A review of machine learning state-of-charge and state-of-health estimation algorithms for lithium-ion batteries

Vehicle electrification has been proven to be an efficient way to reduce carbon dioxide emissions and solve the energy crisis. Lithium-ion batteries (LiBs) are considered the dominant energy storage medium for electric vehicles (EVs) owing to their high energy density and long lifespan. To maintain a safe, efficient, and stable operating condition for the battery system, we must monitor the state of the battery, especially the state-of-charge (SOC) and state-of-health (SOH). With the development of big data, cloud computing, and other emerging techniques, data-driven machine learning (ML) techniques have attracted attention for their enormous potential in state estimation for LiBs. Therefore, this paper…

• HE
  Higher Education Discipline Innovation Project

  Award: B17034