Open AccessOptimization of extended range electric vehicle energy management strategy via driving cycle identification
Author(s) -
Yong Chen,
Zhang Yue,
Changyin Wei,
Guangxin Li,
Congcong Li
Publication year - 2020
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/793/1/012040
Subject(s) - driving cycle , powertrain , automotive engineering , energy management , driving range , fuel efficiency , range (aeronautics) , identification (biology) , energy consumption , electric vehicle , duty cycle , obstacle , engineering , computer science , energy (signal processing) , voltage , electrical engineering , torque , power (physics) , statistics , physics , botany , mathematics , quantum mechanics , biology , law , political science , thermodynamics , aerospace engineering
There are ambitious fuel consumption targets for the manufacturers of heavy-duty vehicles. For this reason, extended range electric vehicle (EREV) is a promising powertrain technology. However, the energy management strategy (EMS) is still an obstacle to the improvement of fuel economy. This paper introduces an energy management strategy for driving cycle identification. Twenty-two typical driving cycles are divided into five categories through Q-type clustering. Euclidean distance is used to identify the driving cycle. For each type of driving cycle, the energy management strategy parameters are optimized through genetic algorithms with fuel consumption and emissions as the goals. The results show that the EMS via driving cycle identification is more effective than a strategy that does not identify it. For comprehensive test cycles, the former’s fuel consumption is optimized by 6%, SOC consumption is optimized by 1%, and there is a slight improvement in emissions.