  Propulsion system electrification revolution has been undergoing in the
automotive industry. The electrified propulsion system improves energy
efficiency and reduces the dependence on fossil fuel. However, the batteries of
electric vehicles experience degradation process during vehicle operation.
Research considering both battery degradation and energy consumption in
battery/ supercapacitor electric vehicles is still lacking. This study proposes
a Q-learning-based strategy to minimize battery degradation and energy
consumption. Besides Q-learning, two heuristic energy management methods are
also proposed and optimized using Particle Swarm Optimization algorithm. A
vehicle propulsion system model is first presented, where the severity factor
battery degradation model is considered and experimentally validated with the
help of Genetic Algorithm. In the results analysis, Q-learning is first
explained with the optimal policy map after learning. Then, the result from a
vehicle without ultracapacitor is used as the baseline, which is compared with
the results from the vehicle with ultracapacitor using Q-learning, and two
heuristic methods as the energy management strategies. At the learning and
validation driving cycles, the results indicate that the Q-learning strategy
slows down the battery degradation by 13-20% and increases the vehicle range by
1.5-2% compared with the baseline vehicle without ultracapacitor.
