Deep Q-learning network based trip pattern adaptive battery longevity-conscious strategy of plug-in fuel cell hybrid electric vehicle

Xinyou Lin, Xinhao Xu and Zhaorui Wang

Applied Energy, 2022, vol. 321, issue C, No S0306261922007206

Abstract: The driving trip pattern is of great significance in hydrogen consumption and battery Longevity of the plug-in fuel cell hybrid electric vehicles (PFCHEV). However, the traditional energy management strategy failed to consider the uncertainty of driving patterns. To overcome this drawback, a deep Q-learning network based trip pattern adaptive (DQN-TPA) battery longevity-conscious strategy is proposed in this study. To begin with, the trip pattern recognition based Learning Vector Quantization Neural Network is devised for pattern identification, and the adaptive-equivalent consumption minimizes strategy (A-ECMS) is conducted to improve the hydrogen consumption. Then, a TPA longevity-conscious strategy is developed and compared with the conventional multi-criteria (MC) optimization strategy to investigate the discrepancy brought by the pattern adaptation. And finally, in combination with the above efforts, an improved DQN-TPA based battery longevity-conscious strategy has been established accordingly. The advances are confirmed by the validation results that, the A-ECMS makes an 11.76% promotion in fuel economy by taking the deviation among different driving patterns into concern. The TPA strategy shows more adaptiveness than the MC optimization strategy in which, the effective Ah-throughput is 5.17% lower than MC-based while keeping the same economy. Further improvement can be achieved by the modified DQN-TPA based approach by remedying the imperfection of TPA-based recognition delay and performing the economy and durability conscious actions with 5.87% further reduction of effective Ah-throughput without observably sacrificing the fuel economy. Furthermore, the effectiveness and adaptiveness of the proposed strategy are validated by the Hardware-in-the-Loop experiments. Both the numerical validation and semi-physical validation results indicate that the DQN-TPA based approach made it possible to develop the battery longevity-conscious strategy capable of significantly adapting various driving patterns and improving the hydrogen consumption and battery durability performance of the PFCHEV.

Keywords: Plug-in fuel cell hybrid electric vehicle; Energy management strategy; Trip pattern recognition; Battery longevity-conscious strategy (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0306261922007206
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:appene:v:321:y:2022:i:c:s0306261922007206

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/bibliographic
http://www.elsevier. ... 405891/bibliographic

DOI: 10.1016/j.apenergy.2022.119378

Access Statistics for this article

Applied Energy is currently edited by J. Yan

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