Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

Article Types

Countries / Regions

Search Results (1)

Search Parameters:
Keywords = hybrid electric tracked vehicle (HETV)

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
18 pages, 783 KiB  
Article
Reinforcement Learning–Based Energy Management Strategy for a Hybrid Electric Tracked Vehicle
by Teng Liu, Yuan Zou, Dexing Liu and Fengchun Sun
Energies 2015, 8(7), 7243-7260; https://doi.org/10.3390/en8077243 - 16 Jul 2015
Cited by 94 | Viewed by 9775
Abstract
This paper presents a reinforcement learning (RL)–based energy management strategy for a hybrid electric tracked vehicle. A control-oriented model of the powertrain and vehicle dynamics is first established. According to the sample information of the experimental driving schedule, statistical characteristics at various velocities [...] Read more.
This paper presents a reinforcement learning (RL)–based energy management strategy for a hybrid electric tracked vehicle. A control-oriented model of the powertrain and vehicle dynamics is first established. According to the sample information of the experimental driving schedule, statistical characteristics at various velocities are determined by extracting the transition probability matrix of the power request. Two RL-based algorithms, namely Q-learning and Dyna algorithms, are applied to generate optimal control solutions. The two algorithms are simulated on the same driving schedule, and the simulation results are compared to clarify the merits and demerits of these algorithms. Although the Q-learning algorithm is faster (3 h) than the Dyna algorithm (7 h), its fuel consumption is 1.7% higher than that of the Dyna algorithm. Furthermore, the Dyna algorithm registers approximately the same fuel consumption as the dynamic programming–based global optimal solution. The computational cost of the Dyna algorithm is substantially lower than that of the stochastic dynamic programming. Full article
(This article belongs to the Special Issue Advances in Plug-in Hybrid Vehicles and Hybrid Vehicles)
Show Figures

Figure 1

Back to TopTop