Multi-Objective Optimization Strategy for Fuel Cell Hybrid Electric Trucks Based on Driving Patern Recognition
Fuel cell hybrid electric trucks have become a cutting-edge field in understanding urban traffic emissions due to their enormous potential in low-carbon areas. In order to improve the economy of fuel cell hybrid electric trucks and reduce the decline of fuel cell lifespan, this paper proposes a mult...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-03-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/17/6/1334 |
| _version_ | 1797241321494151168 |
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| author | Renzhi Lyu Zhenpo Wang Zhaosheng Zhang |
| author_facet | Renzhi Lyu Zhenpo Wang Zhaosheng Zhang |
| author_sort | Renzhi Lyu |
| collection | DOAJ |
| description | Fuel cell hybrid electric trucks have become a cutting-edge field in understanding urban traffic emissions due to their enormous potential in low-carbon areas. In order to improve the economy of fuel cell hybrid electric trucks and reduce the decline of fuel cell lifespan, this paper proposes a multi-objective energy management strategy that optimizes weight coefficients. On the basis of establishing a fuel cell battery hybrid system model, three modes of uniform speed, acceleration, and deceleration were identified through clustering analysis of vehicle speed. Reinforcement learning algorithms were used to learn the corresponding weights for different modes, which reduced the decline in fuel cell life while improving the economic efficiency. The simulation results indicate that, under the conditions of no load, half load, and full load, the truck only sacrificed 0.9–5.6%, 1.7–2.6%, and 1.2–1.6% SOC, saving 5.7–6.45%, 5.9–6.67%, and 6.1–6.67% in lifespan loss, and reducing hydrogen consumption by 3.0–7.1%, 2.8–4.4%, and 1.0–3.0%, respectively. |
| first_indexed | 2024-04-24T18:21:28Z |
| format | Article |
| id | doaj.art-966094d1d73e4ff68df789ad50f2c66d |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-04-24T18:21:28Z |
| publishDate | 2024-03-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-966094d1d73e4ff68df789ad50f2c66d2024-03-27T13:35:28ZengMDPI AGEnergies1996-10732024-03-01176133410.3390/en17061334Multi-Objective Optimization Strategy for Fuel Cell Hybrid Electric Trucks Based on Driving Patern RecognitionRenzhi Lyu0Zhenpo Wang1Zhaosheng Zhang2School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100811, ChinaSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing 100811, ChinaSchool of Mechanical Engineering, Beijing Institute of Technology, Beijing 100811, ChinaFuel cell hybrid electric trucks have become a cutting-edge field in understanding urban traffic emissions due to their enormous potential in low-carbon areas. In order to improve the economy of fuel cell hybrid electric trucks and reduce the decline of fuel cell lifespan, this paper proposes a multi-objective energy management strategy that optimizes weight coefficients. On the basis of establishing a fuel cell battery hybrid system model, three modes of uniform speed, acceleration, and deceleration were identified through clustering analysis of vehicle speed. Reinforcement learning algorithms were used to learn the corresponding weights for different modes, which reduced the decline in fuel cell life while improving the economic efficiency. The simulation results indicate that, under the conditions of no load, half load, and full load, the truck only sacrificed 0.9–5.6%, 1.7–2.6%, and 1.2–1.6% SOC, saving 5.7–6.45%, 5.9–6.67%, and 6.1–6.67% in lifespan loss, and reducing hydrogen consumption by 3.0–7.1%, 2.8–4.4%, and 1.0–3.0%, respectively.https://www.mdpi.com/1996-1073/17/6/1334fuel cell hybrid electric trucksenergy management strategyreinforcement learningmulti-objective optimization |
| spellingShingle | Renzhi Lyu Zhenpo Wang Zhaosheng Zhang Multi-Objective Optimization Strategy for Fuel Cell Hybrid Electric Trucks Based on Driving Patern Recognition Energies fuel cell hybrid electric trucks energy management strategy reinforcement learning multi-objective optimization |
| title | Multi-Objective Optimization Strategy for Fuel Cell Hybrid Electric Trucks Based on Driving Patern Recognition |
| title_full | Multi-Objective Optimization Strategy for Fuel Cell Hybrid Electric Trucks Based on Driving Patern Recognition |
| title_fullStr | Multi-Objective Optimization Strategy for Fuel Cell Hybrid Electric Trucks Based on Driving Patern Recognition |
| title_full_unstemmed | Multi-Objective Optimization Strategy for Fuel Cell Hybrid Electric Trucks Based on Driving Patern Recognition |
| title_short | Multi-Objective Optimization Strategy for Fuel Cell Hybrid Electric Trucks Based on Driving Patern Recognition |
| title_sort | multi objective optimization strategy for fuel cell hybrid electric trucks based on driving patern recognition |
| topic | fuel cell hybrid electric trucks energy management strategy reinforcement learning multi-objective optimization |
| url | https://www.mdpi.com/1996-1073/17/6/1334 |
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