Computation-Driven Control For Hybrid Electric Vehicles Ensuring Optimal Energy Utilization And Reduced Latency

Abstract

Hybrid Electric Vehicles (HEVs) are the next big leap towards cleaner means of transport since they act as a bridge between conventional vehicles and full-electric vehicles therefore require complex control strategies for optimal energy management, battery and vehicle durability as well as instantaneous power availability. This chapter aims for Optimal Energy Management and Latency Minimization by Intelligent Designing a Controller System which explores the computation techniques in the optimization of energy and reduction of latent impacts. The frameworks incorporate the best current methods; model predictive control and machine learning controls to periodically and in real time distribute the power between the internal combustion engine and the electrical motor. It also solves problems including the control of regenerative braking, State of Charge (SOC), temperature control, and fault detection. Particular attention is paid to minimizing computational lag to support streaming adaptation to fluctuating driving environment and other conditions to improve functionality and usability for drivers. This chapter also considers the likelihood of using renewable energy resources, accurate prediction to reduce maintenance cost for HEV components, and the development of new generation batteries to further boost the efficiency of HEVs. By presenting a number of examples and analyzing the mimicked situation, the efficiency of the proposed controller design is shown, and possible emissions and cost decrease is outlined. This work offers useful information for numerous scholars, professionals and authorities who have interest in finding new approaches to enhance the overall performance of HEVs.