Optimization of BEV driving range under real-world running condition

Abstract

Carbon neutral has become a global target and the adoption of Battery Electric Vehicle (BEV) has been considered as a crucial mean to reduce carbon emission. However, the development of electric vehicles currently faces significant challenges, as there exists a discrepancy between the actual mileage and energy consumption under real-world driving conditions and those obtained under standard testing conditions. The driving range under standard running condition (for instance, China Light-Duty Vehicle Test Cycle: CLTC) has been certified by authorities and reported to public by the Original Equipment Manufacturer (OEM). Therefore any adverse difference in driving range under real-world running condition could exaggerate the range anxiety of customers. In this paper, the potential of improving the driving range under real-world running condition was analyzed. At first, the Extracted Real-World Driving Cycles (ERWDC) in the city of Chongqing was proposed based on big data. This cycle was then fed into the simulation model and the corresponding energy consumption agreed well with that gained directly from the real world big-data, with a 2% deviation. Secondly, a sensitivity analysis was conducted to investigate the predominant factor to the variation of running conditions, which showed that the motor efficiency has played as the most sensitive factor (1% variation in efficiency equivalent to a 0.27 kWh/100 km variation in energy consumption.). Then an optimization strategy of motor efficiency was proposed, which could dynamically adjust the loss-minimized operation maps of a Permanent Magnet Synchronous Motor (PMSM) based on the ERWDC. The results revealed that this cycle-oriented strategy could yield a 3.04% improvement in motor efficiency under ERWDC (0.7% higher compared to CLTC), and a corresponding 5.03% improvement in driving range (1.22% lower compared to CLTC).