Energy consumption estimation model for dual-motor electric vehicles based on multiple linear regression

ABSTRACT

The drive range of electric vehicle (EV) is one of the major limitations that impedes its universalism. A great deal of research has been devoted to drive range improvement of EV, an accurate and efficiency energy consumption estimation plays a crucial role in these researches. However, the majority of EV’s energy consumption estimation models are based on single motor EV, these models are not suitable for dual-motor EVs, which are composed of more complex transmission mechanisms and multiple operating modes. Thus, an energy consumption estimation model for dual-motor EV is proposed to estimate battery power. This article focuses on studying the operating modes and system efficiency in each operating mode. The limitation of working area of each mode ensures the vehicle dynamic performance, then PSO algorithm is adopted to optimize the torque (speed) distribution between two motors to improve the system efficiency in the coupled driving mode. Finally, the energy consumption estimation model is established by multiple linear regression (MLR). The result shows that the proposed model has a high precision in energy consumption estimation of dual-motor EV.

KEYWORDS:

• Energy consumption
• dual-motor electric vehicles
• PSO algorithm
• efficiency optimization
• MLR

Acknowledgments

This work has been financial supported by National Natural Science Foundation of China (Grant No. 51505086), partially supported by the Opening Foundation of Key Laboratory of Advanced Manufacture Technology for Automobile Parts, Ministry of Education [No. 2019KLMT06], partially supported by Research Project of Fuzhou University (Jinjiang) Science and Education Park Development Center (NO.2019-JJFDKY-10). And supported by Scientific and Technology Research Program of Chongqing Municipal Education Commission (Grant No. KJQN201800718).

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [51505086]; Scientific and Technology Research Program of Chongqing Municipal Education Commission [KJQN201800718]; Opening Foundation of Key Laboratory of Advanced Manufacture Technology for Automobile Parts, Ministry of Education [2019KLMT06]; Research Project of Fuzhou university (Jinjiang) Science and Education Park Development Center [2019-JJFDKY-10].