{"title":"面向验证的可扩展锂离子电池管理系统的开发","authors":"X. Liu-Henke, Soeren Scherler, Sven Jacobitz","doi":"10.1109/EVER.2017.7935868","DOIUrl":null,"url":null,"abstract":"This paper describes the model-based development and validation of a flexible scalable battery management system (BMS) for lithium-ion batteries (LiFePO4) using a verification-oriented development methodology. The BMS consists of a central control unit and decentral cell modules. It measures voltages, currents and temperatures to ensure safe and slow aging operating states. Furthermore, appropriate algorithms estimate the state of charge and predict the maximum battery power in a defined prediction horizon Δt, based on the measurement data. The entire capacity of the production-related slightly differing battery cells can be optimally used by using passive or active load balancing methods which equalize the cell charging levels. The BMS also has a CAN interface for integration into a vehicle ECU network. The development of an Extended Kalman Filter (EKF) as a state of charge (SOC) estimator will be shown as an example for the model-based design process.","PeriodicalId":395329,"journal":{"name":"2017 Twelfth International Conference on Ecological Vehicles and Renewable Energies (EVER)","volume":"67 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2017-04-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"7","resultStr":"{\"title\":\"Verification oriented development of a scalable battery management system for lithium-ion batteries\",\"authors\":\"X. Liu-Henke, Soeren Scherler, Sven Jacobitz\",\"doi\":\"10.1109/EVER.2017.7935868\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"This paper describes the model-based development and validation of a flexible scalable battery management system (BMS) for lithium-ion batteries (LiFePO4) using a verification-oriented development methodology. The BMS consists of a central control unit and decentral cell modules. It measures voltages, currents and temperatures to ensure safe and slow aging operating states. Furthermore, appropriate algorithms estimate the state of charge and predict the maximum battery power in a defined prediction horizon Δt, based on the measurement data. The entire capacity of the production-related slightly differing battery cells can be optimally used by using passive or active load balancing methods which equalize the cell charging levels. The BMS also has a CAN interface for integration into a vehicle ECU network. The development of an Extended Kalman Filter (EKF) as a state of charge (SOC) estimator will be shown as an example for the model-based design process.\",\"PeriodicalId\":395329,\"journal\":{\"name\":\"2017 Twelfth International Conference on Ecological Vehicles and Renewable Energies (EVER)\",\"volume\":\"67 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2017-04-11\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"7\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2017 Twelfth International Conference on Ecological Vehicles and Renewable Energies (EVER)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EVER.2017.7935868\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2017 Twelfth International Conference on Ecological Vehicles and Renewable Energies (EVER)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EVER.2017.7935868","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Verification oriented development of a scalable battery management system for lithium-ion batteries
This paper describes the model-based development and validation of a flexible scalable battery management system (BMS) for lithium-ion batteries (LiFePO4) using a verification-oriented development methodology. The BMS consists of a central control unit and decentral cell modules. It measures voltages, currents and temperatures to ensure safe and slow aging operating states. Furthermore, appropriate algorithms estimate the state of charge and predict the maximum battery power in a defined prediction horizon Δt, based on the measurement data. The entire capacity of the production-related slightly differing battery cells can be optimally used by using passive or active load balancing methods which equalize the cell charging levels. The BMS also has a CAN interface for integration into a vehicle ECU network. The development of an Extended Kalman Filter (EKF) as a state of charge (SOC) estimator will be shown as an example for the model-based design process.