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Propulsion system electrification revolution has been undergoing in theautomotive industry. The electrified propulsion system improves energyefficiency and reduces the dependence on fossil fuel. However, the batteries ofelectric vehicles experience degradation process during vehicle operation.Research considering both battery degradation and energy consumption inbattery/ supercapacitor electric vehicles is still lacking. This study proposesa Q-learning-based strategy to minimize battery degradation and energyconsumption. Besides Q-learning, two heuristic energy management methods arealso proposed and optimized using Particle Swarm Optimization algorithm. Avehicle propulsion system model is first presented, where the severity factorbattery degradation model is considered and experimentally validated with thehelp of Genetic Algorithm. In the results analysis, Q-learning is firstexplained with the optimal policy map after learning. Then, the result from avehicle without ultracapacitor is used as the baseline, which is compared withthe results from the vehicle with ultracapacitor using Q-learning, and twoheuristic methods as the energy management strategies. At the learning andvalidation driving cycles, the results indicate that the Q-learning strategyslows down the battery degradation by 13-20% and increases the vehicle range by1.5-2% compared with the baseline vehicle without ultracapacitor.

Energy Consumption and Battery Aging Minimization Using a Q-learning Strategy for a Battery/Ultracapacitor Electric Vehicle