Dynamic Distribution Adaptation with Higher-Order Moment Matching for Battery Pack Multi-Fault Diagnosis

GUAN Siwei; HE Zhiwei; DONG Zhekang; TONG Hongtao; MA Shenhui; GAO Mingyu

doi:10.11999/JEIT250226

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GUAN Siwei, HE Zhiwei, DONG Zhekang, TONG Hongtao, MA Shenhui, GAO Mingyu. Dynamic Distribution Adaptation with Higher-Order Moment Matching for Battery Pack Multi-Fault Diagnosis[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250226

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GUAN Siwei, HE Zhiwei, DONG Zhekang, TONG Hongtao, MA Shenhui, GAO Mingyu. Dynamic Distribution Adaptation with Higher-Order Moment Matching for Battery Pack Multi-Fault Diagnosis[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250226

Citation:

GUAN Siwei, HE Zhiwei, DONG Zhekang, TONG Hongtao, MA Shenhui, GAO Mingyu. Dynamic Distribution Adaptation with Higher-Order Moment Matching for Battery Pack Multi-Fault Diagnosis[J]. Journal of Electronics & Information Technology. doi: 10.11999/JEIT250226

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Dynamic Distribution Adaptation with Higher-Order Moment Matching for Battery Pack Multi-Fault Diagnosis

doi: 10.11999/JEIT250226 cstr: 32379.14.JEIT250226

GUAN Siwei¹,
HE Zhiwei^{1, 2
,
,},
DONG Zhekang^{1, 2},
TONG Hongtao¹,
MA Shenhui¹,
GAO Mingyu^{1, 2}

1.
School of Electronics Information, Hangzhou Dianzi University, Hangzhou 310018, China
2.
Provincial Key Laboratory of Intelligent Automotive Electronics Research, Hangzhou 310018, China

Funds: The National Key Research and Development Program of China (2023YFB2504100), The National Natural Science Foundation of China (62171170, 62227802)

Received Date: 2025-04-01
Rev Recd Date: 2025-09-12

Available Online: 2025-09-16

Abstract

Abstract

Objective Electric vehicle battery pack fault diagnosis is challenged by diverse operating conditions, the scarcity of fault data, and the domain shift caused by the non-Gaussian distribution of battery features. Conventional fault diagnosis methods struggle to address multiple fault types, lack the capability for fault isolation, and fail to account for distribution shifts between training and test data. Domain adaptation approaches enable robust multi-fault diagnosis across operating conditions without relying on accurate cell models or abundant labeled data. However, current methods remain limited. (1) They typically assume that aligning global and fine-grained subdomain distributions is equally important, which may not hold in practice. (2) Knowledge transfer cannot be fully achieved by aligning only low-order statistical features; higher-order statistical features are needed to capture the non-Gaussian characteristics of battery discharge profiles. To address these issues, a method is proposed in which global domains and subdomains are dynamically aligned while higher-order statistical moments are extracted to represent complex non-Gaussian distributions, thereby achieving fine-grained domain alignment and effective knowledge transfer. Methods This study proposes a dynamic distribution adaptation method with higher-order moment matching for multi-fault diagnosis of battery packs. The approach consists of three components: (1) Dynamic distribution adaptation. A feature extractor based on a one-dimensional convolutional network with residual connectivity and a multilayer perceptron classifier is constructed. The global distributions of source and target domains are aligned using Maximum Mean Discrepancy (MMD), while subdomain distributions of similar faults are aligned using Local Maximum Mean Discrepancy (LMMD). A dynamic factor is introduced to automatically adjust the relative weights of global and local alignment according to the inter-domain discrepancy, thereby adapting to distribution shifts under different operating conditions. (2) Higher-order moment matching. To address the non-Gaussian characteristics of battery data, higher-order statistical moment matching is incorporated into MMD. Computational complexity in high-dimensional tensors is reduced by random sampling, which enables fine-grained domain alignment across multi-order statistics and enhances the transferability of non-Gaussian distribution features. (3) Multi-fault diagnosis with domain adaptation. Experimental data from three standard vehicle operating conditions are used to jointly optimize classification loss and domain adaptation loss. This enables the diagnosis of multiple faults, including internal short circuit, sensor drift/noise, and battery inconsistency, across operating conditions while reducing reliance on manual annotation. By dynamically integrating global and local feature alignment, the method improves generalization performance under complex operating conditions and non-Gaussian distribution scenarios. Results and Discussions Systematic experiments validate the superiority of the proposed dynamic distribution adaptation with higher-order moment matching for multi-fault diagnosis in electric vehicle battery packs. As shown in Table 3, the results from six transfer tasks under three operating conditions demonstrate that the proposed method achieves an average F1 score of 95%, which is 13.3% higher than that of the best-performing baseline model (DSAN). The confusion matrix in Fig. 6 indicates that the method achieves the lowest misclassification rate in distinguishing similar faults. Feature visualization results (Fig. 7) show that the method effectively reduces cross-domain feature distances of similar faults by dynamically adjusting the weights of global and local distribution alignment. Moreover, it successfully captures non-Gaussian discharge characteristics through higher-order moment matching, thereby achieving fine-grained domain adaptation. In terms of efficiency, the proposed method attains an average diagnosis time of 0.404 3 seconds (Table 4), satisfying real-time on-board application requirements. Nonetheless, optimization of computational resource consumption remains necessary for deployment on edge devices. Importantly, the method does not require labeled data from the target domain and overcomes the generalization bottleneck of traditional methods under domain shift and non-Gaussian conditions. However, some cross-domain features (Fig. 7) are not completely overlapped, and lightweight model design is still required for practical implementation on edge devices. Conclusions The battery pack is recognized as a critical component of electric vehicles, and reliable multi-fault diagnosis is regarded as essential for safe operation. Considering the unknown and diverse nature of real operating conditions, fault diagnosis is investigated across three driving cycles: UDDS, FUDS, and US06. A dynamic distribution adaptation with higher-order moment matching (DDAMD) is proposed for diagnosing multiple faults in series-connected battery packs. The method dynamically evaluates the relative importance of conditional and marginal distributions to align source and target domains, while non-Gaussian features from charge–discharge curves are effectively extracted for fine-grained alignment. Experimental results across six transfer tasks confirm that DDAMD achieves the highest diagnostic performance. Detailed analyses present diagnostic accuracy for each fault type as well as the diagnostic speed, while feature visualization further improves interpretability by demonstrating how the algorithm extracts domain-invariant and discriminative fault features across domains. Future research will extend this work in two directions: (1) incorporating additional operating conditions and a broader set of fault categories, and (2) exploring transfer tasks from simulation to real-world applications to facilitate data acquisition and labeling.
- Battery fault diagnosis,
- Electric vehicles,
- Dynamic distribution adaptation,
- Higher-order moment matching

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References(35)

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