Online Markov Chain-based energy management for a hybrid tracked vehicle with speedy Q-learning

Teng Liu, Bo Wang and Chenglang Yang

Energy, 2018, vol. 160, issue C, 544-555

Abstract: This brief proposes a real-time energy management approach for a hybrid tracked vehicle to adapt to different driving conditions. To characterize different route segments online, an onboard learning algorithm for Markov Chain models is employed to generate transition probability matrices of power demand. The induced matrix norm is presented as an initialization criterion to quantify differences between multiple transition probability matrices and to determine when to update them at specific road segment. Since a series of control policies are available onboard for the hybrid tracked vehicle, the induced matrix norm is also employed to choose an appropriate control policy that matches the current driving condition best. To accelerate the convergence rate in Markov Chain-based control policy computation, a reinforcement learning-enabled energy management strategy is derived by using speedy Q-learning algorithm. Simulation is carried out on two driving cycles. And results indicate that the proposed energy management strategy can greatly improve the fuel economy and be employed in real-time when compared with the stochastic dynamic programming and conventional RL approaches.

Keywords: Hybrid tracked vehicle; Markov chain; Induced matrix norm; Onboard learning algorithm; Reinforcement learning; Speedy Q-learning (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (24)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544218313173
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:160:y:2018:i:c:p:544-555

DOI: 10.1016/j.energy.2018.07.022

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().