Health Prediction and Remaining Useful Life Estimation for Energy-Storage Systems

Purpose: The rechargeable batteries are their major element where the energy-storage systems are central to the modern power networks, electric transportation, and the portable electronic devices. The possibility to evaluate the battery condition and estimate the degradation with time is the key to the performance, reliability, and safety of these systems. Materials and Methods: Two significant measures of such a purpose are the state of health (SOH), which is the present capacity or power capability compared to original specifications, and the remaining useful life (RUL), which is an approximation of operation life until the battery fulfills end-of-life conditions. SOH and RUL because predictability is necessary in order to manage the battery, preventive maintenance, and cost-efficient system operation. The degradation of batteries is dictated by complex electrochemical and mechanical mechanisms with dependence on the conditions of operation like temperature, rate of charge, depth of discharge and patterns of usage. These time-varying nonlinearities are very difficult to deal with through conventional estimation methods. In order to overcome these issues, a broad selection of prognostic techniques has been designed, which can be narrowed down into model-based, data-driven, and hybrid. Model-based approaches are based on physical and electrochemical models of battery behavior, providing interpretability but in most cases, these models are sensitive to the identification of accurate parameters. Machine learning and deep learning models are data-driven approaches that allow the establishment of complex degradation trends at high levels of predictive accuracy using past operational data. Hybrid frameworks strive to build the merits of the two paradigms by blending physical wisdom and data-driven flexibility. Findings: When comparing previous research on the estimation of battery health and the remaining useful life, it becomes apparent that the performance trends are similar in various methodological types. Although, there is no universal method to be used in all of the operating conditions, the literature provides definite advantages and disadvantages related to model-based, data-driven, and hybrid prognostic methods. Unique Contribution to Theory, Practice, and Policy: The article is a thorough piece of work that provides an evaluation of battery health prediction and RUL estimation approaches both in terms of their principles of operation, implementation strategies, and performance attributes.