Intelligent Energy Management Mechanisms for Electric Vehicles: A Review

Authors

Aziza I. HusseinFollow

Abstract

This paper investigates intelligent methods used for energy management (EM) in Electric Vehicles (EV). The key role of EM in EVs to increase the performance of the vehicle and reduce Fuel Consumption (FC) thus producing less greenhouse effect. However, the used tactics had limitations. An introduced model of Intelligent Energy Management System (IEMS) for Plug-in hybrid electric vehicles (PHEVs) was efficient for FC reduction. Whereas for IEMS Based on Kalman Filtering showed a low percentage of error. While EM using Tags Threshold Admission And Greedy Scheduling improves EV’s performance, but it can only manage energy per one EV. Another model of IEMS for hybrid electric vehicles HEV had high reliability since it was performed using various models. Additionally, EMS based on global optimization has reduced FC through Linear programming and Dynamic Programming. Moreover, Particle Swarm Optimization gave less FC by 10.26\% when tested and compared to Torque Efficiency Optimization. However, Artificial Neural Networks (ANN) produced low error since it’s self-learning. ANN works better than Coulomb Counting and Extended Kalman Filter. EM using deep learning with ANN model is useful; it can save a percentage of energy each time it’s tested. Furthermore, Simulation outputs of Deep Reinforcement Learning based EMS had better performance than the rule-based EMS in FC.

Recommended Citation

Hussein, Aziza I. (2022) "Intelligent Energy Management Mechanisms for Electric Vehicles: A Review," Effat Undergraduate Research Journal: Vol. 3: Iss. 1, Article 6.
Available at: https://digitalcommons.aaru.edu.jo/eurj/vol3/iss1/6