Battery Fault: A Comprehensive Dataset and Benchmark for Battery Fault Diagnosis
Authors: Qingdi Liu
Yan Fu
Lishuo LIU
Yanke LIN
Jin Xin
Jianfeng Zhang
Cheng Hao Liu
Lujia Pan
Dongxu Guo
Yuejiu Zheng
Qiang Li
Venue: ICLR 2026 Poster (2026)
Keywords: Lithium-ion batteries
Fault diagnosis
Benchmark dataset
Generative modeling
Time series analysis
Primary area: datasets and benchmarks
TL;DR: CH-BatteryGen, the first large-scale real-world EV battery fault dataset, enables classification and severity grading.

Abstract: With the accelerated popularization of electric vehicles (EV), battery safety issues have become an important research focus. Data-driven battery fault diagnosis algorithms, built on real-world operational data, are critical methods for reducing safety risks. However, existing battery datasets have limitations such as insufficient scale, coarse-grained labels, and lack of coverage of real-world operating conditions, which seriously restrict the development of data-driven fault diagnosis algorithms. To address these issues, this paper introduces a large-scale benchmark dataset named CH-BatteryGen, which is, to the best of our knowledge, the first EV battery system fault diagnosis dataset based on real-world operating conditions. This dataset integrates real on-board operation data with mechanism-constrained generative modeling technology, balancing authenticity and scalability. It covers two mainstream battery chemistries, namely nickel-cobalt-manganese (NCM) lithium batteries and lithium iron phosphate (LFP) batteries, and involves charging, discharging, and operation data of 1000 electric vehicles. It provides four fault labels (normal, self-discharge, high-resistance, low-capacity) and three severity level annotations, supporting two benchmark tasks: fault classification and fault grading. Through systematic validation using traditional machine learning methods (random forest (RF), support vector machine (SVM)) and deep learning models (long short-term memory (LSTM), convolutional neural network (CNN)), the results show that the CNN model performs best in the fault classification task, achieving an F1-score of 0.9280 in the LFP discharging scenario; in the fault grading task, the F1-score reaches 0.8813. The CH-BatteryGen dataset has been open-sourced, aiming to provide a standardized evaluation platform for battery fault diagnosis algorithms, promote research development in this field, and contribute to the transformation of sustainable transportation systems.

Published as a conference paper at ICLR 2026
BATTERY FAULT: A COMPREHENSIVE DATASET AND BENCHMARK FOR BATTERY FAULT DIAGNOSIS
Qingdi Liu"
Yan Fu
Lishuo Liu
Yanke Lin
Xin Jin
Jianfeng Zhang
Chenghao Liu
Lujia Pan"
Dongxu Guo
Yuejiu Zheng"
Qiang Li"
a School of Mechanical Engineering, University of Shanghai for Science and Technology.
Shanghai, 200093, China
b Central Research Institute, 2012 Labs, Huawei Technologies Co., Ltd., Shenzhen, 518129, China
Chima Automotive Engineering Research Institute Co., Ltd. (CAERI), Chongging, 401
China
ABSTRACT
With the accelerated popularization of electric vehicles (EV), battery safety is- sues have become an important research focus. Data-driven battery fault diag- nosis algorithms, built on real-world operational data, are critical methods for reducing safery risks. However, existing battery datasets have limitations such as insufficient scale, coarse-graines labels, and lack of coverage of real-world op- erating conditions, which seriously restrict the development of data-driven fault diagnosis algorithms. To address these issues, this paper introduces large-scale benchmark dataset named CH-BatteryGen, which is, to the best of our knowl- edge, the first EV battery system fault diagnosis dataset based on real-world operating conditions. This dataset integrates real on-board operation data with mechanism-coostrainee generative modeling technology. balancing authenticity and scalability. It covers two mainstream battery chemistries, namely nickel- mabnann-a (NCM) lithium batteries and lithium iron phosphate (LFP) bat- teries, and involves charging, discharging, and operation data of 1000 electric hicles. It provides four fault labels (normal, self-discharge, high-resistance, low- capacity) and three severity level annotations, supporting two benchmark tasks: fault classification and fault grading. Through systematic validation using tra- ditional machine learning methods (random forest (RF), support vector machine (SVM)) and deep learning models (long short-term memory (LSTM), convolu- tional neural network (CNN)), the results show that the CNN model performs best in the fault classification task, achieving an Fl-score of 0.9280 in the LFP dis- charging scenario; the fault grading task, the F1-score reaches 0.8813. The CH-B TH-BatteryGen dataset has been open-sourced, aiming to provide a standardized evaluation platform for battery fault diagnosis algorithms, promote research de- velopment in this field, and contribute to the transformation of sustainable trans- portation systems.
INTRODUCTION
1.1 RESEARCH BACKGROUND AND SIGNIFICANCE
In recent years, green transportation and low-carbon mobility have been widely recognized by gov- ermments worldwide as crucial pathways to achieving carbon peaking and carbon neutrality tar- gets (Chen al., 2024). Against this backdrop, electric vehicles (EVs) and energy storage systems have experienced rapid development, with their safe and reliable operation heavily dependent on lithium-ion batteries (Lai al., [00TT. However, battery faults may lead severe consequences. For instance, internal short circuits or thermal runaway can trigger fires and even explosions, posing direct threats to human safety (Qiao et al. 2025). At the same time, battery failures can significantly
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impair the driving range of EVs: when the battery capacity decays below 80%, the range typically decreases by more than 20%, thereby directly affecting user experience and market acceptance. Therefore, accurate battery fault diagnosis is not only key means of ensuring safe operation and reducing economic risks but also enables early warning to extend battery lifespan, reduce resource waste, and further promote the sustainable development of EVs and energy storage systems.
1.2 THIS WORK AND CONTRIBUTIONS
To address the limitations of existing battery datasets- insufficient scale, coarse-graings labels, limited operating condition coverage, and the lack of unified benchmarking framework- this paper proposes and constructs an Al-generated dataset for new energy vehicle power batteries, named CH- CH--BateryGerr Based on this dataset, we conduct systematic studies, and the main con- tributions are summarized as follows:
Construction of a large-scale, multi-dimensional dataset: For the first time, we integrate real on-board operational data with generative modeling methods build a comprehen- sive dataset covering 1,000 EVs and two mainstream chemistries (nickel-cobalt-mangee) (NCM) and lithium iron phosphate (LFP)). The dataset contains multi-dimensional time- series information such voltage, current, temperature, and state of charge (SOC), and is annotated with four fault labels-"normal, "normal," "high internal resistance," " "low capacity," and "self-discharge." This overcomes the limitations of existing public datasets that are mostly constrained to state-of-healts (SOH) or binary labels.
Establishment of' a unified benchmarking framework: We design a multi-task bench- mark system encompassing both fault classification and fault grading, systematically eval- uating traditional methods such as random forest (RF) and support vector machine (SVM), as well as deep learning models such as long short-term memory (LSTM) and convolutional neural networks (CNN). Results demonstrate that while traditional methods are limited in handling complex tasks, CNN consistently achieves superior performance across different chemistries and operating conditions, exhibiting stronger robustness and generalization.
Revealing cross-scenario sensitivity: Experiments show that model performance varies significantly across different chemistries (NCM and LFP) and operating modes (charging and discharging). In particular, traditional methods experience more than 20% drop in F1-score under discharging conditions, while deep learning models exhibit smaller degra- dation, indicating stronger robustness to scenario changes.
In summary, the proposed CH- CH-BatteryGen dataset and benchmark framework not only fill the gap in battery fault diagnosis data and standards but also provide reproducible baseline for subsequent algorithm optimization and engineering applications under complex operating scenarios.
RELATED WORKS
Existing public datasets mainly focus on battery state of health (SOH) and remaining useful life (RUL), while dedicated datasets for fault diagnosis remain scarce. For example, the EV EVBattery dataset built from real on-board data and contains more than 1.2 million charging segments with multi-dimensional time-series information such as voltage, current, and temperature. However, it only provides binary labels ("normal/abnormall), which cannot capture fine-grained patterns such as lithium dendrite growth internal short circuits (He et al. 2022). The BatteryML platform integrates 383 cycling records from seven public datasets to support model development, but its la- bels are limited to capacity degradation levels, without covering specific fault types (Zhang al., 2023). The BatteryLif dataset spans multiple chemistries including zinc-ion and sodium-ion, and offers 421 Sharging/discharrg sification of "capacity below 80%", which is insufficient for complex diagnostic tasks (Tan et al.) 2025). In addition, datasets such as the NASA battery aging archive (Saha et al.) 2008) and the HNEI dataset (Devie et al. 2018) are widely used, but they suffer from large discrepancies between testing conditions and real on-board scenarios, and they lack key information such as temperature
protocols, yet annotations are restricted coarse coarse-grained clas-
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distributions and cell consistency. Overall, current datasets fall short in terms of label granular- ity and operating condition coverage, limiting their ability to support high-precisee fault diagnosis research.
At the algorithmic level, traditional approaches mainly rely on handcrafted features and machine learning models (Qiao al., 2024bb Sun et al. 2022). For instance, Ren al. (Ren et al. 2020) applied SVM to extract frequency-doman features of voltage fluctuations for high-resistance detec- tion, achieving 92% accuracy on a laboratory dataset, but the performance dropped by more than 15% under varying temperature conditions. Xue et al. (Xue el al. 2021) proposed a random forest- based self-discharge detection method using slope variations of current-voltage curves, yet exhib- ited a misclassification rate of up to 20% due to cell inconsistency. Overall, such methods strongly depend on feature engineering and lack generalization et al. 2022). By contrast, deep learning has significantly improved diagnostic performance through automatic feature extraction. Hong et al. (Hong et al.) 2019) employed LSTT to capture abnormal voltage fluctuations, enabling early warning of internal short circuits up to 50 cycles in advance with an AUROC of 89%. Ma et al. (Ma et al. 2022) combined GRU with an attention mechanism, improving the recall rate of high-resistance detection to 94%. Li et al. (Li et al., 2020) utilized a three-layer CNN to analyze surface temperature data, achieving an average of 120 seconds ad- vance warning for thermal runaway. However, most of these studies based proprietary or self-collected datasets, and lack standardized experimental design, making cross-comparison among algorithms difficult. For example, Deng al. (Deng & Hooi 2021) achieved 71.8% AUROC with a GDN model on EVBattery, but performance dropped to 62.3% on BatteryML, highlighting the strong influence of dataset differences on algorithm evaluation.
multi-fault coupling scenarios (Huang
Despite significant progress, the benchmarking system for battery fault diagnosis remains incom- plete. Some studies only report overall accuracy. For example, Liu et al. (Liu al. 202T) reported 95% accuracy in nigh-resistance detection, but overlooked the fact that fault samples accounted for only 5%, leading to an actual missed detection rate as high as 30%. Other studies rely excessively on idealized laboratory data. Han et al. (Han et al. [022] validated CNN model using noise- free voltage data, without considering sensor noise or missing data, which weakened the model's robustness in real on-board scenarios. More critically, there is currently unified benchmarking framework for fault diagnosis. Most existing works adopt metrics inherited from SOH estimation, such RMSE, whereas fault diagnosis requires fine-gre ine-grained evaluation metrics such confusion matrices and weighted Fi-scores (Zhang et al., 2025) Li et al.) (024).. In addition, the diversity of operating conditions is often neglected. For instance, Shen al. (Shen & Kwok) 2023) reported that an LSTM model achieved an AUROC of 90% under 1C slow charging, but the performance sharply dropped to 65% under 3C fast charging, underscoring the inadequacy of condition coverage. Over- all, the lack of unified and reproducible benchmarking system has become a critical bottleneck restricting the engineering application of battery fault diagnosis (Zhou & Zhang 2024).
3 DATASET DESCRIPTION
The publicly released CH-BatteryGen dataset a generated dataset. Due to commercial confiden- tiality and data-security restrictions associated with real fleet data, raw fault-labeled operational telemetry cannot be directly released. Instead, large-scale real EV data are used for calibration, pa- rameter extraction, statistical constraint modeling, and internal validation of the generation pipeline. The released dataset is generated under these physics-informet and data-constrained mechanisms, ensuring realistic operational patterns while complying with industrial data-sharing regulations. An internal realism validation protocol and distributional comparison between generated and real fleet data are provided in Appendix
3.1 DATA SOURCES
CH-BatteryGen is constructed based on large-scale real-world EV operational data, combined with Al generative models and electrochemical mechanism models to form a generation framework. The final output covers two mainstream battery chemistries, LFP and NCM. The dataset spans the full spectrum of operating states from normal to faulty, where fault modes include three representative failures: self-discharge, high internal resistance, and low capacity. Without requiring additional
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preprocessing, the dataset can be directly applied to the training and validation of downstream algo- rithms.
Regarding the data generation methods, different strategies are adopted for LFP and NCM batteries, with detailed descriptions provided in Appendix [B] A brief overview is as follows: LFP batteries modeled using series of multiple first-order RC equivalent circuits. The charging and dis- charging currents generated by Diffusion-TS (Yuan & Qiao 2024) are used as inputs, and through the simulation of ohmic drop. polarization effects, and hysteresis characteristies, the mapping from current to voltage time-series points is realized. For NCM batteries, a discrete convolution wavelet transform (DCWT) employed to construct the mapping model. Similarly, generated currents are used as inputs, and a three-step process- "baseline calibration, feature matrix solution, and voltage mapping" i-s applied to generate voltage sequences. Leveraging the decomposition and reconstruc- tion capabilities of DCWT, the dynamic response of voltage to current is accurately simulated (Yan) et al. 202T). Compared with real testing data, the average deviation of the generated single-cell voltage is within 10 mV, with the maximum deviation not exceeding 30 mV, effectively reproducing the voltage characteristics of actual faults.
3.2 DATASET SCALE AND CORE FIELDS
The dataset comprises samples from 1000 EVs, including 500 vehicles equipped with NCM batter- ies and 500 vehicles equipped with LFP batteries. Each vehicle contains 10 charging segments and 10 discharging segments, sampled at a frequency of 10 s/point, with each segment lasting 30-60 minutes on average. The dataset covers battery packs with 28/92/96/124 12 cells, operating under am- bient temperatures ranging from - C to 45ºC For each battery chemistry (NCM and LFP), the dataset contains 400 normal vehicles, 30 high-resistance fault vehicles, 30 low-capacity fault vehi- cles, and 40 self-discharge fault vehicles. The dataset is stored in a standardized format, with core fields accessible via mainstream analysis tools such Python and MATLAB. The field definitions are summarized in [ and representative data samples are shown in Fig. [
For clarity, we distinguish three levels of data granularity. A vehicle corresponds to one battery pack instance with a single assigned fault label. A segment corresponds to one continuous charging or discharging record extracted from a vehicle. Severity labels are defined only for faulty vehicles and are derived from cell-level parameter statistics within each battery pack. In segment-level ex- periments, individual segments are treated as samples. In vehicle-level experiments, all segments originating from the same vehicle are grouped into the same data partition to avoid potential infor- mation leakage.
Table 1: Dataset field information
Data
Meaning
Precision
TIME
Timestammp
CHARGE STATUS
Charge (1: charging, driving/standing)
SUM_VOLTAGE
Total voltage [V]
0.1
SUM.CURRENT
Total current [A]
0.1
SOC
State of charge
MAX.CELL.VOL CELL.VOLT
Maximum cell voltage [V]
0.001
MIN,CELL. VOLT
Minimum cell voltage [V]
0,001
MAX_TEMP
Maximum temperature ('C)
1
MIN TEMP
Minimum temperature [ C]
VOLT.N
Cell voltage N [V]
0:001
The dataset incorporates fault samples based on well-defined electrochemical mechanisms. Differ- ent fault severity levels are evaluated by considering the 95th percentile parameters within bat- tery pack (e.g.. the capacity/resistance of 96 cells, excluding outliers), ensuring consistency in fault definitions. The detailed classification and sample distribution are summarized in Table 2 For a comprehensive summary of dataset composition and label taxonomy, please refer to Appendix A
Taking LFP batteries as an example, vehicle samples at the most severe fault levels under three representative fault conditions-sele self-discharge, high internal resistance, and low capacity- well as the normal state were selected for comparison. The corresponding cell voltages are illustrated in Fig. 2 highlighting the distinctive features of different fault types.
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LFP charge
LFP charge
LFP charge
27.0
LFP charrge
Sum vollage())
Sum cunrentia)
Max. temppt"))))
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500
418
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2500
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5000
2500 Timets)
5000
2500 Timo(s)
5000
Time(s)
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I.FP discharge
LFPP discharge
LFP discharge
LFP discharge
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Sum curreei(()
Max Aax sompen (ohia
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100
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400
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2,500 Time(()
2500
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2500
5000
Time(s)
Time(s)
Time(s)
(e)
(I)
(h)
Figure 1: Display of basic battery data samples in CH-BatteryGen. Subplots (a)-(d) show the total voltage, total current, SOC, and maximum temperature during charging, while (e)-(h) illustrate the corresponding fields during discharging.
Table 2: Fault types and sample distribution.
Fault Type
Fauli Indicator
Evaluation Index
fault index
Mild: 0.2 fault index <
Self Self-dischurge
Moderale:: S linde_
Leakage cupacity (An/Day)
Severe: fauilt. index
Mild: 1.5 S fault Index < 2.5
High resistance
Moderate: 2.5 < fault index < 3.5
R/ Ros
Severe: faulttinndx 2 3.5
Mild 0.9 S. fault index < 0.95
Low capacity
Moderate: 0.84 < fnnlt,index 0.9
Q/Qus
Severc: fault_index < 0.84
For normal cells (Fig. 2a. [e), whether during charging or discharging, the voltage curves of indi- vidual cells remain highly consistent, without evident fluctuations or deviations. In contrast, cells with severe faults exhibit pronounced differences in both phases. For self-discharge faults (Fig. bb. 2D, persistent leakage leads to a slower voltage rise during the early charging stage, accompanied by more evident curve fluctuations, during discharging, voltages drop abruptly with high-firequeng oscillations. For high internal resistance faults (Fig. 2c. 2g), the increased ohmic drop produces significantly higher charging plateau compared to normal cells under the same current; in discharg- ing, the initial voltage drop sharper, and the overall level stays consistently lower. For low capacity faults (Fig. 21 D)), the reduced effective capacity causes cells to reach the cut-off voltage earlier, resulting in shortened charging duration; during discharging, the voltage plateau is both lower and shorter in duration.
In traditional fault classification, charging data are often prioritized because the charging process actively regulated by the battery management system (BMS), yielding more stable and controllable features. However, TEH-BattrryGen also provides discharging data, which, although more complex, better reflect real-world driving scenarios. This offers erucial support for developing fault classi- fication algorithms based on discharging conditions and fills a gap in existing battery diagnostic datasets.
BENCHMARK TASKS AND METHODS
This study designs two core benchmark tasks to address the practical requirements of battery fault diagnosis, covering the full pipeline from fault identification to fault severity assessment. These tasks aim provide precise diagnostic insights for BMS. The first task is fault classification, where
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LFP charge normal
charge discharge
LFP charge htgghhigghhhhhhheeeeeeeee resisfance
LFPP charge low capacity
3.375
Vohugevi
3.35
Vologgee)
Vidtagery)
3.350
3,30
5,38
3.325
3.30
3.30
10000 Time(s)
20000
28000
0
10101e Times)
10000
Timees)
Timr(s)
(b)
(c)
(d)
LFP discharge normal
LFP discharge self discharge
LFP discharge high. resistance
LFP discharge low capacitty
Voltaget))
3.4
8,35
Vittepeiy
3.30
33
3.00
3.25-
5000 Timel))
10000 Timets)
2500 Tinels)
5000
2000
4000
1mmets)
(e)
(g)
(h)
Figure 2: Comparison of charging and discharging cell voltages of LFP batteries under normal and severe fault states: (a-d) charging and (e-h) discharging processes.
the input consists of complete charging and discharging process data (including key dimensions such cell voltage, current, and temperature), and the output comprises four classes: normal, self- discharge, high internal resistance, and low capacity. This task focuses on distinguishing between healthy and faulty batteries while further specifying the fault type, thereby supporting maintenance decision-making. The second task is fault grading, which targets cells already identified as faulty. Based on time-series data, the model outputs one of three severity levels-mild, mild, moderate, or se- vere facilitaiitiingggggg repair prioritization and reducing operational risks.
To tackle the multi-scale distribution of fault features across varying pack sizes and cell counts, we propose multi-modal benchmark model, MuliMMMaCNNM This model integrates CNN and MLP to process both image-based, and numerical features. The architecture consists of four main components: image feature extraction, attention modules, numerical feature processing, and feature fusion with classification. The image feature extractor is based on pre-trained ResNet50, where the input layer is modified accept single-channel grayscale voltage images, while preserving residual blocks for both local and global representation learning. CBAM attention module is embedded higher layers highlight channel and spatial features most relevant faults. For numerical features, a two-layer fully connected network maps 12-dimensional statistics into a 64-dimensional representation, ensuring consistency with the image feature granularity. At the fusion stage, both feature types are concatenated and fed into a multi-layer fully connected classifier, producing either four-class or three-class outputs for classification and grading tasks, respectively.
In terms of data processing, raw time-series signals are first transformed into grayscale voltage images. To eliminate recording length differences, the time axis is normalized to the range [0,1], Each voltage curve is then plotted into a 512 x 512 pixel grayscale image without axes, followed by median filtering and super-resolution enhancement to refine details, This allows fault-related voltage patterns to be more clearly visualized, as shown in Fig. In parallel, statistical descriptors such as mean, standard deviation, extrema, and range are extracted from cell voltage sequences, along with consistency- based metrics. These 12-dimensional global features capture inter-cell differences and fault severity that image features alone cannot fully represent. To ensure reliable and leakage-free evaluation, adopt two complementary data partition strategies:
(1) Segment-level split: individual segments are divided into training and testing sets using stratified sampling with an 8:2 ratio.
(2) Strict vehicle-level split: all segments originating from the same vehicle are grouped into the same partition, ensuring that segment from test vehicle appears in training.

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(a) LFP charge fault
(b) LFP charge normal
(c) LFP discharge fault
(d) LFP discharge normal
NCM charge fault
NCM charge normal
(g) NCM discharge fault (h) NCM discharge normal
Figure 3: Examples of grayscale voltage images for fault visualization. LFP (top row) and NCM
(bottom row) cells under fault and normal states during charging and discharging processes.
The vehicle-level split eliminates potential vehicle-specific pattern leakage and provides more conservative evaluation across battery instances. All reported metrics are computed on held-out test sets under the corresponding protocol.
For evaluation, we adopt a comprehensive set of metries, including accuracy, recall, and FI-score. Accuracy reflects prediction reliability, recall captures missed fault risks, while F1-score balances precision and recall, aligning with engineering requirements for both reliability and coverage. All metrics are computed an independent
to ensure objectivity and reproducibility. In experi- ments, we systematically compare traditional machine learning methods (e.g.. RF, SVM) with deep leamning models (e.g., LSTM, CNN) across image, time-series, and statistical modalities, thereby providing thorough assessment of model adaptability and strengths across tasks.
5 RESULTS AND ANALYSIS
5.1 RESULTS OF FAULT CLASSIFICATION TASKS
At the single-file level (where a single file corresponds to one csv segment storing charg- ing/discharging data, and each generated current sequence naturally aligned with such segment), as shown Table 3 and Fig. a-dd,, traditional machine learning methods (RF, SVM) exhibit lim- ited performance in multi-class classification, particularly under discharging conditions where all Fl-scores fall below 0.71. This indicates their inability to effectively capture complex fault patterns. In contrast, deep learning methods demonstrate clear advantages: LSTM maintains relatively stable performance across different scenarios, while CNN consistently achieves the best results. Specifi- cally, CNN reaches an F1-score of 0.9206 in the LFP discharging scenario and maintains 0.8732 in the NCM discharging scenario. The confusion matrices further reveal model-specific characteris- ties. In the L.P scenario, CNN achieves overall high accuracy but still shows some misclassification between "self-discharge" and "low capacity." In the NCM scenario, part of the "self-discharge" and "high resistance" samples are misclassified as normal, reflecting less distinct fault boundaries this chemistry. Overall, CNN demonstrates superior classification accuracy and robustness across both battery chemistries and operating conditions.
At the vehicle-level, shown in Table 3 and Fig. 4-- deep learning models again outperform traditional approaches. In particular, CNN achieves Fl-scores of 0.9280 and 0.8897 under LFP dis- charging and NCM charging scenarios, respectively, significantly surpassing other models. Most

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LFP L_FP_char charge file dissifiiation
_FF__iicharge.. clavefication
NCML charge file classifiatiinn
NCM discharge Bie classification
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860
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LPP discharge vehicle, classification
NCM, charge vehicle classification
discharge vehidee classification
95
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Figure 4: Confusion matrices of the CNN model for fault classification. (a-d) single-file file results (e-h) single-vehicle results.
predictions are concentrated along the diagonal of the confusion matrices, though misclassification between "self-discharge" and "low capacity" still occurs under discharging and NCM charging con- ditions. In summary. CNN consistently delivers the highest accuracy and generalization performance in both single-file and vehicle-level fault classification tasks.
Table 3: Multi-class classification results of battery faults based on single-file and single-vehicle tasks.
Task
Model
Dataset
Precision
Recall
F1-score
LFP charge
0.8600
0.6689
0.6908
RF
NCM cCM.charge 0.7871
LFP dischhche
0.7070
0.6677
0.7454
0.7582
0.6860
NCM discharge
0.8158
0.6643
0,6808
LFP.charge 0.6970
0.7199
0.7077
discharge
SVM
NCM charge
0.799
0.8111
0,8033
Single-fil
NCM dischbabe
0.8430
0.6558
0.7272
LFP.charge charge
LFP
0.8686
0.8448
0,8558
LSTM
NCM.charge
IFP discharge
0.8721
0.8724
0.8004
0.8650
0,8273
0.8676
NiM.discaarge
0.8543
0.8260
0,8374
IFP charge
0.8639
0.8671
0.8647
CNN
LFP discharge
NCMLcharge
0.9315
0.9103
0.9206
NCM discharge
0.8752
0.8715
0.8732
LFP charge
0.9710
0.7335
0,7511
LFP discharge
0.7290
0.6866
0.7051
RF
NCM_charge
0.7326
0.8413
0.7782
NCM.discharge
0.7271
0.7500
0.7380
charge
SVM
NCM NCM.charge
0.8210
0.7210
0.8651
0,5897
0.8129
0.6391
Single-vehicle
discharge
0.7500
0.7935
LFP charge
0.7924
0.7493
0.7685
LSTM
NCM charge
LPP discharee
0.8557
0.9164
0.8375
0.8229
0.8313
0,8664
NCM dischange
0.7121
0.7833
0.7460
LFP-charge
0.8857
0.8965
0.8899
discharge
CNN
NCM charge
0.9151
0.8699
0.8897
NCM Mddiscshargge
0.8711
0.8467
0,8580
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_err_eearre__ae file_eeiieeel grading
LFP discharge file grading
L.FP charge_ vehielo grading
LFP discharge vehiele grading
110
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s0
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Tnur babrkk
Tree tabrls
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25
(a)
(b)
(c)
(d)
Figure 5: Confusion matrices of the CNN model for fault grading. (a-b) single-file results (c-d)
single-vehick results.
5.2 RESULTS OF FAULT GRADING TASKS
Compared with the classification task, fault grading is overall more challenging. In the single-file task (Table 4. traditional methods generally perform poorly, with most Fl-scores below 0.65. In contrast, deep learning methods achieve significantly better results. LSTM reaches an Fl-score of 0.7289 under the LFP charging condition, while CNN consistently delivers the best performance, achieving 0.8031 and 0.7442 in the LFP charging and discharging scenarios, respectively. As shown in Fig. [ a--b), CNN can effectively distinguish different severity levels, though confusion between mild and moderate faults remains, indicating that noise under discharging conditions still poses challenges for fine-grained fault recognition.
At the vehicle-level task (Table 4 and Fig. ((c-d),, the results are consistent with the single-file analysis: traditional methods remain insufficient, whereas deep learning methods show clear advan- tages. In particular, CNN achieves an Fl-score as high as 0.8813 under LFP charging conditions and maintains 0.7823 under discharging conditions. The confusion matrices further illustrate that grading results are more distinct in charging scenarios, while mild and moderate faults remain more difficult separate during discharging.
Overall, CNN demonstrates the highest accuracy and robustness in fault grading tasks. However, the difficulty of fine-grained diagnosis is notably greater than that of classification tasks. Under complex operating conditions such as discharging, mild and moderate faults are more prone to confusion due noise, highlighting the limitations of current models in fine-grained feature extraction. Future research should focus on improving recognition capability under complex conditions.
Table 4: Multi-class grading results of battery faults based on single-fil file and single-vehicle tasks.
Task
Model
Dataset
Precision
Recall Fl-score
RF
I.FP charge
LFP discharge
0.6317
0.5238
0.6400
0.5360
0.5976
0.5288
Singlo-file
SVM
LFP charge
LFP discharge
0.5420
0.6469
0.6440
0.5280
0.5323
0.6419
charge 0.6777
0.7885
0.7289
LSTM
LEP discharge
0.7087
0.7019
0.7053
LP charge 0,8167
0,7939
0.8031
CNN
discharge
0.7442
LFP charge
0.5465
0.6800
0.6050
RF
LFP .iischurge
0.6280
0.6400
0.6171
I.P. charge
0.8056
0.6611
0.6874
Single-vehicle
SVM
LFP discharge
0.6732
0.6000
0.5441
LSTM
L.PP discharge
.harge 0,8000
0.7556
0.7000
0.6667
0.7205
0.7273
CNN
LFP charge 0.9071
0.8639
0.8813
LFP .dischagg
0.7753
0.7944
0.7823
CONCLUSION
This paper proposes and constructs CH-BatteryGen, large-scale benchmark dataset for battery fault diagnosis. Compared with existing public datasets, EH-Baateryyeee Gen demonstrates significant advantages in scale, label diversity, and task coverage. By integrating real onboard operational data

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with generative augmentation methods, the dataset achieves both authenticity and scalability. It pro- vides multi-label annotations covering four typical fault types- normal, high resistance, low capac- ity, and self-discharge and establishes unified benchmarking framework that supports multiple tasks, including fault detection, classification,, and grading.
Experimental results show that traditional machine learning methods are limited in handling com- plex multi-class tasks, whereas deep learning models achieve overall superior performance. In par- ticular, CNN consistently yields the best results across different chemistries and operating condi- tions, maintaining high accuracy and robustness even under challenging discharging scenarios. Fur- thermore, the experiments reveal clear performance sensitivity to operating conditions and battery chemistries: traditional methods suffer substantial degradation under discharging scenarios, whereas deep learning models degrade less severely, demonstrating stronger robustness and adaptability.
Nevertheless, fault grading tasks remain more difficult than classification tasks, especially under discharging conditions where mild and moderate faults are easily confused, indicating room for im- provement in fine-grained diagnosis. Moreover, limitations still exist in CH-BatteryGen such as restricted chemistry coverage, limited fault label diversity, and insufficient data for extreme operat- ing conditions, which highlight directions for future dataset expansion and optimization.
In summary, CH- CH-BatteryGen provides the first systematic large-scale benchmark platform for in- telligent battery diagnosis. It establishes foundation for fair comparisons and reproducible search across multiple algorithms, tasks, and scenarios. We expect Bee-aatteyyGen to accelerate the standardization and engineering applications of battery intelligent diagnosis, while offering new opportunities for fine- mra-grained fault recognition under complex operating conditions.
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Zhi Liu, Ardashir Mohammadzadeh, Hamza Turabich, Majdi Mafarja, Shahab S Band, and Amir Mosavi. A new online learned interval type-3 fuzzy control system for solar energy management systems. leee Access, 9-10498-10500 2021.
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SUPPLEMENTARY MATERIALS
In this section, we provide supplementary materials to complement the main paper:
Appendix A: Data Card for CH-BatteryGen
Appendix B: Dataset Generation Methods
Appendix C: Realism Validation of Generated Data
A
DATA CARD FOR CH-BATTERYGEN
A.1 DATASET SUMMARY
Total vehicles: 1000
Total segments: 19,478 segments
Sampling interval: s
Segment duration: 30-60 minutes
Operating temperature range: -10C 45"C
Table 5: Dataset summary.
Chemistry
Vehicles
Segment Type
Total Segments
Avg. Seg/Veh
Charge Discharge
5,000
10
LFP
500
5,000
10
Charge
4.478
8.956
NCM
500
Discharge
5,000
10
Total
1,000
19,478
19.478
A.2
LABEL TAXONOMY
Fault type (Table 6) Normal, Self-discharge, High resistance, Low capacity
Severity grading (For faulty vehicles of LFP only, Table Mild, Moderate, Severe
important to note that the multi-level severity grading task is specifically defined for the L.P chemistry subset. Given that severe battery degradation is statistically rare in actual vehicle opera- tions, our dataset reflects this sparsity. As shown in Table 7 the sample counts decrease as severity increases: Mild faults constitute the majority, followed by Moderate, while Severe cases the least frequent. The specific criteria for these severity thresholds are detailed in Table 2 This de- liberate imbalance challenges algorithms detect incipient, low-magnitude anomalies, critical requirement for early-warning systems.
Table 6: Distribution of fault types.
Category
Fault Type
LFP (Vehs)
NCM (Vehs)
Total(Vehs)
Normal
Normal
400
400
800
Self-discharge
40
40
80
Faulty
High Resistance
30
30
60
Low Capacity
30
30
60
Total
500
500
1,000
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Table 7: Distribution of Severity grading for faulty vehicles of LFP only.
Category
Severity grading
LFP (Vehs)
Mild
32
Self-discharge
Moderate
Severe
Mild
10
High Resistance
Moderate
16
Severe
Mild
10
Low Capacity
Moderate
10
Severe
10
Total
100
B DATA GENERATION METHODS FOR CH-BATTERYGEN
B. DATA GENERATION PIPELINE
The CH- HH-BatteryGen dataset is synthesized through a multi-stage pipeline that integrates deep gen- erative modeling with physical domain knowledge. summarized in Table [ all signals in the released dataset are generated to preserve privacy while maintaining statistical fidelity.
The generation process initiates with Diffusion- TS Model trained on real-world current profiles during both charging and discharging. These current profiles are then fed into chemistry-specife (LFP or NCM) models, which are detailed in B.3 and B.4. The battery model simulate the elec- trochemical response produce individual cell voltages. Finally, integrated calculation modules derive pack-level attributes detailed in B.5, including Total Voltage, SOC, Maximum/Mimmnii Minimum Cell Voltages, Maximun/Minimum Temperatures, ensuring that all signal dimensions remain coupled through rigorous physical constraints.
B.2 CORE INPUT: GENERATION OF CHARGING DISCHARGING CURRENT SEQUENCES
The current sequence serves the key input for voltage generation, and its fidelity directly deter- mines the reliability of subsequent data. The dataset construction adopts the Diffusion-TS frame- work based on probabilistic diffusion models generate charging and discharging currents. By combining "time-domain decomposition" and "frequency-domain denoising," the framework re- constructs realistic current dynamics. In the forward process, random Gaussian noise is gradually injected into the original EV current sequence until the signal is completely degraded; in the reverse process, the Transformer network progressively restores the true current profile from the noisy sig- nal through denoising and decoding. Meanwhile, a time-series decomposition module is introduced separate long-term trends (e-g., monotonic decrease in charging current during the later stages) and periodic fluctuations (e.g., small-amplitude ripples), which are further refined using multi-level wavelet packet decomposition. This ensures that the frequency characteristics of the generated cur- rents remain consistent with those of real data, thereby avoiding unphysical high-frequency distor- tions.
The final generated current sequence Ieen exhibits stable temporal resolution, with sampling fre- quency set to 10 Hz and a segment length ranging from 30-60 minutes. This setup realistically cov- typical driving and charging scenarios, such constant-speed driving, rapid acceleration. and stop-and-ge conditions, as well as mainstream slow-charging and fast-charging conditions, thereby providing reliable current inputs for subsequent voltage generation of both LFP and NCM batteries.
B.3 LFP VOLTAGE GENERATION
For LFP batteries, a series of first-order RC equivalent circuit models is employed. The generated charging and discharging current I Igen from Diffusion-TS I is used input, and the model simulates
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internal ohmic drop, polarization effects, and hysteresis characteristics to achieve precise mapping from current to voltage time-series data, as shown in Eq. !
U OCV(SOC) + Ru Iee Rı (1-0
(1)
Here, U represents the terminal voltage; OCV(SOC) is the open-cireuit voltage as function of SOC; Igea is the model input current; Rg is the ohmic resistance; R1 is the polarization resistance; 71 the time constant; is time; and h denotes the voltage offset term. By tuning these key equivalent- circuit parameters, the model can accurately inject fault signatures such as high internal resistance, low capacity, and self-discharge, ensuring consistency with real-world LFP battery fault behaviors.
B.4 NCM VOLTAGE GENERATION
For NCM batteries, DCWT-based mapping model is constructed. Given the generated input current Igen. the model applies a three-step procedure- crrddure-baseline calibration, feature matrix solution, and voltage reconstruction to produce the voltage time-series data of NCM batteries. By leveraging the decomposition and reconstruction capability of DCWT, the dynamic response voltage to current is accurately simulated.
To ensure stable voltage computation, a baseline voltage Uzet is introduced. Using semi-annual real NCM battery test data, reference feature matrix Gzef (with dimension 1 x 4) is derived to capture the average response characteristics unit-cell voltage to current, as expressed in Eq. 2
Um,hy =
Grel Imning
(2)
where Um,hy denotes the voltage of unit m at time hy, and Im,by is the corresponding current sequence. Substituting Igen into Eq. yields the baseline voltage Uret, shown in Eq
Unat
Gref Igen
For each generated segment, DCWT is employed to approximate the influence of current voltage, and a feature response matrix F E "x" derived, where n denotes the number of battery cells. This matrix encodes the deviation of unit-cell voltages from the baseline response, given by Eq.
Uorigin
Uzet
F Torigin
where Voriinn represents the measured NCM cell voltages and Lorigin is the corresponding measured current. Finally, combining Gref with the generated input Igen the reconstructed voltage sequence Ugen is obtained, shown in Eq. 5
gen F.
Igon + Urof
By adjusting the dimensional response of F to target faulty cells, fault signatures of high resistance, low capacity, and self-discharge can be precisely injected. The final generated NCM voltages exhibit close agreement with experimental measurements, with average deviation of less than 10 mV and a maximum deviation of 30-50 mV, thereby faithfully reproducing the voltage characteristics of real battery faults.
B.5 PACK LEVEL GENERATION
Building upon the individual cell responses from the battery model, the pack-level voltage attributes are derived to simulate realistic bus-level telemetry. The Total Voltage (Urotat) is calculated as the instantaneous sum of all series-connected cell voltages, reflecting the overall potential of the battery string. To represent cell-to-cell variations and potential fault signatures, we explicitly extract the Maximum and Minimum Cell Voltages (Umaze U at each time step:
N
Utotat (t) = Σ U,(t)
(6)
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Umax (t)
max U.(,), Unin (t) iEl.....
min Ui(t) iEl.....N
To ensure the physical consistency of the generated telemetry, the Pack-level SOC is determined by the short-board effect (minimum cell principle). Specifically, we define the pack SOC the ratio of the minimum remaining capacity among all constituent cells to the total nominal capacity of the pack:
min (SOC,(1)- Qi)
pOCpack (t)
Qtotal
This formulation ensures that when low capacity fault is injected into specific cell (Quell, J), the resulting pack-level SOC trajectory accurately reflects the diminished energy availability, aligning the generated data with real-world BMS behavior during cycling.
To provide a multi-modal perspective, we incorporate a lumped-parameter parameter thermal model. The temperature evolution of each cell T; is governed by the heat generation-dissipatii balance:
dT,(t) = I(t) dt
mCp
- - TTam))
where I(t)2RR R3 denotes the Joule heating and h; the convective heat dissipation coefficient. Given the current simplified parameterization of the thermal environment, we provide the Maximum and Minimum Temperatures as reference features:
T maaz (t) max Ti(t), Tmin (t) min Ti(t) :EI.....N
(10)
ie iEl...,,
It should be emphasized that while the electrical signals (U,1,50C) are highly accurate for diag- nostic benchmarking, the current temperature data (T Tmin) serves primarily as a qualitative reference. Due to the simplified thermal parameters, these signals reflect physical trends (e.g., heat accumulation during high-current events) rather than absolute high-fidelity thermal distributions. We aim to enhance the thermal fidelity in future iterations.
C REALISM VALIDATION OF GENERATED DATA
C.I VALIDATION PROTOCOL
To assess the distributional alignment between generated data and real operational signals, we con- duct an internal embedding- validation. Real fleet samples (not publicly released due confi- dentiality constraints) and generated samples are projected into a shared feature space using identical feature representations. Two-dimensional t-SNE embeddings are then computed examine distri- butional separability.
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40
Normal (Generated data)
High reniotence data)
capacity (Generated data)
Low SOC (Generated data)
-20
Seff-lischargeeGGenernde data)
-40
label (Real-world data)
Thermal runway(Real world data)
-30
-20
-10
Figure 6: t-SNE visualization of real-world and generated samples. Colored points denote generated samples, including normal operation and multiple fault types (low capacity. high resistance, and self-discharge). Gray points represent real-world fleet samples used for internal validation but not publicly released. Black points correspond to thermal runaway cases observed in real-world data.
C.2 RESULTS
As shown in Fig. real and generated samples are intermingled in the embedded space without forming separable clusters, while different fault categories remain distinguishable. This indicates that the released dataset preserves the structural and fault-dependent characteristics observed real- world data. In particular, classifiers operating in this representation space are unable to distinguish generated samples from real ones, whereas fault modes remain separable.
C.3 INTERPRETATION AND SCOPE
We emphasize that this validation does not claim exact equivalence between the generated dataset and raw fleet telemetry. Instead, demonstrates that the released dataset maintains internal distri- butional consistency and fault separability under common representation space, while complying with data-security and confidentiality constraints.
Because the generated data preserve essential characteristics of real-world fault behaviors, models developed using CH-BatteryGen can serve as effective pretraining or benchmarking tools and can be adapted practical diagnostic scenarios through domain-specific fine-tuning. By releasing this dataset, we aim to facilitate broader participation from the machine learning community and to promote reprodueible research on battery safety monitoring.
16


Reviews:
[{"review_id": "fDYL7ovw53", "reviewer": "Reviewer_m2xq", "summary": "This paper introduces CH-BatteryGen, a large-scale AI-generated dataset designed for fault diagnosis of electric vehicle (EV) power batteries. It integrates real on-board operational data from 1,000 EVs—spanning two major chemistries (NCM and LFP)—with generative modeling to synthesize realistic voltage, current, and temperature signals. The dataset is annotated with four fault categories (“normal,” “high internal resistance,” “low capacity,” and “self-discharge”) and three severity levels, supporting two benchmark tasks: fault classification and fault grading.\nA unified evaluation framework is proposed comparing traditional ML (RF, SVM) and deep learning models (LSTM, CNN). Experiments show that CNNs achieve the best overall performance, with F1-scores up to 0.9280 in classification and 0.8813 in grading tasks", "questions": "- Dataset composition:\nWhat kind of data is the generated model used to generate?\n\n- Labeling procedure:\nHow were the four fault labels defined and verified?\nAre thresholds for “high internal resistance” or “low capacity” derived from SOH metrics or engineering heuristics?\n\n- Reproducibility:\nPlease confirm whether the dataset and code will be released publicly before the camera-ready deadline.\nIf open-sourced, include a data card specifying counts per label and per chemistry.\n\n- Methodology extensions:\nWhy didn't you consider using contrastive learning or other commonly used anomaly detection methods? Could you provide ablations with raw time-series input to assess whether image preprocessing (super-resolution) affects fairness?\n\n- Denoise: Real battery data contains a lot of noise. Have you used any noise reduction methods?", "limitations": "", "rating": 6, "confidence": 3, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- The work presents one of the first comprehensive attempts to generate a large-scale, labeled EV battery fault dataset combining real and synthetic data. The dual-task benchmark (classification and grading) provides a novel framework for fair model comparison under diverse operating modes.\n- The experimental design is systematic, evaluating both conventional and deep learning methods across chemistries (LFP/NCM) and conditions (charge/discharge). Quantitative metrics are clearly reported and confusion matrices help illustrate model limitations.\n- The dataset could be impactful for both academic and industrial research, especially given the scarcity of publicly available EV fault data. If properly validated and open-sourced, CH-BatteryGen could become a key benchmark for safety-critical battery diagnosis.", "weaknesses": "- Unclear data provenance and labeling process.\nThe dataset claims to “integrate real on-board operational data with generative modeling methods to build a comprehen-\nsive dataset covering 1,000 EVs”, However, it doesn't clarify where generative models are used. The definition and thresholds for these fault types are not rigorously described.\n\n- Synthetic–real data gap.\nWhile the authors state that generated data match real measurements within 30–50 mV deviation, it is unclear whether the generative process preserves electrochemical consistency or simply fits statistical distributions. There is no evidence that the generated sequences capture real DoD (Depth of Discharge) dynamics or aging mechanisms.\n\n- Generalization experiment\nAlthough the paper mentions that model performance significantly drops when transferring across chemistries (LFP ↔ NCM), no detailed results, tables, or quantitative metrics are provided. To support this claim, the authors should include a clear cross-domain evaluation matrix (e.g., train-on-LFP/test-on-NCM and vice versa) with corresponding F1-scores and confusion matrices to assess the true generalization ability of the models.\n\n- Data availability inconsistency.\nAlthough the paper claims the dataset and code are “open-sourced,” the GitHub repository does not actually contain the full data or scripts, making reproducibility and verification difficult.\n\nSimplistic baselines and missing alternatives.\nThe study focuses on CNN/LSTM but omits more suitable paradigms like contrastive learning or anomaly detection methods, which may be better for rare fault discovery under limited labels.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "khHolXioCB", "reviewer": "Reviewer_h1BD", "summary": "This paper introduces a new dataset for classifying the failure mode and severity for lithium-ion batteries.", "questions": "See weakness.", "limitations": "", "rating": 4, "confidence": 4, "soundness": 3, "presentation": 3, "contribution": 3, "strengths": "- A new dataset for the community, suitable for battery data analysis and also time-series classification research", "weaknesses": "- Why classifying battery failure modes or severity is an important task? I have noted that the authors mentioned battery faults may lead to severe consequences in lines 047 - 051. But in order to avoiding that consequences, what we really need is to predict battery failures in advance? How could judging failure modes after existing battery failures help to prevent these problems?\n- As the experimental results show, some simple techniques using CNN can deliver pretty good classification performance. So what are the real benefits if the classification accuracy increases by 1%?\n- The dataset released seems to be synthetic data, why not releasing real data? How could the model built on the synthetic data perform on real conditions?", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "Nj00HUhB6y", "reviewer": "Reviewer_TS3Q", "summary": "The paper presents CH-BatteryGen, a benchmark dataset and evaluation suite for electric vehicle battery fault diagnosis. It combines real-world EV data with physics-constrained generative modeling to ensure both realism and scalability. The dataset spans two chemistries (LFP, NCM), four fault types, three severity levels, and includes multi-modal time series signals such as voltages, current, temperature, and state of charge. It supports two tasks: four-way fault classification and three-way fault grading.\nData are derived from large-scale EV operations, augmented by AI-generated current profiles and voltage signals from RC and DCWT-based models. The dataset includes 1,000 EVs with diverse conditions and fine-grained per-cell and pack-level signals. Both classical and deep learning baselines are evaluated, with CNNs performing best overall. The authors also propose BatteryMultiModalCNN, which processes voltage time series as grayscale images via a ResNet-50 with attention and feature fusion.\nCH-BatteryGen is positioned as more fine-grained and realistic than prior datasets like EVBattery, BatteryML, NASA PCoE, and HNEI, which lack fault labels or real-world coverage. The dataset is open-sourced to promote standardized evaluation, with visualizations provided for signal patterns, voltage images, and model performance.", "questions": "- The reported total of “1000 EVs × 20 segments” appears inconsistent with the global label counts (400, 30, 30, 40). Are these figures per scenario, per chemistry, or based on labeled subsets? Clarifying this would help assess class imbalance and distribution, considering per-task, per-chemistry breakdowns along with severity distributions.\n\n- For single-file experiments, was the data split by vehicle to prevent leakage? If not, re-running experiments with vehicle-level grouping and reporting the resulting changes in performance would be interesting.\n\n- To support claims of cross-chemistry and cross-mode degradation, explicit train-on-LFP/test-on-NCM (and vice versa), as well as charging-to-discharging transfer experiments may be included. Report macro-F1 scores along with confidence intervals.\n\n- Realism validation. Beyond the reported ≤10 mV voltage deviation, can you provide distributional comparisons between real and generated data, such as MMD or spectral distance? Fault-conditioned precision-recall curves stratified by temperature and SOC would further support realism claims.\n\n- How are the R/R95 and Q/Q95 thresholds computed? Are they defined globally or on a per-vehicle or per-chemistry basis? An ablation under simulated sensor noise or inter-cell variability would help assess their robustness.\n\n- Could you clarify the rationale for excluding recent time-series models such as transformers or state-space architectures? Including such baselines, as well as non-image-based temporal models, would help evaluate whether the CNN-based voltage image approach is optimal.\n\n- Real BMS data often include sensor noise, dropout events, and timestamp misalignments. How are these artifacts modeled during data generation, and how are they handled at training and inference time?\n\n- Have you evaluated model performance under extreme ambient conditions (e.g., –20 °C, +45 °C) or across varying pack topologies (e.g., 28, 92, 96, 124 cells) to test robustness to deployment variability?\n\n- Do you have access to any real-world, fault-labeled datasets, either from lab studies or field deployments, that could be used to assess out-of-distribution generalization and validate the utility of the synthetic data?", "limitations": "", "rating": 4, "confidence": 3, "soundness": 2, "presentation": 2, "contribution": 3, "strengths": "- The paper addresses a critical application in battery fault diagnosis, an area where publicly available, fault-labeled datasets remain scarce. The inclusion of both LFP and NCM chemistries, along with data from both charging and discharging conditions, enhances the dataset’s relevance to real-world electric vehicle operations.\n\n- The paper clearly defines two tasks: fault classification and severity grading. These tasks are evaluated using standard metrics such as F1 score, recall, and accuracy, supported by confusion matrices that facilitate interpretability. The addition of a grading task represents a meaningful advancement beyond traditional binary fault detection.\n\n- The data generation process combines AI-generated current profiles with physics-constrained voltage modeling, using an RC model for LFP and a DCWT-based mapping for NCM. This approach balances scalability with domain fidelity by incorporating electrochemical structure into the synthetic data.\n\n- The empirical study includes a range of classical and deep learning methods, from random forests and SVMs to LSTMs and a CNN-based architecture with CBAM attention. The comparison provides a useful baseline for future work, and the CNN’s performance, particularly under discharging conditions, offers practically valuable insights", "weaknesses": "- While the reported voltage deviation under 10 mV is promising, more comprehensive tests are missing. These include distribution-level comparisons, condition-dependent realism (e.g., by temperature or state of charge), and consistency across sensor modalities. No validation is performed using real-world fault labels, which weakens claims of external validity.\n\n- There is ambiguity regarding dataset scale and class balance. The claim of 1,000 EVs conflicts with the small global label counts reported (e.g., 400, 30, 30, 40), and it is unclear whether these refer to subsets, chemistries, or scenarios. This uncertainty affects class imbalance, grading reliability, and the credibility of performance metrics.\n\n- The current data split risks train-test leakage. If segments from the same vehicle appear in both sets, this could inflate results. A vehicle-level split should be provided alongside the current file-based split, and metrics should be reported for both.\n\n- The experimental protocol would benefit from stronger validation strategies. Given the dataset's moderate scale, leave-one-vehicle-out cross-validation would provide a more robust assessment of generalization across vehicles and chemistries.\n\n- The cross-domain transfer analysis is underdeveloped. Although the paper suggests cross-chemistry and cross-mode degradation, no explicit train-on-A/test-on-B experiments or confidence intervals are presented. Such comparisons are essential to support generalization claims.\n\n- Baselines do not reflect the current state of the art. Recent time-series models such as transformers or state-space models are absent, despite their known performance advantages in noisy, long-horizon data. Their inclusion could affect conclusions about modality and architecture choices.\n\n- The definition of severity levels lacks operational grounding. Although threshold formulas are provided, the paper does not fully explain how they are calibrated, normalized per battery pack, or made robust to sensor noise and cell variability.\n\n- Details on dataset release and ethical considerations are incomplete. Although the dataset is described as open-source, no repository URL or license is provided in the paper. There is also no discussion of data provenance, real versus synthetic composition, consent, or privacy protections for vehicle identifiers.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}, {"review_id": "1R0jc0okeP", "reviewer": "Reviewer_D6gL", "summary": "In this paper, a new benchmark (real-calibrated, synthetically generated) for battery fault diagnosis is presented. The authors use real data for calibrating their data generation method for two different battery chemistries: lithium iron phosphate (LFP) and nickel-cobalt manganese (NCM). They use this benchmark to evaluate different baseline architectures on two tasks: fault classification and fault grading.", "questions": "Please address the points I have raised in the weakenss directly", "limitations": "", "rating": 2, "confidence": 3, "soundness": 2, "presentation": 1, "contribution": 2, "strengths": "- Novel benchmarks that are easily accessible online are an important contribution to the academic community.\n- Claimed open release in a standard format allows for comparisons in the community", "weaknesses": "- My main criticism is that ICLR may not be the right venue for this work. Given its specialized nature concerning batteries, I believe more specialized journals (such as those heavily cited in the references) would be a better fit.\n- The data generation pipeline is quite unclear. On one hand, it states that it is based on real data, but then it mentions that simulations and data-driven methods, such as diffusion models and convolutional wavelets, are used. It is unclear: a) the motivation behind these choices, and b) how this dataset was validated. How can we tell whether this data is actually realistic?\n- In general, the entire manuscript is hard to follow.\n- The benchmark section uses only standard architectures (RF, SVM, LSTM, and CNN) with many arbitrary design choices. The exclusion of more specialized architectures makes the baselines rather weak, while the multitude of arbitrary design choices makes it hard to develop a clear intuition of what is needed for improvement.", "quality": null, "clarity": null, "significance": null, "originality": null, "strengths_and_weaknesses": "", "was_revised": false, "final_justification": "", "rebuttal": "", "claims_and_evidence": "", "methods_and_evaluation_criteria": "", "theoretical_claims": "", "experimental_designs_or_analyses": "", "relation_to_broader_scientific_literature": "", "essential_references_not_discussed": "", "other_strengths_and_weaknesses": "", "other_comments_or_suggestions": "", "questions_for_authors": "", "summary_of_paper": "", "summary_of_recommendation": "", "technical_quality": "", "clarity_of_presentation": "", "potential_impact": "", "robotics_focus": "", "quality_of_limitations": "", "questions_for_rebuttal": "", "reasons_to_accept": "", "reasons_to_reject": "", "questions_to_authors": "", "extra_scores": {}}]

Author rebuttal:

Decision: Accept (Poster)