Unknown

Dataset Information

0

Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation.


ABSTRACT: To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying driving conditions. A facing-forward powertrain model is built in detail, including the engine-generator model, battery model and vehicle dynamical model. The robustness and adaptability of real-time energy management strategy are validated through the comparison with the stationary control strategy based on initial transition probability matrix (TPM) generated from a long naturalistic driving cycle in the simulation. Results indicate that proposed method has better fuel economy than stationary one and is more effective in real-time control.

SUBMITTER: Kong Z 

PROVIDER: S-EPMC5495435 | biostudies-literature | 2017

REPOSITORIES: biostudies-literature

altmetric image

Publications

Implementation of real-time energy management strategy based on reinforcement learning for hybrid electric vehicles and simulation validation.

Kong Zehui Z   Zou Yuan Y   Liu Teng T  

PloS one 20170703 7


To further improve the fuel economy of series hybrid electric tracked vehicles, a reinforcement learning (RL)-based real-time energy management strategy is developed in this paper. In order to utilize the statistical characteristics of online driving schedule effectively, a recursive algorithm for the transition probability matrix (TPM) of power-request is derived. The reinforcement learning (RL) is applied to calculate and update the control policy at regular time, adapting to the varying drivi  ...[more]

Similar Datasets

| S-EPMC6472661 | biostudies-literature
| S-EPMC6648454 | biostudies-literature
| S-EPMC5705705 | biostudies-literature
| S-EPMC4506820 | biostudies-other
| S-EPMC4509519 | biostudies-other
| S-EPMC10370702 | biostudies-literature
| S-EPMC7055740 | biostudies-literature
| S-EPMC6857476 | biostudies-literature
| S-EPMC8092385 | biostudies-literature
| S-EPMC5854388 | biostudies-literature