Analysis on the Driving and Braking Control Logic Algorithm for Mobility Energy Efficiency in Electric Vehicle

This survey study analyzes the driving and braking control logic algorithms in electric vehicles (EVs) to enhance energy efficiency and speed control. It explores their applications with Smart Grids and Sustainable Energy systems, emphasizing the potential impact of integrating EVs with renewable energy technologies. Moreover, it examines recent advancements in adaptive control techniques for optimizing energy consumption in electric vehicles (EVs). It focuses on critical components like the powertrain, regenerative braking, and energy management systems. These techniques allow precise control, considering factors such as road conditions, traffic flow, and battery state of charge. Additionally, the survey study investigates the integration of EVs with Smart Grid infrastructure, highlighting the advantages of utilizing renewable energy sources for EV charging and vehicle-to-grid interactions. Furthermore, this study extensively reviews the literature and presents advanced adaptive control techniques to enhance energy efficiency and speed control in EVs. The techniques examined include model predictive control, sliding mode control, and adaptive neuro-fuzzy inference systems. The study discusses the features and limitations of each technique, providing valuable insights into their applicability and effectiveness in improving mobility energy efficiency. Additionally, our study explores the outputs of the control logic algorithms, such as optimized energy consumption, improved regenerative braking efficiency, and enhanced integration of EVs with renewable energy sources. The survey also addresses the challenges and future directions in the field, including the need for advanced data analytics, real-time optimization algorithms, and seamless integration with Smart Grids and Sustainable Energy systems.