Jose Ponce, Alvin Barbier, Carlos E. Palau, Carlos Guardiola
{"title":"通过遗传算法优化构建虚拟真实驾驶排放行程的新方法","authors":"Jose Ponce, Alvin Barbier, Carlos E. Palau, Carlos Guardiola","doi":"10.1016/j.engappai.2024.109637","DOIUrl":null,"url":null,"abstract":"<div><div>The Real Driving Emission (RDE) test became a critical part of the process conducted by manufacturers to fulfill the approval procedure of every new vehicle model. This test measures the regulated emissions from a vehicle during a trip, which follows a specific set of operation requirements, aiming to assess the vehicle’s emission levels in real-world conditions. Additionally, In-Service Conformity (ISC) tests, which consist in performing an RDE trip, were also introduced to demonstrate vehicles emissions compliance over their lifespan. Considering that modern vehicles embed exhaust emission sensors and connectivity capabilities, it is believed that there is an opportunity for manufacturers to leverage the data generated by these vehicles to forecast the outcomes of an ISC test. However, as this study presents through the analysis of an extensive database of more than 600 trips from a mild-hybrid diesel vehicle, none of the real-world trips might comply with all the driving requirements of the RDE standard. Faced with this outcome, this work proposes the application of a Genetic Algorithm (GA) optimization to construct virtual RDE trips from real-driving data. In particular, the proposed methodology leverages such algorithm to combine real driving fragments from various trips in order to align with the main RDE trip requirements. The methodology focuses on vehicle, engine, and exhaust after-treatment variables, utilizing signal optimization connections to create a realistic analysis of vehicle pollutants. The research suggests that a combination of vehicle speed, coolant temperature, exhaust temperature, and Selective Catalytic Reduction (SCR) load leads to a significant number of RDE-compliant results under simplified legislative conditions, from which emissions profiles could be assessed. The proposed methodology details the development of an Adaptive Genetic Algorithm (AGA) and the data pipeline to create specific RDE trips, offering the capability to customize the desired Driving Cycles (DC).</div></div>","PeriodicalId":50523,"journal":{"name":"Engineering Applications of Artificial Intelligence","volume":"139 ","pages":"Article 109637"},"PeriodicalIF":7.5000,"publicationDate":"2024-11-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A novel approach in constructing virtual real driving emission trips through genetic algorithm optimization\",\"authors\":\"Jose Ponce, Alvin Barbier, Carlos E. Palau, Carlos Guardiola\",\"doi\":\"10.1016/j.engappai.2024.109637\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>The Real Driving Emission (RDE) test became a critical part of the process conducted by manufacturers to fulfill the approval procedure of every new vehicle model. This test measures the regulated emissions from a vehicle during a trip, which follows a specific set of operation requirements, aiming to assess the vehicle’s emission levels in real-world conditions. Additionally, In-Service Conformity (ISC) tests, which consist in performing an RDE trip, were also introduced to demonstrate vehicles emissions compliance over their lifespan. Considering that modern vehicles embed exhaust emission sensors and connectivity capabilities, it is believed that there is an opportunity for manufacturers to leverage the data generated by these vehicles to forecast the outcomes of an ISC test. However, as this study presents through the analysis of an extensive database of more than 600 trips from a mild-hybrid diesel vehicle, none of the real-world trips might comply with all the driving requirements of the RDE standard. Faced with this outcome, this work proposes the application of a Genetic Algorithm (GA) optimization to construct virtual RDE trips from real-driving data. In particular, the proposed methodology leverages such algorithm to combine real driving fragments from various trips in order to align with the main RDE trip requirements. The methodology focuses on vehicle, engine, and exhaust after-treatment variables, utilizing signal optimization connections to create a realistic analysis of vehicle pollutants. The research suggests that a combination of vehicle speed, coolant temperature, exhaust temperature, and Selective Catalytic Reduction (SCR) load leads to a significant number of RDE-compliant results under simplified legislative conditions, from which emissions profiles could be assessed. The proposed methodology details the development of an Adaptive Genetic Algorithm (AGA) and the data pipeline to create specific RDE trips, offering the capability to customize the desired Driving Cycles (DC).</div></div>\",\"PeriodicalId\":50523,\"journal\":{\"name\":\"Engineering Applications of Artificial Intelligence\",\"volume\":\"139 \",\"pages\":\"Article 109637\"},\"PeriodicalIF\":7.5000,\"publicationDate\":\"2024-11-23\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Engineering Applications of Artificial Intelligence\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0952197624017950\",\"RegionNum\":2,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Engineering Applications of Artificial Intelligence","FirstCategoryId":"94","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0952197624017950","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
A novel approach in constructing virtual real driving emission trips through genetic algorithm optimization
The Real Driving Emission (RDE) test became a critical part of the process conducted by manufacturers to fulfill the approval procedure of every new vehicle model. This test measures the regulated emissions from a vehicle during a trip, which follows a specific set of operation requirements, aiming to assess the vehicle’s emission levels in real-world conditions. Additionally, In-Service Conformity (ISC) tests, which consist in performing an RDE trip, were also introduced to demonstrate vehicles emissions compliance over their lifespan. Considering that modern vehicles embed exhaust emission sensors and connectivity capabilities, it is believed that there is an opportunity for manufacturers to leverage the data generated by these vehicles to forecast the outcomes of an ISC test. However, as this study presents through the analysis of an extensive database of more than 600 trips from a mild-hybrid diesel vehicle, none of the real-world trips might comply with all the driving requirements of the RDE standard. Faced with this outcome, this work proposes the application of a Genetic Algorithm (GA) optimization to construct virtual RDE trips from real-driving data. In particular, the proposed methodology leverages such algorithm to combine real driving fragments from various trips in order to align with the main RDE trip requirements. The methodology focuses on vehicle, engine, and exhaust after-treatment variables, utilizing signal optimization connections to create a realistic analysis of vehicle pollutants. The research suggests that a combination of vehicle speed, coolant temperature, exhaust temperature, and Selective Catalytic Reduction (SCR) load leads to a significant number of RDE-compliant results under simplified legislative conditions, from which emissions profiles could be assessed. The proposed methodology details the development of an Adaptive Genetic Algorithm (AGA) and the data pipeline to create specific RDE trips, offering the capability to customize the desired Driving Cycles (DC).
期刊介绍:
Artificial Intelligence (AI) is pivotal in driving the fourth industrial revolution, witnessing remarkable advancements across various machine learning methodologies. AI techniques have become indispensable tools for practicing engineers, enabling them to tackle previously insurmountable challenges. Engineering Applications of Artificial Intelligence serves as a global platform for the swift dissemination of research elucidating the practical application of AI methods across all engineering disciplines. Submitted papers are expected to present novel aspects of AI utilized in real-world engineering applications, validated using publicly available datasets to ensure the replicability of research outcomes. Join us in exploring the transformative potential of AI in engineering.