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Table 2 Comparison of battery modeling, state estimation, and RUL prediction methods

From: Application of Digital Twin in Smart Battery Management Systems

Category Methods Advantages Disadvantages
Battery modeling ECM Simple parameter identification, small model computation and good real-time performance Lack of physical meaning
Electrochemical model High precision, clear physical meaning Large amount of calculation, model parameterization difficult, extreme conditions not applicable
DDM High precision, suitable for dealing with nonlinear problems Large amount of calculation, high dependence on training data
SOC estimation Ampere-hour integral Simple calculation, low cost, good real-time performance High accuracy of sensors and accurate initial SOC
Model-based method High precision, strong adaptability and real-time performance High dependence on models
Adaptive filtering method Reducing the influence of sensor noise, high precision High computational cost
Data-driven method High precision, suitable for dealing with nonlinear problems Large amount of computation
SOH estimation SOC-SOH joint estimation High precision High dependence on models
ICA/DVA High precision, can react to the internal mechanism of the battery The operation is difficult and time-consuming
Data driven method The model is simple and suitable for different working conditions A lot of data is needed, low efficiency of model updating
RUL prediction Empirical prediction method Simple process and less computation Sensitive to the fluctuation of sample data, the results are easy to diverge.
Filter prediction method Reducing the influence of sensor noise, high precision High dependent on the accuracy of empirical models
Time series forecasting No need to consider the rationality of the model High dependence of training data