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

Claudio Rossi, Carlo Falcomer, Luca Biondani and Davide Pontara
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Claudio Rossi: Department of Electrical, Electronic and Information Engineering, University of Bologna, 40136 Bologna, Italy
Carlo Falcomer: Department of Computer Science and Engineering, University of Bologna, 40136 Bologna, Italy
Luca Biondani: Department of Electrical, Electronic and Information Engineering, University of Bologna, 40136 Bologna, Italy
Davide Pontara: Department of Industrial Engineering, University of Bologna, 40136 Bologna, Italy

Energies, 2022, vol. 15, issue 9, 1-18

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.

Keywords: lithium-ion battery; Kalman filter; equivalent circuit model; genetic algorithm (GA); state of health (SOH); online parameters estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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