Battery monitoring and prognostics optimization techniques: Challenges and opportunities

In recent years, many researchers have been conducted on batteries' health monitoring and prognostics, mainly focusing on the batteries' state of charge (SOC). Accurately estimating the state of health (SOH) and predicting the remaining useful life (RUL) of battery components are very important for the prognosis and health management of the overall battery system. However, due to the non-linear dynamics caused by the electrochemical characteristics in batteries, the accurate estimations of SOC, SOH and RUL prediction are still challenging and many technologies have been developed to solve this challenge. This paper reviews and discusses state of the art in SOC and SOH and RUL estimation techniques for all battery types. A novel framework is developed and presented to compare all battery techniques based on three dimensions: battery performance (Z dimension), approaches (X dimension), and criteria (Y dimension) to fulfil. All studies are reviewed and discussed based on the dimensions and the criteria defined in the framework. Based on this investigation, this study summarizes at the end the key outcomes and suggests future research challenges