Simulation and Analysis of the Effect of Real-World Driving Styles in an EV Battery Performance and Aging

Abstract

This paper presents the large data analysis of the real-world driving cycles and studies the effect of different driving styles on the electric vehicle (EV) battery performance and aging. For this study, a MATLAB-based software tool, real EV cycle (REV-cycle) analyzer, is developed. The real-world driving data are recorded from the I-80 highway, CA, USA. In this study, driving cycles are classified into three styles as aggressive, mild, and gentle driving based on their average acceleration. Also, two standard driving cycles (EUDC and HWFET) are simulated by the software and the results are compared. The results show that the real driving cycles are very different from the standard cycles. Also, it is observed that the traffic flow affects the driving style, as the drivers tend to drive more aggressive during the light traffic hours of the highway, while the heavy traffic limits the driver’s aggressive behavior. On the other hand, the driving style has considerable effect on the energy consumption and the battery aging. The aggressive driving style demands higher average power from the battery compared to the mild and gentle driving style. From the aging point of view, the aggressive driving style leads to higher ${ C_\text{rate}}$ demand from the battery and it expedited the capacity fade process compared to the mild and gentle driving styles.