Energy transfer and utilization efficiency of regenerative braking with hybrid energy storage system

A combining sliding mode control approach for electric motor anti-lock braking system of battery electric vehicle

Conventional vehicles running on fossil fuels is one of the cause for global pollution and as a result creating adverse effects on the environment. The electric vehicle is becoming a reality and seen as the promising solution, as it offers great advantage of regenerative braking by which driving range of the vehicle is increased by harvesting braking energy. Braking control systems such as Hybrid braking system and fully Electrified Regenerative braking system are normally replacing conventional frictional braking system to stop the electric vehicles. In such systems stability, precision control and soft transition from one state to another depends on control strategies. Classical controllers have limitations which are overcome by robust and intelligent controller. In this paper implementation methodology of Electrical motors, analysis and advancements of various controllers like Sliding mode controller, H2/H∞ controller, an Adaptive controller, Fuzzy logic controller in combination with various algorithm and control techniques, Artificial neural network control and Neuro-fuzzy with PID Controllers are discussed.

A review on regenerative braking in electric vehicle

A model-free tire slip control solution for a fast, highly nonlinear Anti-lock Braking System (ABS) is proposed in this work via a reinforcement Q-learning optimal control approach. The solution is tailored around a batch neural fitted scheme using two neural networks to approximate the value function and the controller, respectively. The transition samples are collected from the process through interaction by online exploiting the current iteration controller (or policy) under an e-greedy exploration strategy. The ABS process fits this type of learning-by-interaction since it does not need an initial stabilizing controller. The validation case studies carried out on a real laboratory setup reveal that high control system performance can be achieved after several tens of interaction episodes with the controlled process. Insightful comments on the observed control behavior in a set of real-time experiments are offered along with performance comparisons with several other controllers.

Anti-lock braking systems data-driven control using Q-learning

Regenerative braking is a way to harvest electric energy from braking mechanism which usually implemented in electric vehicles. Braking strategies are required to maximize the use of regenerative braking systems. This research aims to design a regenerative braking model for a medium-sized electric bus. Measurements of latitude, longitude, elevation, and speed were firstly conducted by using GPS-based OsmAnd Android application. Transjakarta Corridor 1 (Kota-Blok M) was used for a test track with a distance of 14 km. Besides using data from measurements using GPS, WLTP (Worldwide Harmonised Light Vehicle Test Procedure) data is also used for comparison. This study produced a braking strategy model that considers aerodynamic, rolling, and grade resistances as well as electrical component specifications of the electric bus. The model design is then compared to the existing serial and parallel strategy. With the design of this system, the regenerative braking model can harvest more energy which increases the mileage of the electric bus.

https://iopscience.iop.org/article/10.1088/1742-6596/1402/4/044054/pdf

Modelling of regenerative braking system for electric bus

Regenerative braking is one of the most effective deceleration control methods in case of electric and hybrid vehicles. The properly controlled regenerative braking force of the electric motor(s) brings better braking safety, shorter braking distance, and less depreciation of the brakes if accompanied by the hydraulic braking modules. When the brake pedal is pressed, the electric motor is treated as a generator to produce negative torque. In this scenario the produced energy can be either dissipated or used to charge the batteries. In this paper, based on the registered brake strength, the braking force is calculated. Accordingly, employing a braking torque distribution strategy between the regenerative braking torque and the hydraulic braking torque, the proper braking torques are determined and the optimal slip ratio is reached by adjusting the braking torques. An anti-lock regenerative braking strategy is proposed to attain anti-slip control for negative accelerations. The performance of the proposed system is evaluated using simulations. The attained simulation results show that optimal braking characteristics are achievable for a variety of conditions using the proposed method.

S. Omid Golpayegani

Papers

Anti-lock regenerative braking torque control strategy for electric vehicle