Regenerative Braking Control Strategy for Hybrid and Electric Vehicles Using Artificial Neural Networks

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Regenerative Braking Control Strategy for Hybrid and Electric Vehicles Using Artificial Neural Networks

Shetty, Sanketh S. / Karabasoglu, Orkun

One of the fundamental advantages of hybrid and electric vehicles compared to conventional vehicles is the regenerative braking mechanism. Some portion of the kinetic energy of the vehicle can be recovered during regenerative braking by using the electric drive system as a generator with the appropriate control strategy. The control requires distribution of the brake forces between front and rear axles of the vehicle and also between regenerative braking and frictional braking. In this paper, we propose solving the optimal brake force distribution problem using an Artificial Neural Network based methodology in order to maximize the available energy for recovery while following the rules for stability. Using the proposed approach, we find that for urban driving pattern, UDDS, up to 37 % of the total energy demand can be recovered. Then we compare the amount of recovered energy for different driving cycles and show that aggressive driving reduces recoverable energy up to 7%. An increase in the energy recovery rate directly translates into improvements in fuel economy and reductions in emissions.