{"title":"利用强化学习和小口径柴油发动机的功能模拟装置优化多次喷射的燃油喷射时机","authors":"Abhijeet Vaze, Pramod S. Mehta, Anand Krishnasamy","doi":"10.4271/03-17-06-0041","DOIUrl":null,"url":null,"abstract":"Reinforcement learning (RL) is a computational approach to understanding and\n automating goal-directed learning and decision-making. The difference from other\n computational approaches is the emphasis on learning by an agent from direct\n interaction with its environment to achieve long-term goals [1]. In this work, the RL algorithm was\n implemented using Python. This then enables the RL algorithm to make decisions\n to optimize the output from the system and provide real-time adaptation to\n changes and their retention for future usage. A diesel engine is a complex\n system where a RL algorithm can address the NOx–soot emissions\n trade-off by controlling fuel injection quantity and timing. This study used RL\n to optimize the fuel injection timing to get a better NO–soot trade-off for a\n common rail diesel engine. The diesel engine utilizes a pilot–main and a\n pilot–main–post-fuel injection strategy. Change of fuel injection quantity was\n not attempted in this study as the main objective was to demonstrate the use of\n RL algorithms while maintaining a constant indicated mean effective pressure. A\n change in fuel quantity has a larger influence on the indicated mean effective\n pressure than a change in fuel injection timing. The focus of this work was to\n present a novel methodology of using the 3D combustion data from analysis\n software in the form of a functional mock-up unit (FMU) and showcasing the\n implementation of a RL algorithm in Python language to interact with the FMU to\n reduce the NO and soot emissions by suggesting changes to the main injection\n timing in a pilot–main and pilot–main–post-injection strategy. RL algorithms\n identified the operating injection strategy, i.e., main injection timing for a\n pilot–main and pilot–main–post-injection strategy, reducing NO emissions from\n 38% to 56% and soot emissions from 10% to 90% for a range of fuel injection\n strategies.","PeriodicalId":47948,"journal":{"name":"SAE International Journal of Engines","volume":null,"pages":null},"PeriodicalIF":1.1000,"publicationDate":"2024-05-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimizing Fuel Injection Timing for Multiple Injection Using\\n Reinforcement Learning and Functional Mock-up Unit for a Small-bore Diesel\\n Engine\",\"authors\":\"Abhijeet Vaze, Pramod S. Mehta, Anand Krishnasamy\",\"doi\":\"10.4271/03-17-06-0041\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Reinforcement learning (RL) is a computational approach to understanding and\\n automating goal-directed learning and decision-making. The difference from other\\n computational approaches is the emphasis on learning by an agent from direct\\n interaction with its environment to achieve long-term goals [1]. In this work, the RL algorithm was\\n implemented using Python. This then enables the RL algorithm to make decisions\\n to optimize the output from the system and provide real-time adaptation to\\n changes and their retention for future usage. A diesel engine is a complex\\n system where a RL algorithm can address the NOx–soot emissions\\n trade-off by controlling fuel injection quantity and timing. This study used RL\\n to optimize the fuel injection timing to get a better NO–soot trade-off for a\\n common rail diesel engine. The diesel engine utilizes a pilot–main and a\\n pilot–main–post-fuel injection strategy. Change of fuel injection quantity was\\n not attempted in this study as the main objective was to demonstrate the use of\\n RL algorithms while maintaining a constant indicated mean effective pressure. A\\n change in fuel quantity has a larger influence on the indicated mean effective\\n pressure than a change in fuel injection timing. The focus of this work was to\\n present a novel methodology of using the 3D combustion data from analysis\\n software in the form of a functional mock-up unit (FMU) and showcasing the\\n implementation of a RL algorithm in Python language to interact with the FMU to\\n reduce the NO and soot emissions by suggesting changes to the main injection\\n timing in a pilot–main and pilot–main–post-injection strategy. RL algorithms\\n identified the operating injection strategy, i.e., main injection timing for a\\n pilot–main and pilot–main–post-injection strategy, reducing NO emissions from\\n 38% to 56% and soot emissions from 10% to 90% for a range of fuel injection\\n strategies.\",\"PeriodicalId\":47948,\"journal\":{\"name\":\"SAE International Journal of Engines\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.1000,\"publicationDate\":\"2024-05-03\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"SAE International Journal of Engines\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.4271/03-17-06-0041\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"TRANSPORTATION SCIENCE & TECHNOLOGY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"SAE International Journal of Engines","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.4271/03-17-06-0041","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"TRANSPORTATION SCIENCE & TECHNOLOGY","Score":null,"Total":0}
Optimizing Fuel Injection Timing for Multiple Injection Using
Reinforcement Learning and Functional Mock-up Unit for a Small-bore Diesel
Engine
Reinforcement learning (RL) is a computational approach to understanding and
automating goal-directed learning and decision-making. The difference from other
computational approaches is the emphasis on learning by an agent from direct
interaction with its environment to achieve long-term goals [1]. In this work, the RL algorithm was
implemented using Python. This then enables the RL algorithm to make decisions
to optimize the output from the system and provide real-time adaptation to
changes and their retention for future usage. A diesel engine is a complex
system where a RL algorithm can address the NOx–soot emissions
trade-off by controlling fuel injection quantity and timing. This study used RL
to optimize the fuel injection timing to get a better NO–soot trade-off for a
common rail diesel engine. The diesel engine utilizes a pilot–main and a
pilot–main–post-fuel injection strategy. Change of fuel injection quantity was
not attempted in this study as the main objective was to demonstrate the use of
RL algorithms while maintaining a constant indicated mean effective pressure. A
change in fuel quantity has a larger influence on the indicated mean effective
pressure than a change in fuel injection timing. The focus of this work was to
present a novel methodology of using the 3D combustion data from analysis
software in the form of a functional mock-up unit (FMU) and showcasing the
implementation of a RL algorithm in Python language to interact with the FMU to
reduce the NO and soot emissions by suggesting changes to the main injection
timing in a pilot–main and pilot–main–post-injection strategy. RL algorithms
identified the operating injection strategy, i.e., main injection timing for a
pilot–main and pilot–main–post-injection strategy, reducing NO emissions from
38% to 56% and soot emissions from 10% to 90% for a range of fuel injection
strategies.