PHM overview on battery models approaches

• Autor(a) principal: Esteves, Manuel A. M.
• Data de Publicação: 2017
• Outros Autores: Nunes, Eusébio P.
• Idioma: eng
• Título da fonte: Repositórios Científicos de Acesso Aberto de Portugal (RCAAP)
• Texto Completo: http://hdl.handle.net/1822/50790
• topic: Battery prognostics; Model-based; Data-driven; Fusion; Engenharia e Tecnologia::Engenharia Eletrotécnica, Eletrónica e Informática

The Battery Management Systems (BMS) brought a new impetus to the battery energy management which lead to an increase in battery life. But the BMS fails when the State of Charge (SoC), State of Health (SoH), State of Life (SoL) or Remaining Useful Life (RUL) prognostics systems do not provide the required accuracy. Despite the increase of complexity and accuracy of battery models, the poor performance with floating temperature and load profiles persists. With the development of innovative products on wide-ranging applications, the battery materials, technologies, reliability and safety are being pressed to their limits. Therefore, a huge amount of work is still necessary, not only on the development of new battery technologies but also on the BMS, battery models and metrics accuracy improvements. The paper gives a comprehensive overview of the applicability, accuracy, weaknesses and advantages of the most recent battery models. The paper will also discuss how the Prognostics Health Management (PHM) can support a technologic impetus on battery affairs with battery models and metrics accuracy improvements.