Real-time Energy Management Strategy of Hybrid Electric Vehicle: A Review

Electric Vehicles (EVs) present a sustainable alternative to reduce greenhouse gas emissions, yet challenges such as limited travel range, battery degradation, and power density hinder their widespread adoption. Hybrid Electric Vehicles (HEVs), utilizing multiple energy sources, mitigate these issues with the aid of Energy Management Strategies (EMS) to optimize power distribution. This study reviews six real-time EMS techniques: Rule-Based (RB), Fuzzy Logic Control (FLC), Equivalent Consumption Minimization Strategy (ECMS), Model Predictive Control (MPC), Neural Networks (NN), and Reinforcement Learning (RL). While NN and RL excel in energy efficiency and adaptability, they require significant computational resources, which may challenge real-time applications. Optimization-based methods like ECMS and MPC offer a balance between performance and feasibility and are not constrained by predefined cycle dependencies, making them suitable for dynamic conditions. In contrast, RB and FLC rely on predefined cycles, which limit their adaptability but provide simplicity and computational efficiency. Using both quantitative and qualitative metrics, this review identifies the strengths, limitations, and scalability of each method. The insights aim to guide researchers and practitioners toward advanced EMS solutions while addressing computational challenges and exploring emerging trends like vehicle-to-everything (V2X) integration and intelligent traffic systems(ITS) for improved energy management in HEVs.