{"title":"基于回归的电池健康状况评估,适用于多种电动汽车快速充电协议","authors":"","doi":"10.1016/j.jpowsour.2024.235601","DOIUrl":null,"url":null,"abstract":"<div><div>In this work, a data-driven estimation method is developed to estimate the battery state of health (SOH), exploiting SOH features that can be obtained during fast-charging events. A newly expanded experimental dataset with six cells, cycled 1200 to 1800 times until 70% SOH is reached, is used and made available. Our investigation focuses on the variability that can be encountered in charging events due to different charging protocols (particularly for fast charging) and partial charging events. In particular, we investigated nine different SOH features, introducing novel formulations to increase their flexibility with respect to different charging events. Then, we assessed the practical implementability of these features and employed correlation and feature importance analyses to identify the most effective. Finally, we developed a linear regression model for SOH estimation using the selected features as inputs. The model shows an RMS prediction error as low as 1.09% over the battery lifetime and a maximum error no greater than 3.5% until SOH falls below 80%, corresponding to the end-of-life for automotive applications. The estimator is also shown to be robust against significant errors of the state of charge (SOC) input value (as high as 5%), ensuring it will perform well even when SOC is not accurately known.</div></div>","PeriodicalId":377,"journal":{"name":"Journal of Power Sources","volume":null,"pages":null},"PeriodicalIF":8.1000,"publicationDate":"2024-10-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Regression based battery state of health estimation for multiple electric vehicle fast charging protocols\",\"authors\":\"\",\"doi\":\"10.1016/j.jpowsour.2024.235601\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><div>In this work, a data-driven estimation method is developed to estimate the battery state of health (SOH), exploiting SOH features that can be obtained during fast-charging events. A newly expanded experimental dataset with six cells, cycled 1200 to 1800 times until 70% SOH is reached, is used and made available. Our investigation focuses on the variability that can be encountered in charging events due to different charging protocols (particularly for fast charging) and partial charging events. In particular, we investigated nine different SOH features, introducing novel formulations to increase their flexibility with respect to different charging events. Then, we assessed the practical implementability of these features and employed correlation and feature importance analyses to identify the most effective. Finally, we developed a linear regression model for SOH estimation using the selected features as inputs. The model shows an RMS prediction error as low as 1.09% over the battery lifetime and a maximum error no greater than 3.5% until SOH falls below 80%, corresponding to the end-of-life for automotive applications. The estimator is also shown to be robust against significant errors of the state of charge (SOC) input value (as high as 5%), ensuring it will perform well even when SOC is not accurately known.</div></div>\",\"PeriodicalId\":377,\"journal\":{\"name\":\"Journal of Power Sources\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":8.1000,\"publicationDate\":\"2024-10-14\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Journal of Power Sources\",\"FirstCategoryId\":\"5\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S0378775324015532\",\"RegionNum\":2,\"RegionCategory\":\"工程技术\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"CHEMISTRY, PHYSICAL\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Power Sources","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S0378775324015532","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"CHEMISTRY, PHYSICAL","Score":null,"Total":0}
Regression based battery state of health estimation for multiple electric vehicle fast charging protocols
In this work, a data-driven estimation method is developed to estimate the battery state of health (SOH), exploiting SOH features that can be obtained during fast-charging events. A newly expanded experimental dataset with six cells, cycled 1200 to 1800 times until 70% SOH is reached, is used and made available. Our investigation focuses on the variability that can be encountered in charging events due to different charging protocols (particularly for fast charging) and partial charging events. In particular, we investigated nine different SOH features, introducing novel formulations to increase their flexibility with respect to different charging events. Then, we assessed the practical implementability of these features and employed correlation and feature importance analyses to identify the most effective. Finally, we developed a linear regression model for SOH estimation using the selected features as inputs. The model shows an RMS prediction error as low as 1.09% over the battery lifetime and a maximum error no greater than 3.5% until SOH falls below 80%, corresponding to the end-of-life for automotive applications. The estimator is also shown to be robust against significant errors of the state of charge (SOC) input value (as high as 5%), ensuring it will perform well even when SOC is not accurately known.
期刊介绍:
The Journal of Power Sources is a publication catering to researchers and technologists interested in various aspects of the science, technology, and applications of electrochemical power sources. It covers original research and reviews on primary and secondary batteries, fuel cells, supercapacitors, and photo-electrochemical cells.
Topics considered include the research, development and applications of nanomaterials and novel componentry for these devices. Examples of applications of these electrochemical power sources include:
• Portable electronics
• Electric and Hybrid Electric Vehicles
• Uninterruptible Power Supply (UPS) systems
• Storage of renewable energy
• Satellites and deep space probes
• Boats and ships, drones and aircrafts
• Wearable energy storage systems