Optimization-oriented adaptive equivalent consumption minimization strategy based on short-term demand power prediction for fuel cell hybrid vehicle
The fuel economy and battery charge sustaining capability are two key criteria for the energy management of a full-power fuel cell hybrid vehicle equipped with small-capacity battery pack. In order to achieve stable battery charge sustenance and near-optimal fuel consumption, this study proposes an...
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Published in | Energy (Oxford) Vol. 227; p. 120305 |
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Main Authors | , , , , , , , |
Format | Journal Article |
Language | English |
Published |
Oxford
Elsevier Ltd
15.07.2021
Elsevier BV |
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Abstract | The fuel economy and battery charge sustaining capability are two key criteria for the energy management of a full-power fuel cell hybrid vehicle equipped with small-capacity battery pack. In order to achieve stable battery charge sustenance and near-optimal fuel consumption, this study proposes an optimization-oriented adaptive equivalent consumption minimization strategy (A-ECMS) based on demand power prediction achieved via an iterative predictor. The proposed strategy updates the optimal equivalent factor periodically via local optimization process according to the predicted power to converge the state of charge (SOC) and guarantee fuel economy. The simulation results show that the iterative predictor has considerable accuracy, and the correlation between the predicted data and the real data reaches up to 0.987. The proposed strategy can quickly recover the battery SOC within 40 s in a 500-s driving cycle, which is shorter than existing feedback-oriented A-ECMS. At charge sustaining stage, the proposed strategy maintains the battery SOC around the reference value with an extremely low fluctuation degree of 0.36. The equivalent fossil-fuel consumption of the proposed strategy is 8.003 L/100 km, which is lower than that of existing A-ECMS. Besides, further investigation reveals that the proposed strategy has robust performance against the disturbance of power prediction errors.
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•An optimization-oriented predictive adaptive equivalent consumption minimization strategy is developed.•A short-term demand power predictor is designed based on iterative learning framework.•A local optimization method is proposed to update near-future optimal equivalent factor periodically.•Better battery charge sustaining capability is achieved while guaranteeing fuel economy. |
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AbstractList | The fuel economy and battery charge sustaining capability are two key criteria for the energy management of a full-power fuel cell hybrid vehicle equipped with small-capacity battery pack. In order to achieve stable battery charge sustenance and near-optimal fuel consumption, this study proposes an optimization-oriented adaptive equivalent consumption minimization strategy (A-ECMS) based on demand power prediction achieved via an iterative predictor. The proposed strategy updates the optimal equivalent factor periodically via local optimization process according to the predicted power to converge the state of charge (SOC) and guarantee fuel economy. The simulation results show that the iterative predictor has considerable accuracy, and the correlation between the predicted data and the real data reaches up to 0.987. The proposed strategy can quickly recover the battery SOC within 40 s in a 500-s driving cycle, which is shorter than existing feedback-oriented A-ECMS. At charge sustaining stage, the proposed strategy maintains the battery SOC around the reference value with an extremely low fluctuation degree of 0.36. The equivalent fossil-fuel consumption of the proposed strategy is 8.003 L/100 km, which is lower than that of existing A-ECMS. Besides, further investigation reveals that the proposed strategy has robust performance against the disturbance of power prediction errors. The fuel economy and battery charge sustaining capability are two key criteria for the energy management of a full-power fuel cell hybrid vehicle equipped with small-capacity battery pack. In order to achieve stable battery charge sustenance and near-optimal fuel consumption, this study proposes an optimization-oriented adaptive equivalent consumption minimization strategy (A-ECMS) based on demand power prediction achieved via an iterative predictor. The proposed strategy updates the optimal equivalent factor periodically via local optimization process according to the predicted power to converge the state of charge (SOC) and guarantee fuel economy. The simulation results show that the iterative predictor has considerable accuracy, and the correlation between the predicted data and the real data reaches up to 0.987. The proposed strategy can quickly recover the battery SOC within 40 s in a 500-s driving cycle, which is shorter than existing feedback-oriented A-ECMS. At charge sustaining stage, the proposed strategy maintains the battery SOC around the reference value with an extremely low fluctuation degree of 0.36. The equivalent fossil-fuel consumption of the proposed strategy is 8.003 L/100 km, which is lower than that of existing A-ECMS. Besides, further investigation reveals that the proposed strategy has robust performance against the disturbance of power prediction errors. [Display omitted] •An optimization-oriented predictive adaptive equivalent consumption minimization strategy is developed.•A short-term demand power predictor is designed based on iterative learning framework.•A local optimization method is proposed to update near-future optimal equivalent factor periodically.•Better battery charge sustaining capability is achieved while guaranteeing fuel economy. |
ArticleNumber | 120305 |
Author | Cao, Dongpu Zhang, Caizhi Hao, Dong Zhang, Yanyi Ran, Hongxu Zeng, Tao Zhu, Zhongwen Deng, Chenghao |
Author_xml | – sequence: 1 givenname: Tao surname: Zeng fullname: Zeng, Tao organization: School of Automotive Engineering, The State Key Laboratory of Mechanical Transmissions, Chongqing Automotive Collaborative Innovation Centre, Chongqing University, Chongqing, 400044, China – sequence: 2 givenname: Caizhi surname: Zhang fullname: Zhang, Caizhi email: czzhang@cqu.edu.cn organization: School of Automotive Engineering, The State Key Laboratory of Mechanical Transmissions, Chongqing Automotive Collaborative Innovation Centre, Chongqing University, Chongqing, 400044, China – sequence: 3 givenname: Yanyi surname: Zhang fullname: Zhang, Yanyi organization: China Automotive Technology and Research Center Co., Ltd., Tianjin, 300300, China – sequence: 4 givenname: Chenghao surname: Deng fullname: Deng, Chenghao organization: Propulsion Research Institute of Chongqing Changan New Energy Vehicle Technology Co., Ltd., Chongqing, 400000, China – sequence: 5 givenname: Dong surname: Hao fullname: Hao, Dong organization: China Automotive Technology and Research Center Co., Ltd., Tianjin, 300300, China – sequence: 6 givenname: Zhongwen surname: Zhu fullname: Zhu, Zhongwen organization: China Automotive Technology and Research Center Co., Ltd., Tianjin, 300300, China – sequence: 7 givenname: Hongxu surname: Ran fullname: Ran, Hongxu organization: Propulsion Research Institute of Chongqing Changan New Energy Vehicle Technology Co., Ltd., Chongqing, 400000, China – sequence: 8 givenname: Dongpu surname: Cao fullname: Cao, Dongpu email: dongpu.cao@uwaterloo.ca organization: Department of Mechanical and Mechatronics Engineering, University of Waterloo, Ontario, Canada |
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Keywords | Equivalent consumption minimization Battery charge sustaining capability Fuel cell hybrid vehicle Power prediction Energy management |
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SubjectTerms | Battery charge sustaining capability Consumption Energy efficiency Energy management Equivalence Equivalent consumption minimization Extreme values Fossil fuels Fuel cell hybrid vehicle Fuel cells Fuel consumption Fuel economy Fuel technology Hybrid vehicles Local optimization Optimization Power consumption Power prediction Predictions State of charge |
Title | Optimization-oriented adaptive equivalent consumption minimization strategy based on short-term demand power prediction for fuel cell hybrid vehicle |
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