Regenerative braking strategy for electric vehicles using improved adaptive genetic algorithm

An electric vehicle (EV) is a clear alternative compared to the conventional internal combustion engine (ICE) vehicles which are powered using fossil fuels. One of the fundamental advantages of EVs compared to conventional vehicles is the regenerative braking mechanism. Using this technique, some portion of the kinetic energy of the vehicle can be recovered during regenerative braking by using the electric drive system as a generator. Either parallel or series regenerative braking systems can be used to harvest the braking energy. Studies have shown that the amount of regenerated energy using a series (co-operative) system is more than the energy that a parallel system can harvest. A co-operative regenerative braking strategy should perform two tasks. Firstly, it distributes the brake forces between front and rear axles of the vehicle. This braking force distribution affects vehicles stability while braking. If rear wheels lock prior to the front wheels, the vehicle would be unstable and start to skid. In order to keep vehicle's stability, the required braking force should be distributed according to the Economic Commission for Europe (ECE) regulations. Secondly, it distributes the brake forces between regenerative braking and frictional braking systems. This braking force distribution affects the amount of harvested energy during braking. There are different algorithms used as a regenerative braking strategy. It is a time-consuming task to model and develop a regenerative braking strategy. Genetic Algorithm (GA) performance is flexible; it does not require many changes to be used in a different vehicle configuration. However, there are still issues remain to address such as increasing driving range and braking efficiency. Two GA based regenerative braking strategies are proposed in this study. One uses Standard Genetic Algorithm (SGA), the second strategy uses Improved Adaptive Genetic Algorithm (IAGA). IAGA is able to solve complex and none-liner problems while providing faster convergence speed. In addition, SGA is prone to be stuck in local optimums when compared to IAGA. The performance of the proposed regenerative braking strategy is evaluated using Advanced Vehicle Simulator (ADVISOR) which is created in MATLAB/Simulink environment. In order to illustrate the contrast between different regenerative braking strategies, three various strategies' performances are compared along with an EV configuration that does not utilize regenerative braking. The first regenerative braking strategy is the ADVISOR's Embedded Braking Strategy. The second strategy is based on Standard Genetic Algorithm (SGA). Lastly, the third strategy is utilizing Improved Adaptive Genetic Algorithm (IAGA). Four standard drive cycles are selected in order to evaluate each braking strategy performance under different driving conditions. 1015 drive cycle has been used in Japan to investigate on emissions and fuel economy for light duty vehicles. The Highway Fuel Economy Test (HWFET or HFET) cycle is a chassis dynamometer driving schedule developed by the US Environmental Protection Agency (US EPA) for the determination of highway fuel economy of light duty vehicles. The Urban Dynamometer Driving Schedule (UDDS) simulates an urban rote to evaluate urban fuel economy rating. Lastly, US06 drive cycle represents an aggressive, high speed and/or high acceleration driving behavior with rapid speed fluctuations. The results on these four driving cycles show a superior performance of IAGA in compare with other strategies. IAGA is able to improve braking effciency up to 90% while increasing driving range up to 25%. However, the amount of available energy to recover is depended on driver's behavior.

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