دورية أكاديمية
An Energy Management Strategy for a Super-Mild Hybrid Electric Vehicle Based on a Known Model of Reinforcement Learning
| العنوان: | An Energy Management Strategy for a Super-Mild Hybrid Electric Vehicle Based on a Known Model of Reinforcement Learning |
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| المؤلفون: | Yanli Yin, Yan Ran, Liufeng Zhang, Xiaoliang Pan, Yong Luo |
| المصدر: | Journal of Control Science and Engineering, Vol 2019 (2019) |
| بيانات النشر: | Hindawi Limited, 2019. |
| سنة النشر: | 2019 |
| المجموعة: | LCC:Engineering (General). Civil engineering (General) LCC:Electronic computers. Computer science |
| مصطلحات موضوعية: | Engineering (General). Civil engineering (General), TA1-2040, Electronic computers. Computer science, QA75.5-76.95 |
| الوصف: | For global optimal control strategy, it is not only necessary to know the driving cycle in advance but also difficult to implement online because of its large calculation volume. As an artificial intelligent-based control strategy, reinforcement learning (RL) is applied to an energy management strategy of a super-mild hybrid electric vehicle. According to time-speed datasets of sample driving cycles, a stochastic model of the driver’s power demand is developed. Based on the Markov decision process theory, a mathematical model of an RL-based energy management strategy is established, which assumes the minimum cumulative return expectation as its optimization objective. A policy iteration algorithm is adopted to obtain the optimum control policy that takes the vehicle speed, driver’s power demand, and state of charge (SOC) as the input and the engine power as the output. Using a MATLAB/Simulink platform, CYC_WVUCITY simulation model is established. The results show that, compared with dynamic programming, this method can not only adapt to random driving cycles and reduce fuel consumption of 2.4%, but also be implemented online because of its small calculation volume. |
| نوع الوثيقة: | article |
| وصف الملف: | electronic resource |
| اللغة: | English |
| تدمد: | 1687-5249 1687-5257 |
| العلاقة: | https://doaj.org/toc/1687-5249Test; https://doaj.org/toc/1687-5257Test |
| DOI: | 10.1155/2019/9259712 |
| الوصول الحر: | https://doaj.org/article/07c26739da7a4db287a732b36627da02Test |
| رقم الانضمام: | edsdoj.07c26739da7a4db287a732b36627da02 |
| قاعدة البيانات: | Directory of Open Access Journals |
Full Text Finder | تدمد: | 16875249 16875257 |
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| DOI: | 10.1155/2019/9259712 |