Markov velocity predictor and radial basis function neural network-based real-time energy management strategy for plug-in hybrid electric vehicles

Hui Liu, Xunming Li, Weida Wang, Lijin Han and Changle Xiang

Energy, 2018, vol. 152, issue C, 427-444

Abstract: Power management strategy of plug-in hybrid electric vehicle for real-time application is a major challenge as the driving pattern is unknown beforehand. In this work, an innovative real-time power management strategy framework is proposed, including short horizon driving pattern prediction, driving pattern recognition, parameter off-line optimisation, parameter on-line prediction modelling, and power management strategy real-time application. A group of characteristic parameters is used to recognise driving patterns and the engine and motor working points are optimised globally by distributed genetic algorithm off-line. The optimised results approximation model is built on the basis of a radial basis function-neural network. Based on a linear programming algorithm, the higher order Markov velocity predictor is designed to obtain the short-horizon driving conditions. Combining the optimisation results approximation model, the real-time power management strategy is proposed. The on-line optimisation power management strategy comparing to the rule-based is analysed and the MATLAB/Simulink/AVL Cruise co-simulation results demonstrate that the fuel economy of real-time power management strategy improved by 16.3%, 12.7%, and 9.1% in HWFET, LA92, and Japanese urban driving patterns, respectively, which is largely higher than with a traditional rule-based strategy and slightly lower than, or approximately equal to, the global optimisation strategy.

Keywords: PHEV; Real-time power management strategy; Markov velocity predictor; Driving pattern recognition; RBF neural network; HIL (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (15)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:152:y:2018:i:c:p:427-444

DOI: 10.1016/j.energy.2018.03.148

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