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Quantifying imbalances in parallel-connected cell groups using group voltage and current
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DOI：10.1016/j.est.2026.120507.png)
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Abstract
En 中文
Insight into imbalances within a group of parallel-connected cells is critical for effective battery management but is challenging to obtain due to limited sensor data and measurement noise. This work presents a novel approach for quantifying imbalances in parallel-connected lithium-ion cells using only group-level current and voltage measurements. First, by modeling groups of two parallel-connected cells with varying capacity and resistance, we demonstrate that features of the group's differential voltage with respect to differential state of charge (dV/dz) - specifically the height and skewness of the dV/dz peak corresponding to the graphite Stage 2 phase transition - can quantify imbalance in the capacity-resistance product (CR). Furthermore, we show that dV/dz peak features can quantify current rate and SOC imbalances, as these imbalances are proportional to CR imbalance. After establishing how imbalances can be quantified using the group dV/dz peak features, we introduce a novel algorithm, which we term OCP-informed Feature Identification, that accurately and precisely estimates these features from noisy voltage data, enabling a robust diagnosis of imbalances. Finally, we analyze how the sensitivity of the dV/dz peak features changes with the number of cells in parallel, providing insight into the scalability of the proposed diagnostic approach. This work lays the groundwork for diagnosing imbalances within parallel-connected cell groups in battery modules using the limited and noisy sensor measurements typically available in the field.
Keywords:
Parallel-connected cells
Lithium-ion batteries
Differential voltage analysis
Capacity imbalance
Resistance imbalance
Current rate imbalance
State-of-charge (SOC) imbalance
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