{"title":"串联式混合动力汽车瞬态发电机最优实时控制","authors":"Jonathan Lock, Rickard Arvidsson, T. McKelvey","doi":"10.1115/dscc2019-8964","DOIUrl":null,"url":null,"abstract":"\n We study the dynamic engine-generator optimal control problem with a goal of minimizing fuel consumption while delivering a requested average electrical power. By using an infinite-horizon formulation and explicitly minimizing fuel consumption, we avoid issues inherent with penalty-based and finite-horizon problems. The solution to the optimal control problem, found using dynamic programming and the successive approximation method, can be expressed as instantaneous non-linear state-feedback. This allows for trivial real-time control, typically requiring 10–20 CPU instructions per control period, a few bytes of RAM, and 5–20 KiB of nonvolatile memory. Simulation results for a passenger vehicle indicate a fuel consumption improvement in the region of 5–7% during the transient phase when compared with the class of controllers found in the industry. Bench-tests, where the optimal controller is executed in native hardware, show an improvement of 3.7%, primarily limited by unmodeled dynamics. Our specific choice of problem formulation, a guaranteed globally optimal solution, and trivial real-time control resolve many of the limitations with the current state of optimal engine-generator controllers.","PeriodicalId":41412,"journal":{"name":"Mechatronic Systems and Control","volume":"5 1","pages":""},"PeriodicalIF":1.0000,"publicationDate":"2019-11-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Optimal Transient Real-Time Engine-Generator Control in the Series-Hybrid Vehicle\",\"authors\":\"Jonathan Lock, Rickard Arvidsson, T. McKelvey\",\"doi\":\"10.1115/dscc2019-8964\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"\\n We study the dynamic engine-generator optimal control problem with a goal of minimizing fuel consumption while delivering a requested average electrical power. By using an infinite-horizon formulation and explicitly minimizing fuel consumption, we avoid issues inherent with penalty-based and finite-horizon problems. The solution to the optimal control problem, found using dynamic programming and the successive approximation method, can be expressed as instantaneous non-linear state-feedback. This allows for trivial real-time control, typically requiring 10–20 CPU instructions per control period, a few bytes of RAM, and 5–20 KiB of nonvolatile memory. Simulation results for a passenger vehicle indicate a fuel consumption improvement in the region of 5–7% during the transient phase when compared with the class of controllers found in the industry. Bench-tests, where the optimal controller is executed in native hardware, show an improvement of 3.7%, primarily limited by unmodeled dynamics. Our specific choice of problem formulation, a guaranteed globally optimal solution, and trivial real-time control resolve many of the limitations with the current state of optimal engine-generator controllers.\",\"PeriodicalId\":41412,\"journal\":{\"name\":\"Mechatronic Systems and Control\",\"volume\":\"5 1\",\"pages\":\"\"},\"PeriodicalIF\":1.0000,\"publicationDate\":\"2019-11-26\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Mechatronic Systems and Control\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1115/dscc2019-8964\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"AUTOMATION & CONTROL SYSTEMS\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Mechatronic Systems and Control","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1115/dscc2019-8964","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"AUTOMATION & CONTROL SYSTEMS","Score":null,"Total":0}
Optimal Transient Real-Time Engine-Generator Control in the Series-Hybrid Vehicle
We study the dynamic engine-generator optimal control problem with a goal of minimizing fuel consumption while delivering a requested average electrical power. By using an infinite-horizon formulation and explicitly minimizing fuel consumption, we avoid issues inherent with penalty-based and finite-horizon problems. The solution to the optimal control problem, found using dynamic programming and the successive approximation method, can be expressed as instantaneous non-linear state-feedback. This allows for trivial real-time control, typically requiring 10–20 CPU instructions per control period, a few bytes of RAM, and 5–20 KiB of nonvolatile memory. Simulation results for a passenger vehicle indicate a fuel consumption improvement in the region of 5–7% during the transient phase when compared with the class of controllers found in the industry. Bench-tests, where the optimal controller is executed in native hardware, show an improvement of 3.7%, primarily limited by unmodeled dynamics. Our specific choice of problem formulation, a guaranteed globally optimal solution, and trivial real-time control resolve many of the limitations with the current state of optimal engine-generator controllers.
期刊介绍:
This international journal publishes both theoretical and application-oriented papers on various aspects of mechatronic systems, modelling, design, conventional and intelligent control, and intelligent systems. Application areas of mechatronics may include robotics, transportation, energy systems, manufacturing, sensors, actuators, and automation. Techniques of artificial intelligence may include soft computing (fuzzy logic, neural networks, genetic algorithms/evolutionary computing, probabilistic methods, etc.). Techniques may cover frequency and time domains, linear and nonlinear systems, and deterministic and stochastic processes. Hybrid techniques of mechatronics that combine conventional and intelligent methods are also included. First published in 1972, this journal originated with an emphasis on conventional control systems and computer-based applications. Subsequently, with rapid advances in the field and in view of the widespread interest and application of soft computing in control systems, this latter aspect was integrated into the journal. Now the area of mechatronics is included as the main focus. A unique feature of the journal is its pioneering role in bridging the gap between conventional systems and intelligent systems, with an equal emphasis on theory and practical applications, including system modelling, design and instrumentation. It appears four times per year.