An Energy Management Strategy for a Super-Mild Hybrid Electric Vehicle Based on a Known Model of Reinforcement Learning
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 strat...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2019-01-01
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| Series: | Journal of Control Science and Engineering |
| Online Access: | http://dx.doi.org/10.1155/2019/9259712 |
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| _version_ | 1849305283111682048 |
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| author | Yanli Yin Yan Ran Liufeng Zhang Xiaoliang Pan Yong Luo |
| author_facet | Yanli Yin Yan Ran Liufeng Zhang Xiaoliang Pan Yong Luo |
| author_sort | Yanli Yin |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-07c26739da7a4db287a732b36627da02 |
| institution | Kabale University |
| issn | 1687-5249 1687-5257 |
| language | English |
| publishDate | 2019-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Control Science and Engineering |
| spelling | doaj-art-07c26739da7a4db287a732b36627da022025-08-20T03:55:28ZengWileyJournal of Control Science and Engineering1687-52491687-52572019-01-01201910.1155/2019/92597129259712An Energy Management Strategy for a Super-Mild Hybrid Electric Vehicle Based on a Known Model of Reinforcement LearningYanli Yin0Yan Ran1Liufeng Zhang2Xiaoliang Pan3Yong Luo4School of Mechatronics & Automobile Engineering, Chongqing Jiao Tong University, Chongqing 400054, ChinaSchool of Mechatronics & Automobile Engineering, Chongqing Jiao Tong University, Chongqing 400054, ChinaSchool of Mechatronics & Automobile Engineering, Chongqing Jiao Tong University, Chongqing 400054, ChinaChongqing Changan Automobile Stock Co., Ltd., Chongqing 400054, ChinaKey Laboratory of Advanced Manufacturing Technology for Automobile Parts, Ministry of Education, Chongqing University of Technology, Chongqing 400054, ChinaFor 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.http://dx.doi.org/10.1155/2019/9259712 |
| spellingShingle | Yanli Yin Yan Ran Liufeng Zhang Xiaoliang Pan Yong Luo An Energy Management Strategy for a Super-Mild Hybrid Electric Vehicle Based on a Known Model of Reinforcement Learning Journal of Control Science and Engineering |
| title | An Energy Management Strategy for a Super-Mild Hybrid Electric Vehicle Based on a Known Model of Reinforcement Learning |
| title_full | An Energy Management Strategy for a Super-Mild Hybrid Electric Vehicle Based on a Known Model of Reinforcement Learning |
| title_fullStr | An Energy Management Strategy for a Super-Mild Hybrid Electric Vehicle Based on a Known Model of Reinforcement Learning |
| title_full_unstemmed | An Energy Management Strategy for a Super-Mild Hybrid Electric Vehicle Based on a Known Model of Reinforcement Learning |
| title_short | An Energy Management Strategy for a Super-Mild Hybrid Electric Vehicle Based on a Known Model of Reinforcement Learning |
| title_sort | energy management strategy for a super mild hybrid electric vehicle based on a known model of reinforcement learning |
| url | http://dx.doi.org/10.1155/2019/9259712 |
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