Improving regenerative braking strategy using genetic algorithm for electric vehicles / H. Taleghani ... [et al.]

• Main Authors: Taleghani, H., Hassan, M.K, Abdul Rahman, R. Z., Che Soh, A.
• Format: Article
• Language: English
• Published: Faculty of Mechanical Engineering Universiti Teknologi MARA (UiTM) 2018
• Subjects: Algorithms; TJ Mechanical engineering and machinery
• Online Access: http://ir.uitm.edu.my/id/eprint/41026/1/41026.pdf; http://ir.uitm.edu.my/id/eprint/41026/

The regenerative braking system is one of the most fundamental advantages of electric vehicles compared with internal combustion vehicles. With a proper regenerative braking strategy, a fraction of vehicle’s kinetic energy is harvested by the electric motor, which is configured as a generator during braking. The strategy distributes the required braking force between friction brakes of both axles and regenerative breaks. This study presents a genetic algorithm brake force distribution strategy to increase energy recovery, considering the Economic Commission for Europe (ECE) regulations. The performance of the proposed regenerative braking control algorithm is evaluated by the ADVISOR which is based on MATLAB/Simulink environment. The results indicate that the driving range has maximum increased to 25 percent with regards to the drive cycle.