Energy consumption and battery aging minimization using a Q-learning strategy for a battery/ultracapacitor electric vehicle

Highlights: • Q-learning is proposed to actively determines the engagement of ultracapacitor. • Two heuristic strategies are proposed and optimized by Particle Swarm Optimization. • Battery aging model identification is described and conducted using Genetic Algorithm. • Results from Q-learning are extensively analyzed and explained. • Q-learning reduces battery degradation by 20% and extends vehicle range by 2%. 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/ultracapacitor electric vehicles is still lacking. This study proposes a Q-learning-based strategy to minimize battery degradation and energy consumption. Besides Q-learning, two rule-based 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 rule-based 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.