A state of health estimation method for series-connected lithium-ion battery packs: From narrow interval estimation to exact point matching

Series-connected lithium-ion battery packs are key energy units in electric and storage systems, and accurate state-of-health (SOH) estimation is important for safe operation and capacity management. However, under the coupled effects of operating-condition variation and inconsistency evolution, point-estimation models are prone to mismatch. Interval estimation can describe this uncertainty, but existing methods often produce overly wide intervals or biased interval centers. In addition, single-dimensional health indicators (HIs) are often insufficient to characterize the complex degradation behavior of battery packs. To address these issues, this paper proposes an SOH estimation method for series-connected lithium-ion battery packs that combines multi-dimensional HIs construction with interval optimization. First, a graph neural network with multi-distance adaptive fusion is used to quantify inconsistency at multiple scales, and multi-dimensional HIs are constructed by combining inconsistency-related, incremental-capacity, and voltage-response features. Second, the Bayesian neural network is used to generate the SOH confidence interval, and the multi-kernel Gaussian process regression model is used to establish the reverse mapping from SOH to HIs. Within the estimation interval, candidate SOH values are iteratively optimized by joint statistical consistency, so that interval compression and interval-center correction can be achieved simultaneously. Experimental results on measured battery-pack data show that the proposed method achieves a mean absolute error below 1.57% and reduces interval width by 75.21% compared with the baseline method. These results demonstrate that the proposed method can effectively mitigate model mismatch and improve pack-level SOH estimation performance.