Research on the driving control strategy for ISG hybrid electric vehicles

ABSTRACT

To overcome such Hybrid Electric Vehicle (HEV) engine problems as high fuel consumption and poor economical efficiency when working in low-efficiency areas, this paper intends to establish the ISG light hybrid vehicle structure-based mathematical models of engines, electric motors, batteries and the whole vehicle power output first; to analyse the relationship between the engine load rate and the optimal engine fuel consumption characteristics, and then to take into consideration the performance characteristics of the motor battery output efficiency, so as to come up with the switching pattern of the ISG parallel hybrid vehicle operating mode. According to the switching rules, the primary influence parameters of the engine load rate as well as the State of Charge (SOC) were extracted and a fuzzy neural network control strategy for torque management was developed. Under the Matlab/Advisor joint simulation platform, a simulation model was established and the effectiveness of the fuzzy control strategy was verified under the typical state of cyclic operation NEDC. The simulation results indicate that the adoption of fuzzy neural network control strategy for torque management can lead to considerable advancements in engine operating efficiency–the fuel economical efficiency can be improved by about 12%.

KEYWORDS:

• Hybrid electric vehicle
• torque management
• modelling and Simulation
• fuzzy neural network

Acknowledgments

The authors would like to acknowledge Project (38) Supported by 2019 Longyuan Youth Innovation and Entrepreneurship Talent Project of Gansu Provincial, Project (2019-RC-48) Supported by Lanzhou Talent Innovation and Entrepreneurship Project, Project (2019A-142) Supported by 2019 innovation ability improvement project of higher education institutions of Gansu Provincial Department of Education, Project (2018KW-04) Funded by the “Kaiwu” Innovation Team Support Project of Lanzhou Institute of Technology, and Project (2019QZ-01) Funded by the “Qizhi” Talent Cultivation Project of Lanzhou Institute of Technology.

Disclosure statement

No potential conflict of interest was reported by the authors.

Additional information

Funding

This work was supported by the 2019 Longyuan Youth Innovation and Entrepreneurship Talent Project of Gansu Provincial [38]; Lanzhou Talent Innovation and Entrepreneurship Project [2019-RC-48]; ‘Qizhi’ Talent Cultivation Project of Lanzhou Institute of Technology [2019QZ-01]; ‘Kaiwu’ Innovation Team Support Project of Lanzhou Institute of Technology [2018KW-04]; 2019 innovation ability improvement project of higher education institutions of Gansu Provincial Department of Education [2019A-142].

Notes on contributors

Xiaojun Lin

XIAOJUN LIN received the B.Tech. and M.S.degrees in Mechanical engineering from the  Beijing University of Technology, Beijing, China,in 2005 and 2007, respectively.His research interests include Automotive electronics.

Wanmin Li

WANMIN LI received the B.Tech. and M.S.degrees in vehicle engineering from the chang'an University in 2007 and 2017, respectively. His research interests include Vehicle intelligent control.

Yaping Luo

YAPING LUO received the B.Tech. and M.S.degrees in vehicle engineering from the chang'an University in 2014 and 2016, respectively. His research interests include autonomous driving.

Yan Wang

YAN WANG received the B.Tech. and M.S.degrees in vehicle engineering from the chang'an University in 2008 and 2017, respectively. His research interests include machine  control.

Yaping Zhang

YAPING ZHANG received the B.Tech. and M.S.degrees in Agricultural Machinery Engineering from the Gansu Agricultural University in 2005 and 2010, respectively. His research interests include  vehicle active safety.