# Transferred Energy Management Strategies for Hybrid Electric Vehicles Based on Driving Conditions Recognition

arXiv: https://arxiv.org/abs/2007.08337 · HTML: https://mlsanity.com/paper/2007.08337/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Jul 2020.

## Built on by (most cited first, 1 of 1)

- [Progress and summary of reinforcement learning on energy management of MPS-EV](https://mlsanity.com/paper/2211.04001/) (2022)

## Abstract (arXiv)

Energy management strategies (EMSs) are the most significant components in hybrid electric vehicles (HEVs) because they decide the potential of energy conservation and emission reduction. This work presents a transferred EMS for a parallel HEV via combining the reinforcement learning method and driving conditions recognition. First, the Markov decision process (MDP) and the transition probability matrix are utilized to differentiate the driving conditions. Then, reinforcement learning algorithms are formulated to achieve power split controls, in which Q-tables are tuned by current driving situations. Finally, the proposed transferred framework is estimated and validated in a parallel hybrid topology. Its advantages in computational efficiency and fuel economy are summarized and proved.