Xiao Yu , Cheng Lin , Peng Xie , Yu Tian , Haopeng Chen , Kai Liu , Huimin Liu
{"title":"低温区域燃料电池混合动力电动汽车的电热协同控制和多模式能量流分析","authors":"Xiao Yu , Cheng Lin , Peng Xie , Yu Tian , Haopeng Chen , Kai Liu , Huimin Liu","doi":"10.1016/j.etran.2024.100341","DOIUrl":null,"url":null,"abstract":"<div><p>The energy flow distribution characteristics of electric vehicles operating in various propulsion modes and all climatic scenarios have not been thoroughly explored. To achieve effective electric-thermal collaborative energy management, intelligent control methods must be applied considering various climatic conditions to alleviate mileage anxiety. In this study, we developed a novel electric–thermal collaborative energy management strategy based on an improved deep neural network and energy quantification model to increase the global energy conversion efficiency. The complete energy consumption distribution characteristics are summarized under various strategies and propulsion modes based on an experiment data collected by the vehicle control unit that involves battery self-heating, cabin heating, acceleration consumption, and fuel consumption in the temperature range of −10°C-35 °C. Our findings indicate that, for a fuel cell hybrid bus in the cycle including the initial cabin heating process, the heating consumption in the pure electric mode was 9.9 kWh/cycle and 13 kWh/cycle when the ambient temperature is −2 °C and −10 °C, respectively, accounting for 33 % and 42 % of the total consumption, respectively. After using the waste heat from the fuel cell, the consumption of electric heating under the same conditions is only 3.7 kWh/cycle. In the high-temperature scenario, the cabin cooling consumption is 3.26 kWh/cycle, accounting for only 18 % of the total energy consumption. Finally, in low-temperature scenarios, the electric–thermal collaborative strategy reduced the cost by 14.7 % and 9.2 % in the pure electric and hybrid modes, respectively. Thus, our approach significantly improves energy utilization and conversion efficiency, especially at low temperatures.</p></div>","PeriodicalId":36355,"journal":{"name":"Etransportation","volume":null,"pages":null},"PeriodicalIF":15.0000,"publicationDate":"2024-05-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Electric-thermal collaborative control and multimode energy flow analysis of fuel cell hybrid electric vehicles in low-temperature regions\",\"authors\":\"Xiao Yu , Cheng Lin , Peng Xie , Yu Tian , Haopeng Chen , Kai Liu , Huimin Liu\",\"doi\":\"10.1016/j.etran.2024.100341\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The energy flow distribution characteristics of electric vehicles operating in various propulsion modes and all climatic scenarios have not been thoroughly explored. To achieve effective electric-thermal collaborative energy management, intelligent control methods must be applied considering various climatic conditions to alleviate mileage anxiety. In this study, we developed a novel electric–thermal collaborative energy management strategy based on an improved deep neural network and energy quantification model to increase the global energy conversion efficiency. The complete energy consumption distribution characteristics are summarized under various strategies and propulsion modes based on an experiment data collected by the vehicle control unit that involves battery self-heating, cabin heating, acceleration consumption, and fuel consumption in the temperature range of −10°C-35 °C. Our findings indicate that, for a fuel cell hybrid bus in the cycle including the initial cabin heating process, the heating consumption in the pure electric mode was 9.9 kWh/cycle and 13 kWh/cycle when the ambient temperature is −2 °C and −10 °C, respectively, accounting for 33 % and 42 % of the total consumption, respectively. After using the waste heat from the fuel cell, the consumption of electric heating under the same conditions is only 3.7 kWh/cycle. In the high-temperature scenario, the cabin cooling consumption is 3.26 kWh/cycle, accounting for only 18 % of the total energy consumption. Finally, in low-temperature scenarios, the electric–thermal collaborative strategy reduced the cost by 14.7 % and 9.2 % in the pure electric and hybrid modes, respectively. Thus, our approach significantly improves energy utilization and conversion efficiency, especially at low temperatures.</p></div>\",\"PeriodicalId\":36355,\"journal\":{\"name\":\"Etransportation\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":15.0000,\"publicationDate\":\"2024-05-28\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Etransportation\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2590116824000316\",\"RegionNum\":1,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"ENERGY & FUELS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Etransportation","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2590116824000316","RegionNum":1,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
Electric-thermal collaborative control and multimode energy flow analysis of fuel cell hybrid electric vehicles in low-temperature regions
The energy flow distribution characteristics of electric vehicles operating in various propulsion modes and all climatic scenarios have not been thoroughly explored. To achieve effective electric-thermal collaborative energy management, intelligent control methods must be applied considering various climatic conditions to alleviate mileage anxiety. In this study, we developed a novel electric–thermal collaborative energy management strategy based on an improved deep neural network and energy quantification model to increase the global energy conversion efficiency. The complete energy consumption distribution characteristics are summarized under various strategies and propulsion modes based on an experiment data collected by the vehicle control unit that involves battery self-heating, cabin heating, acceleration consumption, and fuel consumption in the temperature range of −10°C-35 °C. Our findings indicate that, for a fuel cell hybrid bus in the cycle including the initial cabin heating process, the heating consumption in the pure electric mode was 9.9 kWh/cycle and 13 kWh/cycle when the ambient temperature is −2 °C and −10 °C, respectively, accounting for 33 % and 42 % of the total consumption, respectively. After using the waste heat from the fuel cell, the consumption of electric heating under the same conditions is only 3.7 kWh/cycle. In the high-temperature scenario, the cabin cooling consumption is 3.26 kWh/cycle, accounting for only 18 % of the total energy consumption. Finally, in low-temperature scenarios, the electric–thermal collaborative strategy reduced the cost by 14.7 % and 9.2 % in the pure electric and hybrid modes, respectively. Thus, our approach significantly improves energy utilization and conversion efficiency, especially at low temperatures.
期刊介绍:
eTransportation is a scholarly journal that aims to advance knowledge in the field of electric transportation. It focuses on all modes of transportation that utilize electricity as their primary source of energy, including electric vehicles, trains, ships, and aircraft. The journal covers all stages of research, development, and testing of new technologies, systems, and devices related to electrical transportation.
The journal welcomes the use of simulation and analysis tools at the system, transport, or device level. Its primary emphasis is on the study of the electrical and electronic aspects of transportation systems. However, it also considers research on mechanical parts or subsystems of vehicles if there is a clear interaction with electrical or electronic equipment.
Please note that this journal excludes other aspects such as sociological, political, regulatory, or environmental factors from its scope.