双视角频谱注意力融合的电池组多故障诊断算法

刘明俊; 顾深宇; 尹敬德; 张逸凡; 董哲康; 纪晓悦

doi:10.11999/JEIT251156

双视角频谱注意力融合的电池组多故障诊断算法

doi: 10.11999/JEIT251156 cstr: 32379.14.JEIT251156

刘明俊^{1, 2},
顾深宇^{1, 2},
尹敬德^{1, 2},
张逸凡^{1, 2},
董哲康^{1, 2, ,},
纪晓悦³

1.
杭州电子科技大学电子信息学院杭州 310018
2.
全省智能汽车电子研究重点实验室杭州 310018
3.
清华大学精密仪器系北京 100084

基金项目: 博士后科学基金(2024T170463, 2024M751676)，国家自然科学基金(62206062)，浙江省优秀青年基金(LZYQ25F020005)，长三角科技创新共同体联合攻关重点项目(2023CSJGG1300)

详细信息

作者简介:
刘明俊：男，博士生，研究方向为基于数据驱动的电池多故障诊断

顾深宇：男，硕士生，研究方向为基于数据驱动的电池多故障诊断

尹敬德：男，硕士生，研究方向为电池全生命周期管理

张逸凡：男，硕士生，研究方向为基于数据驱动的电池健康状态估计

董哲康：男，教授，研究方向为面向智慧能源的神经形态计算系统研究

纪晓悦：女，助理研究员，研究方向为面向智慧能源的神经形态计算系统研究

通讯作者:
董哲康　englishp@126.com

中图分类号: TN911; TP183
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出版历程
- 收稿日期: 2025-11-01
- 修回日期: 2025-12-18
- 录用日期: 2025-12-22
- 网络出版日期: 2026-01-03

Battery Pack Multi-Fault Diagnosis Algorithm Based on Dual-Perspective Spectral Attention Fusion

LIU Mingjun^{1, 2},
GU Shenyu^{1, 2},
YIN Jingde^{1, 2},
ZHANG Yifan^{1, 2},
DONG Zhekang^{1, 2
, ,},
JI Xiaoyue³

1.
School of Electronic Information, Hangzhou Dianzi University, Hangzhou 310018, China
2.
Zhejiang Province Key Laboratory of Intelligent Vehicle Electronics, Hangzhou 310018, China
3.
Department of Precision Instrument, Tsinghua University, Beijing 100084, China

Funds: China Postdoctoral Science Foundation (2024T170463, 2024M751676), The National Natural Science Foundation of China (62206062), Zhejiang Provincial Natural Science Foundation of China (LZYQ25F020005), Ministry of Science and Technology-Yangtze River Delta Science and Technology Innovation Program (2023CSJGG1300)

摘要

摘要: 随着新能源汽车的快速发展，其使用规模不断扩大，电池组故障的概率和严重程度随之增加，迫切需要高效的故障诊断方法。近年来，尽管基于深度学习的电池故障诊断方法已取得显著进展，但现有研究在内短路(ISC)、传感器噪声、传感器漂移及荷电状态(SOC)不平衡故障的多故障下的工况的覆盖性以及故障间耦合关系的挖掘方面仍存在不足。针对既有挑战，该文提出一种双视角频谱注意力融合算法。该算法由两大核心模块组成：一是双视角分词模块，负责全链路捕捉电池组的时空信息；二是频谱注意力机制，负责非平稳特征处理与长期依赖挖掘。这种特征工程与频域分析的深度结合，有效增强了模型的故障诊断鲁棒性。该文提出的方法在联邦城市驾驶循环(FUDS)、城市测功机行驶工况(UDDS)和补充联邦测试程序(US06)3种典型工况下的诊断性能均显著优于现有主流算法，其平均精确率提升了10.98%，召回率提升了12.64%，F1分数提升了13.84%，准确率提升了13.45%。此外，该文设计并实施了系统的消融实验与鲁棒性分析，对比了各核心模块对模型整体性能的贡献机理，同时充分验证了所提方法在复杂噪声环境下的抗干扰能力与鲁棒性。该文所提出的双视角频谱注意力框架不仅提升了多故障诊断性能，也为复杂时空特征建模提供了新思路，为提升汽车安全性提供新的方案。
- 多故障诊断 /
- 锂离子电池组 /
- 频谱注意力
Abstract: Objective With the rapid growth of electric vehicles and their widespread deployment, battery pack faults have become more frequent, creating an urgent need for efficient fault diagnosis methods. Although deep learning-based approaches have achieved notable progress, existing studies remain limited in addressing multiple fault types, such as Internal Short Circuit (ISC), sensor noise, sensor drift, and State-Of-Charge (SOC) inconsistency, and in modeling the coupling relationships among these faults. To address these limitations, a multi-fault diagnosis algorithm for battery packs based on dual-perspective spectral attention is proposed. A dual-perspective tokenization module is designed to extract spatiotemporal features from battery data, whereas a spectral attention mechanism addresses non-stationary time-series characteristics and captures long-term dependencies, thereby improving diagnostic performance. Experimental results under the Federal Urban Driving Schedule (FUDS), Urban Dynamometer Driving Schedule (UDDS), and Supplemental Federal Test Procedure (US06) demonstrate that the proposed method achieves average improvements of 10.98% in precision, 12.64% in recall, 13.84% in F1 score, and 13.45% in accuracy compared with existing multi-fault diagnosis methods. Furthermore, systematic ablation studies and robustness analyses are conducted to examine the contribution of core modules to overall model performance and to validate the anti-interference capability and robustness of the proposed method under complex noise conditions. Overall, the dual-perspective spectral attention framework improves multi-fault diagnosis performance and provides a new approach for modeling complex spatiotemporal features, thereby supporting enhanced vehicle safety. Methods To improve spatiotemporal feature extraction and fault diagnosis performance, a dual-perspective spectral attention fusion algorithm for battery pack multi-fault diagnosis is proposed. The overall architecture consists of four core modules (Fig. 3): a dual-perspective tokenization module, a spectral attention module, a feature fusion module, and an output module. The dual-perspective tokenization module applies positional encoding to jointly model temporal and spatial dimensions, enabling comprehensive spatiotemporal feature representation. When combined with the spectral attention mechanism, the capability of the model to handle non-stationary characteristics is strengthened, leading to improved diagnostic performance. In addition, to address the lack of comprehensive publicly available datasets for battery pack fault diagnosis, a new dataset is constructed, covering ISC, sensor noise, sensor drift, and SOC inconsistency faults. The dataset includes three operating conditions, FUDS, UDDS, and US06, which alleviates data scarcity in this research field. Results and Discussions Experimental results indicate that the proposed method improves average precision, recall, F1 score, and accuracy by 10.98%, 12.64%, 13.84%, and 13.45%, respectively, compared with existing optimal fault diagnosis methods. Comparison experiments under different operating conditions (Table 7) support this conclusion. Conventional convolutional neural network methods perform well in local feature extraction; however, fixed-size convolution kernels are not well suited to time features with varying frequencies, which limits long-term temporal dependency modeling and global feature capture. Recurrent neural network-based methods show reduced computational efficiency when large-scale datasets are processed. Transformer-based models face constraints in spatial feature extraction and in representing temporal variations. By contrast, the proposed algorithm addresses these limitations through an integrated architectural design. Ablation experiments demonstrate the contribution of each module to overall performance (Table 8), and the complete framework improves average F1 score and accuracy by 9.30% and 9.26%, respectively, compared with ablation variants. Robustness analysis under simulated noise conditions (Table 9) shows that the proposed method achieves accuracy improvements ranging from 49.95% to 124.34% over baseline methods at noise levels from –2 dB to –8 dB, indicating strong noise resistance. Conclusions A multi-fault diagnosis algorithm for battery packs is presented that integrates dual-perspective tokenization and spectral attention to combine spatiotemporal and spectral information. The dual-perspective tokenization module performs tokenization and positional encoding along temporal and spatial axes, which improves spatiotemporal representation. The spectral attention mechanism strengthens modeling of non-stationary signals and long-term dependencies. Experiments under FUDS, UDDS, and US06 driving cycles show that the proposed method outperforms existing multi-fault diagnosis approaches, with average gains of 13.84% in F1 score and 13.45% in accuracy. Ablation studies confirm that both modules contribute substantially and that their combination enables effective handling of complex time-series features. Under high-noise conditions (–2 dB, –4 dB, –6 dB, and –8 dB), the method also shows improved robustness, with accuracy gains of 49.95%, 90.39%, 112.01%, and 124.34%, respectively, compared with baseline methods. Several limitations remain. First, the data are mainly derived from laboratory simulations, and further validation under real-world operating conditions is required. Second, the effect of fault severity on battery management system hierarchical decision making has not been fully addressed, and future work will focus on establishing a fault severity grading strategy. Third, physical interpretability requires further improvement, and subsequent studies will explore the integration of equivalent circuit models or electrochemical mechanism models to balance diagnostic accuracy and interpretability.
- Multi-fault diagnosis /
- Lithium-ion battery pack /
- Spectral attention

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图 1 实验平台

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图 2 FUDS工况下电池组特征曲线

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图 3 本文所模型的算法框架图

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图 4 频谱注意力模块

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图 5 模型在不同超参设置下的F1分数

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图 6 不同工况下的聚类图

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图 7 不同工况下各模型混淆矩阵对比

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表 1 实验设备参数

设备名称	类型	参数
高精度数字采集设备	KEYSIGHT 34980A	采样频率：10 Hz；测量精度：0.1%
可控双向直流电源	ITECH IT6012-500-80	模式：恒压模式初始电压：4.2 V
串联电池组	ITR18650-2600P	标称容量：2 000 mAh

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表 2 电池组不同故障类型信息汇总

故障名称	类型介绍	测试方法	评判标准	参数¹
正常	电池处于正常工作状态	无	无	无
ISC故障	电池内部短路并发热	并联不同阻值的电阻表征故障严重程度	电阻接入时段标记为故障	1 Ω/5 Ω/10 Ω
传感器噪声故障	传感器低频扰动	叠加最大幅度为0.1 V的随机扰动	施加区间均标记为噪声故障	1 Hz
传感器漂移故障	传感器随机波动	两端施加固定幅度的低频干扰电压	施加区间均标记为漂移故障	0.1 V
SOC不平衡故障	低容量电池引发	更换低SOC的单体电池实现	运行周期内均标记为故障	0.2 V
注：上标¹表示所有故障类型参数，主要选自参考文献[21, 22, 31–33]

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表 3 FUDS, UDDS, US06工况下训练和测试数据

名称	数据量
名称	训练集	测试集
正常	13707 × 3	2000 × 3
ISC故障	13707 × 3	2000 × 3
传感器噪声故障	13707 × 3	2000 × 3
传感器漂移故障	13707 × 3	2000 × 3
SOC不平衡故障	13707 × 3	2000 × 3
总计	68535 × 3	10000 × 3

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1 双视角频谱注意力融合算法

算法：双视角频谱注意力融合算法
输入: 电池组归一化时序数据 Xenc (BatchSize, SeqLen, 　NumCells)
输出: 预测类别概率 Output
1: T_L = DataEmbedding(Xenc)
2: y_t = FreqEncoder(T_L)
3: Xperm = Permute(Xenc [0, 2, 1])
4: S_L = PatchEmbedding(Xperm)
5: y_s = FreqEncoder (S_L)
6: y = Concatenate(y_t, y_s)
7: Output = Projection(y)
8: return Output

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表 4 模型超参数设置

超参数	值
滑动窗长度	300
Batch_size大小	64
Epoch大小	20
d_model	64
d_feedforward	128
e_layers	2
学习率	0.001
优化器	Adam

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表 5 对比方法模型超参数设置

模型	超参数设置
文献[21]	Batch_size：128；epochs：20；滑动窗长度：300；BatchNorm：True；Number：1000；Normal：True
文献[25]	网络深度10层特征提取 (5×Conv, 5×Pool) + 2层分类(FC, Softmax)；卷积核大小64, 32, 32, 16, 16(按顺序)；池化核数量16, 64, 128, 128, 128(按顺序)；池化策略第1次: 16 (步幅)，后续4次: 2(步幅)
文献[26]	使用野马优化器寻找最优参数
文献[27]	Batch_size：16；Epoch：50；学习率：0.001；权重衰减：0.0001

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表 6 不同工况下的对比实验(%)

模型	FUDS				UDDS				US06
模型	精确率	召回率	F1分数	准确率	精确率	召回率	F1分数	准确率	精确率	召回率	F1分数	准确率
文献[21]	92.67²	92.42²	91.67²	91.41²	91.22³	90.13³	90.06²	90.12³	93.41²	93.48²	92.86²	92.45²
文献[25]	79.63	78.88	78.40	78.87	79.53	79.90	77.37	77.75	75.42	75.05	75.18	75.03
文献[26]	90.08³	84.89	83.86	84.89	89.76	85.17	84.07	85.05	89.01	83.44	82.10	83.44
文献[27]	89.77	90.98³	89.42³	88.71³	93.16²	90.40²	89.61³	90.40²	90.69³	93.10³	91.50³	90.32³
本文模型	97.47¹	97.46¹	97.44¹	97.45¹	98.15¹	98.08¹	98.10¹	98.07¹	95.56¹	95.31¹	95.06¹	94.89¹
注：上标1,2,3分别代表第1、第2和第3。

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表 7 不同工况下的消融实验(%)

模型	FUDS				UDDS				US06
模型	精确率	召回率	F1分数	准确率	精确率	召回率	F1分数	准确率	精确率	召回率	F1分数	准确率
时间+频谱注意力	91.97³	90.18³	89.88³	89.99³	91.22³	90.13³	90.06³	90.12³	92.01³	90.79	90.75³	90.79³
空间+频谱注意力	81.69	86.77	82.39	81.05	83.41	86.10	82.56	82.07	85.98	91.57³	88.23	85.79
时间+空间视角	92.79²	92.35²	91.70²	91.49²	94.79²	93.90²	93.69²	93.67²	94.55²	94.29²	94.42²	94.29²
本文模型	97.47¹	97.46¹	97.44¹	97.45¹	98.15¹	98.08¹	98.10¹	98.07¹	95.56¹	95.31¹	95.43¹	94.89¹
注：上标1,2,3分别代表第1、第2和第3。

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表 8 噪声鲁棒性实验(%)

模型	SNR
	FUDS				UDDS				US06
	–2 dB	–4 dB	–6 dB	–8 dB	–2 dB	–4 dB	–6 dB	–8 dB	–2 dB	–4 dB	–6 dB	–8 dB
文献[21]	80.60²	74.11²	60.22²	52.43²	79.73²	75.50²	64.75²	48.92²	78.66²	68.89²	57.14²	48.32²
文献[25]	43.31	28.77	20.85	19.56	41.11	27.33	22.02	21.33	42.20	31.11	22.92	20.97
文献[26]	55.26	37.09	26.39	19.97	58.97	40.41	28.74	19.62	59.74	38.82	27.94	18.05
文献[27]	57.16³	43.58³	43.54³	24.76³	59.83³	46.98³	46.24³	23.71³	62.76³	42.23³	40.96³	25.40³
本文模型	85.02¹	81.01¹	71.05¹	55.57¹	81.86¹	74.01¹	72.16¹	58.91¹	89.41¹	81.19¹	66.48¹	52.34¹
注：上标1,2,3分别代表第1、第2和第3。

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