Computational Intelligent Techniques in Mechatronics

11Study on Integrated Neural Networks and Fuzzy Logic Control for Autonomous Electric Vehicles

S. Boopathi

Department of Mechanical Engineering, Muthayammal Engineering College, Namakkal, Tamilnadu, India

Abstract

As the automotive industry undergoes a paradigm shift toward autonomous electric vehicles (AEVs), the development of advanced control systems becomes crucial to ensure safe, efficient, and reliable operation. This chapter delves into the convergence of neural networks and fuzzy logic control techniques in the context of AEVs. Neural networks, with their ability to learn complex patterns from data, and fuzzy logic, which excels in handling imprecise information, offer promising solutions for addressing the challenges associated with autonomy and energy efficiency in AEVs. This chapter explores the fundamentals of neural networks and fuzzy logic, discusses the specific control requirements of AEVs, and provides insights into the integration of these techniques to enhance autonomy. Case studies showcasing real-world applications of these advancements in AEVs further illustrate their impact on urban mobility, energy management, and fleet operations. Lastly, the chapter delves into future prospects, challenges, and emerging trends, underscoring the potential of neural networks and fuzzy logic to shape the future of autonomous electric transportation.

Keywords: Sensing, path, energy management, adaptive cruise control, regenerative braking, hybrid control, fleet management, ...