Lithium-Plating Suppressed and Deep Deterministic Policy Gradient-Based Energy Management Strategy

Lithium plating (LP) and the formation of lithium dendrite are the main causes of battery internal short circuit and thermal runaway. This article proposes a deep reinforcement learning (DRL)-based energy management framework for power-split hybrid electric vehicles (HEVs), which effectively suppresses LP inside batteries. First, single particle model with electrolyte (SPMe) and LP model (LPM) is integrated into a deep deterministic policy gradient-based energy management strategy (DDPG-EMS) for the first time. Second, the hybrid particle swarm optimization gray wolf optimizer (HPSOGWO) algorithm is applied to the parameter identification (PI) of the two models for the first time to ensure the optimal tradeoff between convergence speed and identification accuracy. Third, the superiority of the control strategy is achieved by balancing among fuel consumption, LP occurrence, and state-of-charge (SOC) sustainability effect under different initial cell temperatures and verified by comparison with deep Q-network (DQN). The training and validation results both show that the proposed strategy with LP considered not only slows down battery degradation by redressing the charging current in time before the negative overpotential drops to zero but also reduces the fuel consumption cost as well as total cost.