Real-Time Optimization of Regenerative Braking System in Electric Vehicles

Introduction

With the increasing demand of fossil fuels, there has been an increase in deterioration of environmental pollution. So, electric vehicles (EV) have been an evident solution for the problem of strident environment and depletion of the energy resources (Wu & Luo, 2017). Apart from these, there are lot more advantages like torque, efficiency. But electric vehicles are limited by its energy storage. It takes a longer duration for the energy storage to be recharged which is not feasible. So, there is a significant research in developing batteries that are compatible with electric vehicles (Grbovic et al., 2010). In order to increase the drive range of the EV, there is requirement of secondary energy storage to support the primary storage. The secondary storage is decided based on the energy density and power density. Energy density is defined as the amount of energy per unit volume whereas power density means the amount of power stored per unit volume. Batteries come with high energy density which signifies that it can supply the vehicle without any support and low power density which implies that it takes longer duration for recharging. So, ideally an energy storage with high power density and high energy density is required which is not available. Therefore, a dual layer module with high power density called supercapacitor has been designed. But due to its lower energy density, it cannot alone supply the vehicle. So, a compound energy storage system (CESS) has been designed for craving the advantages of the supercapacitor and battery (Shabarish et al., 2020). Another advantage of the vehicle is the motor has the capability to harvest the energy back to storage system which is called regenerative braking. Thereby, increasing the regenerative braking energy can effectively increase the overall efficiency and the drive range of the vehicle. Significantly, 60% of the energy during braking is utilized in the urban driving cycles. With addition of the motor braking, a composite braking system is formed which enhances the energy recovery capacity (Gao & Ehsani, 2014).

To enhance the energy harvested during regeneration, a detailed understanding of regenerative braking system is required. However, it is more complex compared to that of the conventional braking system design. A model for regenerative braking has been designed mathematically and with the use of sliding mode controller, torque is generated (Fazeli et al., 2012). A detailed investigation is carried out on the factors like state of charge (SOC) of the battery, SOC of the supercapacitor, characteristics of the battery, supercapacitor, motor of the EV, road’s adhesive coefficient which limits regenerative braking energy capability (Crolla & Cao, 2012).