Study on power matching strategy of PHEV based on fuzzy recognition of driving cycles

Abstract

In order to overcome adaptation deficiency of PHEV power assembly controlling strategy in complicated driving cycle, adaptive matching controlling strategy of driving-cycle fuzzy recognition was presented. The driving cycles of automobiles were classified in 3 types: rural cycle (RC), urban cycle (UC) and expressway cycle (EC). Based on information of automobile speed and acceleration, environment cycle is recognized via fuzzy inference, on those basis, adaptation of power matching model was realized. Moreover, simulation of power matching adaptive controlling strategy designed in this paper was conducted in ADVISOR. The designed controlling strategy and the electric-assisted controlling strategy were compared and analyzed. The results indicated that compared with electric-assisted controlling strategy, power-matching adaptive strategy of PHEV based on driving-cycle fuzzy recognition could further reduce energy consumption and exhaust emission, which proved that the newly designed controlling strategy could adapt driving cycles and enhance intellectualization of full vehicle.

Support vector machine–based driving cycle recognition for dynamic equivalent fuel consumption minimization strategy with hybrid electric vehicle

“…Simulation results indicate that DCR with fuzzy logic controller can make better fuel efficiency than the original and conventional dynamic programming-based control strategies 21 ; L Niu et al presented an adaptive power matching controlling strategy of driving cycle fuzzy recognition. The simulation based on ADVISOR software shows that the newly designed controlling strategy with DCR could adapt driving cycles and enhance intellectualization of full vehicle 22 ; Y Tian et al 23 recognize the arterial road and secondary main road of Guangzhou and Shanghai in China by optimized fuzzy controller; third, applying clustering theory in DCR: Z Lei et al confirmed current driving cycle type by computing the Euclid distance of the characteristic parameters between standard driving cycle and current driving cycle. The simulation results show that compared with the original rule-based EMS, DCR can reduce the fuel consumption by 11.68% 24 ; J Wang et al 25 also adopted DCR by computing the Euclid distance and used in equivalent consumption minimization strategy (ECMS) so as to realize an optimization of fuel economy; S Zhan et al 10 applied K-means clustering method, which is optimized by genetic algorithm to conduct DCR in order to adjust the equivalent fuel consumption factor in realtime control.…”