Jingzhou Gao, Kai Xu, Ke Li, Wei Du, Zhenhao Zheng, Shengdun Zhao, Lijun Yan
{"title":"使用两种改进的速度预测器对混合动力电动汽车进行确定性和随机模型预测能源管理","authors":"Jingzhou Gao, Kai Xu, Ke Li, Wei Du, Zhenhao Zheng, Shengdun Zhao, Lijun Yan","doi":"10.1177/16878132241259937","DOIUrl":null,"url":null,"abstract":"The performance of model predictive control strategies for hybrid electric vehicles (HEVs) highly depends on the accuracy of future speed predictions. This paper proposes improved prediction models for deterministic model predictive control (DMPC) and stochastic model predictive control (SMPC), respectively. For DMPC, the neural network-based predictor is first introduced and taken as the benchmark predictor. A novel deterministic predictor considering historical prediction errors is proposed, which relies on the assumption that the offset between the prediction and measurement at current instant is a good estimate of the offset in the short future. Based on the proposed deterministic predictor, a stochastic predictor that considers the distribution law of historical data at different locations is further proposed for SMPC. Simulation results show that the controller using the proposed deterministic prediction model improves fuel economy by 2.89%, and the controller using the proposed stochastic prediction model improves fuel economy by 4.5% compared with the benchmark.","PeriodicalId":49110,"journal":{"name":"Advances in Mechanical Engineering","volume":null,"pages":null},"PeriodicalIF":1.9000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Deterministic and stochastic model predictive energy management of hybrid electric vehicles using two improved speed predictors\",\"authors\":\"Jingzhou Gao, Kai Xu, Ke Li, Wei Du, Zhenhao Zheng, Shengdun Zhao, Lijun Yan\",\"doi\":\"10.1177/16878132241259937\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The performance of model predictive control strategies for hybrid electric vehicles (HEVs) highly depends on the accuracy of future speed predictions. This paper proposes improved prediction models for deterministic model predictive control (DMPC) and stochastic model predictive control (SMPC), respectively. For DMPC, the neural network-based predictor is first introduced and taken as the benchmark predictor. A novel deterministic predictor considering historical prediction errors is proposed, which relies on the assumption that the offset between the prediction and measurement at current instant is a good estimate of the offset in the short future. Based on the proposed deterministic predictor, a stochastic predictor that considers the distribution law of historical data at different locations is further proposed for SMPC. Simulation results show that the controller using the proposed deterministic prediction model improves fuel economy by 2.89%, and the controller using the proposed stochastic prediction model improves fuel economy by 4.5% compared with the benchmark.\",\"PeriodicalId\":49110,\"journal\":{\"name\":\"Advances in Mechanical Engineering\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.9000,\"publicationDate\":\"2024-06-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Advances in Mechanical Engineering\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://doi.org/10.1177/16878132241259937\",\"RegionNum\":4,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"ENGINEERING, MECHANICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advances in Mechanical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1177/16878132241259937","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, MECHANICAL","Score":null,"Total":0}
Deterministic and stochastic model predictive energy management of hybrid electric vehicles using two improved speed predictors
The performance of model predictive control strategies for hybrid electric vehicles (HEVs) highly depends on the accuracy of future speed predictions. This paper proposes improved prediction models for deterministic model predictive control (DMPC) and stochastic model predictive control (SMPC), respectively. For DMPC, the neural network-based predictor is first introduced and taken as the benchmark predictor. A novel deterministic predictor considering historical prediction errors is proposed, which relies on the assumption that the offset between the prediction and measurement at current instant is a good estimate of the offset in the short future. Based on the proposed deterministic predictor, a stochastic predictor that considers the distribution law of historical data at different locations is further proposed for SMPC. Simulation results show that the controller using the proposed deterministic prediction model improves fuel economy by 2.89%, and the controller using the proposed stochastic prediction model improves fuel economy by 4.5% compared with the benchmark.
期刊介绍:
Advances in Mechanical Engineering (AIME) is a JCR Ranked, peer-reviewed, open access journal which publishes a wide range of original research and review articles. The journal Editorial Board welcomes manuscripts in both fundamental and applied research areas, and encourages submissions which contribute novel and innovative insights to the field of mechanical engineering