Genetically Optimized Extended Kalman Filter for State of Health Estimation Based on Li-Ion Batteries Parameters

Abstract

The state of health (SOH) is among the most important parameters to be monitored in lithium-ion batteries (LIB) because it is used to know the residual functionality in any condition of aging. The paper focuses on the application of the extended Kalman filter (EKF) for the identification of the parameters of a cell model, which are required for the correct estimation of the SOH of the cell. This article proposes a methodology for tuning the covariance matrices of the EKF by using an optimization process based on genetic algorithms (GA). GAs are able to solve the minimization problems for the non-linear functions, and they are better than other optimization algorithms such as gradient descent to avoid the local minimum. To validate the proposed method, the cell parameters obtained from the EKF are compared with a reference model, in which the parameters have been determined with proven procedures. This comparison is carried out with different cells and in the whole range of the cell’s SOH, with the aim of demonstrating that a single tuning procedure, based on the proposed GA process, is able to guarantee good accuracy in the estimation of the cell parameters at all stages of the cell’s life.

State of Health (SOH) Estimation of Lithium-Ion Batteries Based on ABC-BiGRU

“…Feng Zhu et al [23] improved the speed and accuracy of SOH assessment for lithium-ion batteries by integrating unscented Kalman filtering and an enhanced unscented particle filtering algorithm, achieving a practical estimation of SOH. Claudio Rossi et al [24] utilized a genetic algorithm to optimize the covariance matrix of the extended Kalman filter to aid in SOH prediction. This method demonstrated good accuracy throughout the entire battery lifecycle.…”