# Transfer Deep Reinforcement Learning-enabled Energy Management Strategy for Hybrid Tracked Vehicle

arXiv: https://arxiv.org/abs/2007.08690 · HTML: https://mlsanity.com/paper/2007.08690/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2020.

## Built on by (most cited first, 3 of 3)

- [A Comparative Study of Deep Reinforcement Learning-based Transferable Energy Management Strategies for Hybrid Electric Vehicles](https://mlsanity.com/paper/2202.11514/) (2022)
- [Progress and summary of reinforcement learning on energy management of MPS-EV](https://mlsanity.com/paper/2211.04001/) (2022)
- [Sequence Aware SAC Control for Engine Fuel Consumption Optimization in Electrified Powertrain](https://mlsanity.com/paper/2508.04874/) (2025)

## Abstract (arXiv)

This paper proposes an adaptive energy management strategy for hybrid electric vehicles by combining deep reinforcement learning (DRL) and transfer learning (TL). This work aims to address the defect of DRL in tedious training time. First, an optimization control modeling of a hybrid tracked vehicle is built, wherein the elaborate powertrain components are introduced. Then, a bi-level control framework is constructed to derive the energy management strategies (EMSs). The upper-level is applying the particular deep deterministic policy gradient (DDPG) algorithms for EMS training at different speed intervals. The lower-level is employing the TL method to transform the pre-trained neural networks for a novel driving cycle. Finally, a series of experiments are executed to prove the effectiveness of the presented control framework. The optimality and adaptability of the formulated EMS are illuminated. The founded DRL and TL-enabled control policy is capable of enhancing energy efficiency and improving system performance.