Dynamic programming solutions extracted SOC-trajectory online learning generation algorithm based approximate global optimization control strategy for a fuel cell hybrid electric vehicle
The trajectory of the battery state of charge (SOC) optimized by using dynamic programming (DP) is the global optimization solution to enhance the economy performance of the fuel cell hybrid electric vehicles under various driving cycles, however, this method requires prior knowledge of the future d...
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| Vydané v: | Energy (Oxford) Ročník 295; s. 130728 |
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| Hlavní autori: | , , , |
| Médium: | Journal Article |
| Jazyk: | English |
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Elsevier Ltd
15.05.2024
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| ISSN: | 0360-5442 |
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| Abstract | The trajectory of the battery state of charge (SOC) optimized by using dynamic programming (DP) is the global optimization solution to enhance the economy performance of the fuel cell hybrid electric vehicles under various driving cycles, however, this method requires prior knowledge of the future driving cycles. To utilize the solutions of DP, a SOC-trajectory online learning generation algorithm based approximate global optimization energy management control strategy is proposed. Initially, the global optimality of DP is used to extract the optimal SOC gradients for diverse driving scenarios. Real-time generation of optimal gradient factors for SOC trajectories is facilitated through the training of a backpropagation neural network with DP solutions. Subsequently, the deterministic rules are designed to plan SOC under actual driving conditions, with a dynamically updated threshold by the trained agents. Finally, based on the above, the optimal calculation of energy allocation is performed by combining sequence quadratic programming. Numerical verification, inclusive of hardware-in-the-loop experiments, show the effectiveness of the proposed strategy. The results demonstrate that the proposed strategy improves fuel economy by 7.39% compared to ECMS. Additionally, it reduces the cost of fuel cell life loss by 32.09% and achieves over 90% optimization of global driving cost.
•BPNN agent online learning-based approach to estimate optimal gradient factor in real-time for SOC trajectories by using DP solutions.•The constructed SOC threshold value deterministic rule enables the planning of SOC trajectory for diverse driving modes.•Combining the sequence quadratic programming algorithm enables real-time optimal solving, allowing the design of an approximate global optimal strategy.•Comparative analysis proves that the proposed strategy is effective in achieving global cost optimization. |
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| AbstractList | The trajectory of the battery state of charge (SOC) optimized by using dynamic programming (DP) is the global optimization solution to enhance the economy performance of the fuel cell hybrid electric vehicles under various driving cycles, however, this method requires prior knowledge of the future driving cycles. To utilize the solutions of DP, a SOC-trajectory online learning generation algorithm based approximate global optimization energy management control strategy is proposed. Initially, the global optimality of DP is used to extract the optimal SOC gradients for diverse driving scenarios. Real-time generation of optimal gradient factors for SOC trajectories is facilitated through the training of a backpropagation neural network with DP solutions. Subsequently, the deterministic rules are designed to plan SOC under actual driving conditions, with a dynamically updated threshold by the trained agents. Finally, based on the above, the optimal calculation of energy allocation is performed by combining sequence quadratic programming. Numerical verification, inclusive of hardware-in-the-loop experiments, show the effectiveness of the proposed strategy. The results demonstrate that the proposed strategy improves fuel economy by 7.39% compared to ECMS. Additionally, it reduces the cost of fuel cell life loss by 32.09% and achieves over 90% optimization of global driving cost.
•BPNN agent online learning-based approach to estimate optimal gradient factor in real-time for SOC trajectories by using DP solutions.•The constructed SOC threshold value deterministic rule enables the planning of SOC trajectory for diverse driving modes.•Combining the sequence quadratic programming algorithm enables real-time optimal solving, allowing the design of an approximate global optimal strategy.•Comparative analysis proves that the proposed strategy is effective in achieving global cost optimization. The trajectory of the battery state of charge (SOC) optimized by using dynamic programming (DP) is the global optimization solution to enhance the economy performance of the fuel cell hybrid electric vehicles under various driving cycles, however, this method requires prior knowledge of the future driving cycles. To utilize the solutions of DP, a SOC-trajectory online learning generation algorithm based approximate global optimization energy management control strategy is proposed. Initially, the global optimality of DP is used to extract the optimal SOC gradients for diverse driving scenarios. Real-time generation of optimal gradient factors for SOC trajectories is facilitated through the training of a backpropagation neural network with DP solutions. Subsequently, the deterministic rules are designed to plan SOC under actual driving conditions, with a dynamically updated threshold by the trained agents. Finally, based on the above, the optimal calculation of energy allocation is performed by combining sequence quadratic programming. Numerical verification, inclusive of hardware-in-the-loop experiments, show the effectiveness of the proposed strategy. The results demonstrate that the proposed strategy improves fuel economy by 7.39% compared to ECMS. Additionally, it reduces the cost of fuel cell life loss by 32.09% and achieves over 90% optimization of global driving cost. |
| ArticleNumber | 130728 |
| Author | Huang, Hao Lin, Xinyou Xie, Liping Xu, Xinhao |
| Author_xml | – sequence: 1 givenname: Xinyou orcidid: 0000-0002-4061-1055 surname: Lin fullname: Lin, Xinyou email: linxinyoou@fzu.edu.cn – sequence: 2 givenname: Hao surname: Huang fullname: Huang, Hao – sequence: 3 givenname: Xinhao surname: Xu fullname: Xu, Xinhao – sequence: 4 givenname: Liping surname: Xie fullname: Xie, Liping email: lpxie@fzu.edu.cn |
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| Cites_doi | 10.1109/TITS.2017.2729621 10.1016/j.jpowsour.2020.228798 10.1016/j.energy.2020.117224 10.1109/TCST.2015.2409235 10.1016/j.energy.2020.117499 10.1016/j.apenergy.2015.06.003 10.1109/TVT.2019.2903119 10.1016/j.apenergy.2015.01.021 10.1016/j.apenergy.2015.10.152 |
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| Keywords | Energy management strategy Back propagation neural network Dynamic programming Fuel cell electric vehicle |
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| SubjectTerms | algorithms Back propagation neural network batteries Dynamic programming energy Energy management strategy Fuel cell electric vehicle fuel cells fuels vehicles (equipment) |
| Title | Dynamic programming solutions extracted SOC-trajectory online learning generation algorithm based approximate global optimization control strategy for a fuel cell hybrid electric vehicle |
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