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Novel SOC monitoring approach for lithium batteries

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Detalhes bibliográficos
Resumo:The key element in storage based systems remains the ability to monitor, control and optimise the performance of one or more modules of these batteries, the type of device performing this task is often referred to as a Battery Management System (BMS). A BMS is a basical units of electrical energy storage systems, a variety of already developed algorithms can be applied to define the main states of the battery, among others: state of charge (SOC), state of health (SOH) and state of functions (SOF) that allow real-time management of the batteries. All research in the field of Extended Kalman Filter (EKF) based BMS is based on bench-scale experiments using powerful softwares, such as MATLAB, for data processing and controllers such as dSPACE. So far, the constraint of computational power limitation is not really addressed in the majority of scientific papers dealing with this subject. This paper proposes an approach to implement an extended Kalman filter linked to a Coulomb counting method, this method called DCC-EKF will allow a better quality monitoring of the battery.
Autores principais:Chellal, Arezki Abderrahim
Outros Autores:Lima, José; Gonçalves, José; Megnafi, Hicham
Assunto:Embedded system Battery management system Extended kalman filter Coulomb counting
Ano:2021
País:Portugal
Tipo de documento:comunicação em conferência
Tipo de acesso:acesso aberto
Instituição associada:Instituto Politécnico de Bragança
Idioma:inglês
Origem:Biblioteca Digital do IPB
Descrição
Resumo:The key element in storage based systems remains the ability to monitor, control and optimise the performance of one or more modules of these batteries, the type of device performing this task is often referred to as a Battery Management System (BMS). A BMS is a basical units of electrical energy storage systems, a variety of already developed algorithms can be applied to define the main states of the battery, among others: state of charge (SOC), state of health (SOH) and state of functions (SOF) that allow real-time management of the batteries. All research in the field of Extended Kalman Filter (EKF) based BMS is based on bench-scale experiments using powerful softwares, such as MATLAB, for data processing and controllers such as dSPACE. So far, the constraint of computational power limitation is not really addressed in the majority of scientific papers dealing with this subject. This paper proposes an approach to implement an extended Kalman filter linked to a Coulomb counting method, this method called DCC-EKF will allow a better quality monitoring of the battery.