Gao, Z., Sun, T., and He, L., "A Multi-mode Control Strategy for EV Based on Typical Situation," SAE Technical Paper 2017-01-0438, 2017, doi:10.4271/2017-01-0438.
A multitude of recent studies are suggestive of the EV as a paramount representative of the NEV, its development direction is transformed from “individuals adapt to vehicles” to “vehicles serve for occupants”. The multi-mode drive control technology is relatively mature in traditional auto control sphere, however, a host of EV continues to use a single control strategy, which lacks of flexibility and diversity, little if nothing interprets the vehicle performances. Furthermore, due to the complex road environment and peculiarity of vehicle occupants that different requirement has been made for vehicle performance. To solve above problems, this paper uses the key technology of mathematical statistics process in MATLAB, such as the mean, linear fitting and discrete algorithms to clean up, screening and classification the original data in general rules, and based on short trips in the segments of kinematics analysis method to establish a representative of quintessential driving cycle. Therefore, it can provide us a way of extracting characteristic data in original rules. What we further do is fuzzy reasoning and intention recognition for controlling action of driver; we divided different classifications into two types: SPORT model and ECONOMIC model in order to improve the performance of vehicle and its accessories. At last, taking a simulation experiment for EV’s velocity, motor efficiency and battery SOC by DSPACE, which depends on the different opening degree of accelerator pedal, and we can summarize the difference between two strategies under typical situation. In addition, by optimizing control strategy we can make a single vehicle achieve multi-mode drive control, which improves the occupants’ sensation and receptiveness, also leads to the EV matching an optimum control of multi-mode based on typical situation.