Fuzzy optimal energy management for battery electric vehicles concerning equivalent speed

Owing to the restriction of battery energy density and technology, the energy management strategies (EMSs) of battery electric vehicles (BEVs) always play a critical role in reducing the energy consumption rate. In this article, a fuzzy optimal EMS, for BEVs, considering equivalent speed has been proposed. Three efforts have been made to realize optimal energy management of BEVs. First, the relationship between the road slope and the energy consumption has been analyzed, and an equivalent speed has been proposed by mapping the road slope into the vehicle speed. Besides, the driving cycle has been constructed based on the equivalent speed in a specific city. Second, the vehicle has been modeled, and the fuzzy logic control (FLC) has been adopted to reasonably allocate battery energy with the equivalent speed as one of the inputs. Third, the genetic algorithm (GA) has been applied to optimize the fuzzy controller-to improve the management efficiency. Simulation results demonstrate that the motor output torque has been corrected when the road slope is not zero. The reduction in the battery current fluctuations and the slow drop in battery voltage show that the fuzzy optimal strategy is beneficial to battery life. In addition, the energy consumption rate (ECR) decreases by 7.97% when compared with the FLC, which indicates that the fuzzy optimal strategy has better energy economy.

Automated summary

This article proposes a fuzzy optimal EMS for battery electric vehicles considering equivalent speed. By analyzing the relationship between road slope and energy consumption, constructing driving cycles, adopting fuzzy logic control, and optimizing the fuzzy controller with genetic algorithm, the optimal energy management of BEVs has been achieved, leading to improved battery life and better energy economy.