Volume 13: Low Carbon Cities and Urban Energy Systems: Part II

K-FOLD FUZZY LEARNING FOR IMPLEMENTATION OF DYNAMIC PROGRAMMING RESULTS IN REAL-TIME ENERGY MANAGEMENT OF THE PLUG-IN HYBRID VEHICLE Quan Zhou, Ji Li, Yinglong He, Bin Shuai, Huw Williams, Hongming Xu, Yanfei Li, Fuwu Yan

Abstract

Plug-in hybrid vehicles will become a major part of urban transportation before 2030. The hybrid powertrain is a micro energy system that needs to be managed to achieve low carbon emission. Dynamic programming is widely adopted to optimize the energy efficiency, but it cannot be directly used for real-time control. This paper proposes a new Global K-fold Fuzzy Learning (GKFL) scheme to implement the offline optimization results in real-time control with adaptive neuro-fuzzy inference system (ANFIS). It aims to obtain an ANFIS network that can robustly achieve the optimum control utility which is defined as a function of the vehicle’s energy efficiency and the battery state-of-charge (SoC). The performances of the 2 ANFIS network systems developed by both standard method and the GKFL method respectively are evaluated through experimental studies. GKFL is shown effective in knowledge implementation. Compared to the default solver in the MATLAB ANFIS toolbox, GKFL can increase the control utility of the studied vehicle by 8% in the Worldwide-harmonized Light-duty Testing Cycle.

Keywords Energy management, Hybrid vehicle, Adaptive neuro-fuzzy inference system, K fold cross validation, Machine learning