Abstract：In practical applications, batteries are typically assembled into packs by connecting dozens to thousands of cells in series, parallel, or hybrid topologies to meet diverse operational demands. However, after multiple charge-discharge cycles, battery packs undergo irreversible physicochemical changes, leading to overall degradation of their lifespan. This process also exacerbates cell inconsistencies, adversely affecting pack performance. Consequently, reliably estimating the pack’s state of health (SOH) presents significant challenges. Additionally, due to economic constraints and wiring complexity, battery packs still cannot provide comprehensive monitoring of all cells, making it difficult to estimate the battery pack’s SOH due to cell inconsistencies. Therefore, this paper proposes an inconsistency characteristic extraction and SOH estimation method for lithium-ion battery packs under limited monitoring conditions. First, features are identified by analyzing the charging-voltage curves of monitored cells and examining correlations between cell features and the overall health of the battery pack, including HI1: open-circuit voltage after constant-current discharge and HI2: the ratio of average charging voltage to charging capacity. Subsequently, an equivalent circuit model of the cell is established using electrochemical impedance spectroscopy (EIS). The correlation between cell features and the internal parameters of the equivalent circuit is examined, and the intrinsic mechanism linking these features to cells is examined from an electrochemical perspective. The HI1 parameter of cells shows a strong correlation with the SEI film's equivalent resistance RSEI, whereas HI2 shows a strong correlation with the ohmic resistance R0. Second, based on the extracted features of the monitored cells, the health features of unmonitored cells are estimated. Subsequently, the aging features of the battery pack are constructed by leveraging the cumulative relationship between cell-voltage and battery-pack-voltage features. The battery pack’s inconsistency features are determined by comparing each cell's health characteristics with the average cell health characteristics. Based on the constructed aging and inconsistency features, feature selection engineering is employed to rank and screen the optimal feature subset. Finally, an integrated support vector machine (SVM)-AdaBoost model is built to estimate the SOH of battery packs with varying degrees of consistency, using the optimal subset of features as inputs. Experimental results demonstrate that this model not only effectively captures capacity degradation trends but also exhibits excellent adaptability under varying levels of inconsistency within the battery pack. Even with limited data, the RMSEs for the four battery packs were 0.30%, 0.43%, 0.35%, and 0.37%, respectively, indicating high accuracy. In addition, the proposed model's sensitivity to the number and placement of monitored cells is analyzed. Deploying two voltage sensors achieves an effective balance between estimation accuracy and monitoring coverage. When sensors are placed in the middle cell, estimation error is minimized, leading to a more accurate representation of the battery pack’s overall health. In summary, the proposed model effectively improves the estimation accuracy of battery-pack SOH and provides valuable recommendations for sensor configuration in large-scale battery packs.
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2026, Vol. 41 
