Deep Q-learning network based trip pattern adaptive battery longevity-conscious strategy of plug-in fuel cell hybrid electric vehicle. (1st September 2022)

• Record Type: Journal Article
• Authors: Lin, Xinyou; Xu, Xinhao; Wang, Zhaorui
• Is Part Of: Applied energy. Volume 321(2022)
• Publication Date: 2022-09-01
• Subjects: Plug-in fuel cell hybrid electric vehicle -- Energy management strategy -- Trip pattern recognition -- Battery longevity-conscious strategy; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2022.119378
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: Trip pattern recognition is using the Learning Vector Quantization Neural Network. Deep Q-learning network trip pattern adaptive battery aging-aware method is devised. Multi-criteria and trip pattern adaptive strategies are used as benchmark methods. Tests results indicate the proposed method has good fuel economy and battery aging. 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.