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

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.

Automated summary

This article provides an overview of the control system architecture and various control strategies for regenerative braking system (RBS), including fuzzy logic control, neural network, MPC, sliding mode, and adaptive control modelling approaches. It also discusses the design process, calibration variables, challenges, and suggestions for the future development of RBS.