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Accurate prediction and monitoring of the state of charge (SOC) of each cell in a battery pack are of great significance for safe driving of electric vehicles. Subject to the factors of differences between cells, temperature, and aging, the accuracy of existing SOC prediction methods may be affected in applications. This article employs a data-driven method with a proposed novel transfer learning framework considering conditional probability distribution adaptation to solve the impact of the aforementioned influencing factors on SOC prediction and obtains a favorable SOC prediction result for each cell. As experiments of actual battery pack provided by China First Automobile Work based demonstrated, the proposed method can accurately predict SOC of each cell under different influencing factors by using only one cell modeling data. Moreover, the proposed method is robust against model parameter uncertainties, sensor noise, etc. [ABSTRACT FROM AUTHOR]

Large battery packs are used in electric vehicles. Heat is generated when the battery pack is being used. Therefore, it is necessary to predict battery heat generation. An enhanced electro-thermal model is developed to describe the temperature distribution inside a battery pack. It combines the dynamic resistance model and the current distribution model. The resistance model is affected by the thermal and electrical parameters, while the current distribution model considers the interaction between cell status and current variation in the parallel branch. The proposed model can accurately predict the temperature change of cells in the pack under static and dynamic current conditions. Experiments are conducted to validate the prediction accuracy. Most of the average absolute errors (AEave) between the predicted value and test value displayed on the experimental device do not exceed 0.4 °C under static current conditions, and all of them are below 0.1 °C under dynamic current conditions. The two existing models, namely the state-of-charge (SOC)-dependent resistance model [R(SOC)] and SOC-T-dependent resistance model [R(SOC, T)], have AEave values of 1.6 and 0.54 °C when the pack is discharged at 0.5 C. In contrast, the AEave value achieved by our proposed model is 0.4 °C. Under dynamic current conditions, the maximum AEaves are 0.42 °C for the R(SOC) model, 0.26 °C for the R(SOC, T) model, and 0.16 °C for the proposed model. These results demonstrate that the proposed model provides more accurate predictions of the temperature rise inside the pack than the popular existing models. [ABSTRACT FROM AUTHOR]

The state-of-charge (SOC) estimation approaches based on the pack model can hardly provide precise estimation due to cell difference, while the approaches based on each cell cost high computation resource, which are not suitable for real-time application. The estimation error can be further enlarged without considering balancing current. The SOC estimation approach of the battery pack considering balancing current is proposed, which dynamically searches for the cell with maximum or minimum voltage, and it only needs to calculate the selected cell in every estimation cycle. Compared to the approaches based on the pack model or each single cell, this approach can achieve precise pack SOC and cost less calculation time and resource. It has been verified with ten series-connected 200 Ah Li(NiCoMn)O2 batteries. The SOC estimation error is limited to 0.3% during the charging process, and a reduction of 2.5% is achieved compared to an error of 2.8% based on the pack model. A reduction of 1% is achieved compared to an error of 1.5% based on the pack model during the discharging process. Compared to an error of 1.7% without considering balancing current, a reduction of 1.4% is achieved during the charging process. [ABSTRACT FROM PUBLISHER]