine (SVM) based classification theory, the RBF neural network kernel function is introduced and the multi classification SVM with the one-against-one method is selected. The feature parameters are then determined and extracted using real road experiment data. It is seen from the classification results that RBF kernel function based SVM has relatively high accuracy of 93.2%. Based on the developed energy management strategy library and driving cost theory, adaptive optimal control strategy is developed using Matlab/Simulink. It is found from the simulation results that the adaptive optimal control achieves the efficiency increase of 13.4%, which implies validity of the proposed adaptive optimal control strategy.

Keywords: driving condition classification; SVM; support vector machine; RBF; radical basis function; driving cost; adaptive optimal control strategy.

DOI: 10.1504/IJEHV.2019.101299

International Journal of Electric and Hybrid Vehicles, 2019 Vol.11 No.3, pp.235 - 254

Received: 09 Nov 2018
Accepted: 21 Mar 2019
Published online: 30 Jul 2019 *

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