{"title":"基于卡尔曼滤波的纯电动汽车实时环境温度估计与牵引功率感知的座舱气候控制","authors":"Maryam Alizadeh, Sumedh Dhale, A. Emadi","doi":"10.1109/itec53557.2022.9813905","DOIUrl":null,"url":null,"abstract":"In this paper, an improved climate control system is presented for a Heating, Ventilation, and Air conditioning (HVAC) unit of a battery electric vehicle (BEV) to improve the system’s efficiency while maintaining the desired cabin temperature for the passengers. Since BEVs are entirely dependent on the battery power for HVAC usage, it is crucial to adapt the HVAC control according to the battery status to improve the battery usage. Therefore, our proposed climate control system has taken into account the dynamics of the HVAC model while considering the importance of the ambient temperature and route behavior on the power usage that is needed to provide a comfortable climate in the cabin. Since the ambient temperature has a critical role in estimating the required HVAC power, it is necessary to assess it precisely. Accordingly, a Kalman filter is designed to achieve high precision temperature estimation in real-time. Furthermore, the effect of the driving cycle on the traction motor is considered to improve the overall performance of the vehicle’s system and battery’s health by adjusting climate controller behavior in different weather conditions. A comprehensive simulation study in MATLAB/Simulink® is provided to evaluate the effectiveness of the proposed climate control technique and Kalman filter based ambient temperature estimation.","PeriodicalId":275570,"journal":{"name":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","volume":"68 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2022-06-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Real-Time Ambient Temperature Estimation Using Kalman Filter and Traction Power-Aware Cabin Climate Control in Battery Electric Vehicles\",\"authors\":\"Maryam Alizadeh, Sumedh Dhale, A. Emadi\",\"doi\":\"10.1109/itec53557.2022.9813905\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"In this paper, an improved climate control system is presented for a Heating, Ventilation, and Air conditioning (HVAC) unit of a battery electric vehicle (BEV) to improve the system’s efficiency while maintaining the desired cabin temperature for the passengers. Since BEVs are entirely dependent on the battery power for HVAC usage, it is crucial to adapt the HVAC control according to the battery status to improve the battery usage. Therefore, our proposed climate control system has taken into account the dynamics of the HVAC model while considering the importance of the ambient temperature and route behavior on the power usage that is needed to provide a comfortable climate in the cabin. Since the ambient temperature has a critical role in estimating the required HVAC power, it is necessary to assess it precisely. Accordingly, a Kalman filter is designed to achieve high precision temperature estimation in real-time. Furthermore, the effect of the driving cycle on the traction motor is considered to improve the overall performance of the vehicle’s system and battery’s health by adjusting climate controller behavior in different weather conditions. A comprehensive simulation study in MATLAB/Simulink® is provided to evaluate the effectiveness of the proposed climate control technique and Kalman filter based ambient temperature estimation.\",\"PeriodicalId\":275570,\"journal\":{\"name\":\"2022 IEEE Transportation Electrification Conference & Expo (ITEC)\",\"volume\":\"68 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2022-06-15\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2022 IEEE Transportation Electrification Conference & Expo (ITEC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/itec53557.2022.9813905\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2022 IEEE Transportation Electrification Conference & Expo (ITEC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/itec53557.2022.9813905","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Real-Time Ambient Temperature Estimation Using Kalman Filter and Traction Power-Aware Cabin Climate Control in Battery Electric Vehicles
In this paper, an improved climate control system is presented for a Heating, Ventilation, and Air conditioning (HVAC) unit of a battery electric vehicle (BEV) to improve the system’s efficiency while maintaining the desired cabin temperature for the passengers. Since BEVs are entirely dependent on the battery power for HVAC usage, it is crucial to adapt the HVAC control according to the battery status to improve the battery usage. Therefore, our proposed climate control system has taken into account the dynamics of the HVAC model while considering the importance of the ambient temperature and route behavior on the power usage that is needed to provide a comfortable climate in the cabin. Since the ambient temperature has a critical role in estimating the required HVAC power, it is necessary to assess it precisely. Accordingly, a Kalman filter is designed to achieve high precision temperature estimation in real-time. Furthermore, the effect of the driving cycle on the traction motor is considered to improve the overall performance of the vehicle’s system and battery’s health by adjusting climate controller behavior in different weather conditions. A comprehensive simulation study in MATLAB/Simulink® is provided to evaluate the effectiveness of the proposed climate control technique and Kalman filter based ambient temperature estimation.