DRIVING CYCLES SELF-RECOGNITION OF HEV BASED ON MULTI-AGENT THEORY - Niu, Limin; Ye, Lijun; Yang, Hongyuan (Malaysian Journal of Computer Science, 2019)
In wiew of the current control strategies of Hybrid Electric Vehicle (HEV) that cannot satisfies the requirement of complex driving cycles, a driving cycles self-recognition strategy based on multi-agent theory is presented. Driving cycles recognition agent, powertrain agent, and system agent are built to form a multi-agent system. Based on the mean vehicle speed, absolute mean acceleration and standard variance of acceleration, current driving cycles are recognized by fuzzy logic controller. Powertrain adaptive matching control for current driving cycles is realized by coordination with the system agent. Simulation model is established in Advisor software. Driving cycle of the New European Driving Cycle (NEDC) and CHINA are chosen as the experiment driving cycles. The results show that driving cycles self-recognition strategy of HEV based on multi-agent theory can recognize current driving cycle accurately. Powertrain coordination matching control is realized. Vehicle performance and intellectualization are improved further.