Speed Profile Algorithm using Artificial Intelligence for Vehicle Control Unit on Quest Motors Electric Vehicles

Abstract

The throttle levers used by the majority of the electric vehicles in the market are directly connected to the inverter or motor controller. As a result, it is impossible for the rider to determine the desired or required speed profile. The available modes, such as eco, urban, and sport speed modes carried by the motor controller firmware are not very self-explanatory. A Vehicle Control Unit (VCU) is a programmable device meant to digitize the throttle lever and bridge the communication between the throttle lever and the motor controller. Here we are, Quest Motors, developing both the hardware and software of the VCU. We have developed an artificial intelligence-based algorithm that will dynamically change the riding profiles based on the road profiles, the battery conditions, and the rider’s driving style, a major upgrade from the mere static modes (eco, urban, sport). This aims not only to further improve the vehicle’s energy consumption but also to provide the optimal comfortable driving experience for the drivers. The implementation of this method can reduce the battery pack’s power consumption by up to 10% through the adjustments of the speed profile, temperature, current, and various other parameters. Last but not least, the speed profile in electric vehicles which is usually in the form of a linear curve, is no longer the case. The algorithm will continuously learn the data it receives to generate the best speed profile for the particular rider, such as an exponential curve or a logarithmic curve.