Optimization of Battery Life and State of the Charge in an Electric Motorcycle Braking System

The regenerative-friction braking system in electric motorcycles has attracted much attention in recent years due to the increase in energy demand. In this regard, recovering the vehicle energy from heat during braking is of significant importance. Without proper energy management, the battery lifetime may be negatively affected by the recurrent energy produced from braking. This paper presents a practical approach of regenerative-friction braking system for an electric motorcycle using Fuzzy Inference System (FIS) optimized via Whale Optimization Algorithm (WOA). The FIS determines the amount of the regenerative and friction braking based on different velocities and movement path slopes. The dynamic model of the electric motorcycle and the proposed controller are implemented in Matlab using the commercial Bikesim software. Moreover, the proposed method is implemented on a complete setup of an electric motorcycle to evaluate its applicability in practice. Results show that by using the proposed method, the negative effect of recurrent energy on the battery lifetime is significantly reduced with minimized risk to safety.