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The battery management system in electrified transportation requires an accurate battery model for online state estimation of the battery. The parameters of the battery model depend upon state of charge, C‐rate, and temperature. A detailed battery model defined by 31 polynomial coefficients is used for determination of battery parameters. The parameter estimation is formulated as an optimisation problem and six different meta‐heuristic optimisation techniques are utilised for solving it. The efficiency of optimisation techniques is compared in terms of solution quality, computation efficiency, and convergence characteristics. Further, their performance is analysed statistically using parametric (t‐test) and non‐parametric tests (Wilcoxon test). The parameters values estimated by applying optimisation techniques are cross‐validated with value of parameters extracted using standard constant‐current pulse charge–discharge test to establish the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

The battery management system in electrified transportation requires an accurate battery model for online state estimation of the battery. The parameters of the battery model depend upon state of charge, C-rate, and temperature. A detailed battery model defined by 31 polynomial coefficients is used for determination of battery parameters. The parameter estimation is formulated as an optimisation problem and six different meta-heuristic optimisation techniques are utilised for solving it. The efficiency of optimisation techniques is compared in terms of solution quality, computation efficiency, and convergence characteristics. Further, their performance is analysed statistically using parametric (t-test) and non-parametric tests (Wilcoxon test). The parameters values estimated by applying optimisation techniques are cross-validated with value of parameters extracted using standard constant-current pulse charge--discharge test to establish the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]