A Novel Energy Management Strategy for Series Hybrid Electric Rescue Vehicle

Abstract

The performance and fuel consumption of hybrid electric vehicle heavily depend on the EMS (energy management strategy). This paper presents a novel EMS for a series hybrid electric rescue vehicle. Firstly, considering the working characteristics of engine and battery, the EMS combining logic threshold and fuzzy control is proposed. Secondly, a fuzzy control optimization method based on IQGA (improved quantum genetic algorithm) is designed to achieve better fuel efficiency. Then, the modeling and simulation are completed by using MATLAB/Simulink; the results demonstrate that the fuel consumption can be decreased by 5.17% after IQGA optimization and that the optimization effect of IQGA is better than that of GA (genetic algorithm) and QGA (quantum genetic algorithm). Finally, the HILS (hardware in loop simulation) platform is constructed with dSPACE; the HILS experiment shows that the proposed EMS can effectively improve the vehicle working efficiency, which can be applied to practical application.

Hybrid Sources Powered Electric Vehicle Configuration and Integrated Optimal Power Management Strategy

“…This is due to the fact that electric automobiles can create high torque right away, whereas combustion engines must acquire speed to achieve that torque. So that vehicle transmuting in electric vehicles, have an alternate solution [63]. Ever since Lohner Porshe, developed the first HEV in 1901.…”

“…Bathaee et al [61] proposed an FL-based torque controller [62] and optimize the energy flow, generation, and conversion in the individual component [56] of the parallel hybrid vehicle. Li et al [63] proposed strategy combined the logic threshold and fuzzy control in which fuzzy control design based on Improve Quantum Genetic Algorithm (IQGA) optimization technique to improve the fuel efficiency. Fuel consumption is decreased by 5.17 % by applying IQGA which gives a better response than GA and QGA.…”

“…Adaptive [58], [54], [59], [60], [61], [62] , [63], [64], [65], [66], [67] Predictive [54], [53], [67], [68], [69], [70], [71], [72], [73], [74], [75]…”