A Comprehensive Review of Advanced Control Strategies for Hybrid Electric Vehicles

Abstract

This comprehensive review explores advanced control strategies for hybrid electric vehicles (HEVs), focusing on the application of artificial intelligence (AI) and machine learning (ML) techniques. The chapter traces the evolution of HEV control strategies from conventional methods to more sophisticated approaches, such as fuzzy logic, neural networks, model predictive control, and adaptive control. The integration of AI and ML, including reinforcement learning, deep learning, and hybrid AI approaches, is discussed in detail. A comparative analysis of these strategies evaluates their performance in terms of fuel efficiency, emissions reduction, computational complexity, and adaptability. Case studies highlight real-world applications and benefits. Future directions and research opportunities, including edge computing, IoT integration, and advanced sensors, are explored. This review underscores the importance of adopting advanced control strategies to optimize HEV performance, efficiency, and reliability, contributing to a sustainable and energy-efficient future.