Critical review on optimal regenerative braking control system architecture, calibration parameters and development challenges for EVs

Abstract

Summary Vehicle technology advances and the world shifts towards E‐mobility, improving the performance of electric vehicles (EVs) and HEVs, which are becoming the prominent focus. One of the prime topics of EV development is increasing the driving range, which is the fundamental requirement. Apart from increasing the battery capacity, retrieving the wasted energy during conventional braking, also called as regenerative braking, is a hot topic. In this context, numerous control architectures and major braking approaches are considered to examine in this study in order to create an efficient regenerative braking system (RBS). This review article intends to provide an overview of major subsystems in the RBS such as motor control system and hydraulic braking system and how it affects the braking performance is also well discussed. Additionally, it complies with some of the recent research applications and systematically reviews the several braking control strategies implied. The prominent ones are fuzzy logic control, neural network, MPC, sliding mode, and adaptive control modelling approaches. These control strategies are used to enhance energy regeneration without affecting vehicle performance. Further, this article discusses the RBS design process and its calibration variables, such as speed of the vehicle and brake force estimation, which can be used to improve braking performance. Moreover, challenges on RBS improvements are effectively addressed, coupled with brief suggestions and discussions for the growth of future RBS development. Finally, this article will hopefully help the reader to critically analyse the working of RBS and encourage to design of an efficient RBS for electro‐mobility application. Highlights A holistic overview of the RBS control system architecture and its various control systems is reviewed. Various brake energy control strategies like Fuzzy, MPC, NN, SMC, Adaptive and learning‐based controls are critically evaluated. The discussion of necessary calibration process and its associated parameters are elucidated. Representation of real‐time design and development process of an efficient RBS in EV. Some of the prominent challenges faced during design and development of RBS and scope of future improvement is suggested.

Design and analysis of reconfigurable sensorless brushless DC motor drive for regenerative braking and battery charging in electric vehicle

“…The integral of voltage across inductance V L must be zero for one switching cycle. The duty ratio for switch Q 4 can be derived by integrating Equations ( 11), (14), and ( 16) and the duty ratio for switch Q 6 can be derived by integrating the Equations ( 13), (15), and (17).…”

“…13 A regenerative braking system with different control schemes like neural network, adaptive control, and sliding mode control is described. 14 An electric scooter with an integrated control for braking and driving is proposed, and its performance on different road conditions is verified. 15 An analytical design of regenerative braking system for EVs is proposed without using extra power circuit.…”

“…The regenerative braking method based on line back emf, which delivers better performance than the mechanical braking, is also discussed for two‐wheeler EVs 13 . A regenerative braking system with different control schemes like neural network, adaptive control, and sliding mode control is described 14 . An electric scooter with an integrated control for braking and driving is proposed, and its performance on different road conditions is verified 15 …”