Adversarial imitation reinforcement learning is proposed for power allocation. Establishing the expert knowledge by offline optimization. Mitigate ineffective exploration, accelerate training, and enhance reward.
Does deep reinforcement learning reduce battery capacity loss?
However, deep reinforcement learning relies on a large amount of trial-and-error training to acquire near-optimal performance. An adversarial imitation reinforcement learning energy management strategy is proposed for electric vehicles with hybrid energy storage system to minimize the cost of battery capacity loss.
Is a reinforcement learning energy management strategy possible for electric vehicles?
To bridge the aforementioned research gap, an adversarial imitation reinforcement learning energy management strategy is proposed for electric vehicles with HESS to minimize the cost of battery capacity loss and energy loss, which combines GAIL and deep reinforcement learning.
Why do we need mechanical reinforcement for structural batteries?
Mechanical properties of batteries are often 2–3 orders of magnitude lower than load-bearing structural components for aircraft or ground transportation . Hence, to develop structural batteries, strategies for mechanical reinforcement are required.
Can reinforcement learning optimize the charging process of a Dicke battery?
However, the chaotic nature of the model severely hinders the extractable energy (ergotropy). Here, we use reinforcement learning to optimize the charging process of a Dicke battery either by modulating the coupling strength, or the system-cavity detuning.
Can adversarial imitation reinforcement learning reduce battery capacity loss and energy loss?
Conclusion An adversarial imitation reinforcement learning-based energy management strategy for lithium-ion battery/supercapacitor electric vehicles is proposed in this paper to minimize the battery capacity loss and energy loss cost.
What is adversarial imitation reinforcement learning based energy management?
1. An adversarial imitation reinforcement learning-based energy management framework is first proposed for electric vehicles with HESS, which effectively integrates generative adversarial imitation learning and deep reinforcement learning, improving the training effectiveness and robustness in stochastic unknown driving conditions.