Battery aging is an inevitable macroscopic phenomenon in the use of the battery, which is characterized by capacity decline and power reduction. If the charging and discharging strategy does not adjusted with the aging state, it is easy to cause battery abuse and accelerate the decline. In order to avoid this situation, the aging model with consideration of the battery degradation is coupled into the pseudo-two-dimensional (P2D) model. An aging effect-aware finite element model that can describe battery physical information accurately is presented in this paper. The model parameters are divided into four parts: structure parameters, thermodynamic parameters, kinetic parameters and aging parameters. The identification experiments are designed based on the characteristics of these types of parameters. The decoupling and parameter identification methods of kinetic parameters according to the response characteristics of each parameter under specific excitation, and state of charge (SOC) partitioned range identification technology of aging parameters are proposed and verified. Finally, the aging effect-aware model and the identification parameters are verified under constant current (CC) and different dynamic conditions with different charge rate (C-rate). And the ability of the proposed model to track the aging trajectory in the whole life cycle is verified under various cycle conditions. The proposed model can be applied to aging mechanism analysis and health management from point of inner properties of the batteries.
The inconsistency of cells in the battery pack is one of the main causes of battery failure. In practical applications, the port voltage is an important parameter that is easy to obtain and can characterize the inconsistency of cells. In this paper, a fault diagnosis method based on piecewise dimensionality reduction and outlier identification is proposed according to the voltage inconsistency of cells in the battery pack. This method uses piecewise aggregate approximation (PAA) algorithm with a shift factor to reduce the dimension of the cell voltage time series, after which a deletion mechanism is designed based on the clustering algorithm and outlier identification to calculate the clustering quality after deleting each cell, reflecting the deviate degree of each cell. In addition, a safety management strategy is designed based on the Z-score method, and an abnormality coefficient is set to evaluate the inconsistency of cells. The effectiveness of the proposed diagnosis method is verified by the monitoring voltage data of two real-world electric vehicles. The verification results show that the method can not only detect the inconsistency before the failure of the faulty cell in the battery pack in advance, but also reduce the risk of computational explosion caused by the voltage time series and accurately locate the faulty cell.
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