In the rapidly evolving field of new energy vehicles, the power battery stands as a critical component, directly influencing vehicle safety and operational stability. We focus on addressing the frequent fault issues encountered during the operation of China EV battery systems, which are often subjected to complex conditions leading to performance degradation or thermal runaway. Traditional methods, such as physical modeling or expert rules, have shown limitations in generalization and data adaptability. Therefore, we adopt a data-driven approach to construct a comprehensive fault diagnosis and early warning system based on multi-source data collected from real vehicles. Our research encompasses data preprocessing, feature dimensionality reduction, diagnostic model construction, and validation of warning mechanisms, aiming to enhance real-time fault identification and前瞻性 for EV power battery systems. By integrating alarm data screening with neural network algorithms and parameter prediction techniques, we achieve early detection of key states like voltage, temperature, and state of charge (SOC). Experimental results demonstrate high accuracy and stability, providing effective support for intelligent maintenance in the China EV battery industry.
Data preprocessing is a foundational step in our methodology, as it directly impacts the efficiency and accuracy of subsequent models. In the context of EV power battery systems, vast amounts of data are generated during operation, much of which is redundant or unrelated to faults. We begin by screening fault-relevant data to improve model convergence and diagnostic precision. Specifically, we define data sources and their correlation with battery system faults, employing a targeted processing strategy to eliminate non-critical items such as vehicle-level parameters, powertrain data, and non-physical alarm information. During the initial screening phase, we rely on a fault knowledge base to categorize and map alarm items, analyzing their impact paths to mark relevance to core battery performance indicators. Interfering alarms are labeled as irrelevant and excluded from modeling samples.
To address issues like repeated alarms and redundant records, we implement a fusion logic within time windows. For instance, if an alarm item appears continuously without parameter fluctuations exceeding a set threshold, it is merged as a non-abnormal resampling record, thereby reducing data redundancy. Additionally, sensor misreporting is mitigated by setting upper and lower validation bounds, combined with statistical distribution analysis to filter out records that deviate significantly from normal patterns. For data completeness, we introduce a multi-channel interpolation algorithm to handle missing values, fitting gaps based on trends from other parameters in the same period. This ensures temporal data structure recovery and consistency, with all data meeting criteria of field completeness, record continuity, and clear labeling before model training.
In the data adaptation analysis, we identify redundant and low-contribution feature variables. Common parameters in China EV battery data, such as battery pack voltage, cell temperature differences, and discharge rates, often exhibit strong linear or nonlinear correlations. Through feature correlation matrix analysis, we clarify dependencies between variables, eliminating repetitive expressions and retaining core drivers. For categorical variables like alarm type encodings, which can be sparse or unevenly distributed, we transform them into structured embedded representations before dimensionality reduction. This step enhances the input data’s expressiveness for modeling EV power battery faults.
To illustrate the data characteristics, we present a table summarizing the distribution of alarm levels and feature combinations based on our preprocessing:
| Alarm Level | Alarm Subtype | Sample Count | Percentage (%) | Trigger Feature Combination (Example) |
|---|---|---|---|---|
| High Risk | Voltage Anomaly | 1826 | 13.5 | Voltage difference > threshold and large temperature fluctuation |
| Medium Risk | Temperature Anomaly | 5348 | 39.5 | Battery temperature rise exceeds ΔT standard |
| Low Risk | Communication Jitter | 6354 | 47 | Signal out-of-sync time > set cycle |
The construction of a fault diagnosis model is central to our approach for EV power battery systems. We start by processing alarm information through structured classification, converting raw alarm signals into standardized labels that enhance model distinguishability and learnability. Alarm signals, including over-voltage, under-voltage, over-temperature, and communication abnormalities, are clustered based on alarm level, frequency, and parameter trigger logic. A hierarchical label system is built according to their impact on vehicle safety. To handle class imbalance in raw alarm data, which can bias the model toward majority classes, we apply resampling strategies to balance categories by increasing the weight of minority samples while preserving the original label structure. Labels are encoded by combining alarm codes, duration, and triggered parameter fluctuations, improving the model’s ability to recognize abnormal trajectories.
For the algorithm design, we employ neural networks due to their capability in modeling time-series data and extracting nonlinear features. The dynamic nature of EV power battery state changes requires capturing temporal dependencies, which traditional static models struggle with. Our diagnostic network is a multi-layer neural model incorporating fully connected structures and a sliding time-window input mechanism. It encodes historical states sequentially to output current fault presence and types. The input layer receives preprocessed feature vectors, including voltage, current, temperature, SOC, and combined alarm information. Hidden layers consist of two layers with up to 256 neurons each, using ReLU activation for nonlinear expression. The output layer employs a Softmax function for fault probability distribution, with the number of nodes corresponding to label types.
The training process uses cross-entropy as the loss function, defined as:
$$ \mathcal{L}_{CE} = -\frac{1}{N} \sum_{i=1}^{N} \sum_{j=1}^{C} y_{ij} \log(p_{ij}) $$
where \( N \) is the total number of samples, \( C \) is the total number of classes, \( y_{ij} \) is the true label for sample \( i \) in class \( j \), and \( p_{ij} \) is the predicted probability for that class. This function effectively measures the difference between predicted and true distributions, ensuring good gradient propagation and classification efficiency. We incorporate L2 regularization and Dropout mechanisms to prevent overfitting, and the Adam optimizer to accelerate convergence. Key parameters of the neural network model are summarized below:
| Parameter Name | Parameter Value |
|---|---|
| Network Structure | 2 fully connected layers |
| Neurons per Layer | 256 |
| Activation Function | ReLU |
| Optimizer | Adam |
| Learning Rate | 0.001 |
| Dropout Ratio | 0.3 |
| Regularization Coefficient | 0.0001 |
| Input Sequence Length | 10 s time window |
| Output Nodes | 3 fault types |
The architecture is designed to deeply learn potential fault triggers, enabling the model to not only identify current anomalies but also infer developmental trends and risk levels. By leveraging the neural network’s ability to model high-order feature combinations and contextual time structures, it demonstrates robust performance in handling complex fault modes and composite abnormalities for China EV battery applications.
Moving to the fault warning mechanism, we establish a parameter prediction model to address the gradual evolution of faults in EV power battery systems. Before critical faults occur, key operational parameters often exhibit fluctuating trends, and early identification of these can prevent severe incidents. Our model is based on time-series modeling, enhanced with memory-capable structures like Long Short-Term Memory (LSTM) networks to capture dynamic variable evolution. The input data is constructed from continuous historical segments with a window length of 30 seconds, forming a multi-dimensional matrix of battery system features sampled per second. The target variables are parameter value sequences for future time steps (e.g., 5 to 30 seconds ahead), tailored to specific warning scenarios.
The LSTM output layer is combined with fully connected layers to form a regression output structure, producing predicted parameter curves. Training employs the mean squared error (MSE) loss function to minimize deviations between predicted and actual values over time and amplitude. The loss function is expressed as:
$$ \mathcal{L}_{MSE} = \frac{1}{n} \sum_{i=1}^{n} ( \hat{y}_i – y_i )^2 $$
where \( \hat{y}_i \) is the predicted value, \( y_i \) is the actual value, and \( n \) is the total number of time steps. This model supports multi-variable cooperative prediction, enabling strong generalization and early detection of fault precursors, even under weak signal conditions. When predictions deviate from normal trajectories, the backend warning module is activated for potential fault trend annotation and response preparation.
In the input layer, we embed a multi-variable standardization module to eliminate gradient fluctuations caused by unit differences. The middle layer comprises stacked LSTM units to enhance modeling capability when switching between gradual trends and sudden disturbances in EV power battery systems. The output structure generates target value regression outputs as sets of predicted curve points, facilitating the extraction and summarization of battery state precursors.
To assess the warning mechanism’s practicality, we evaluate model stability across varying environments, as generalization is crucial for real-world applications. We introduce a multi-dimensional performance indicator system to comprehensively measure error distribution, response speed, and prediction consistency across different fault types and operational periods. Prediction accuracy is gauged using root mean square error (RMSE) and mean absolute error (MAE), which reflect overall deviation and average error magnitude in continuous regression tasks. Warning response capability is measured by early identification accuracy, indicating how timely the model detects parameter anomaly inflection points before faults reach critical states. Additionally, cross-fault category stability evaluation tests consistency in identifying trends under various诱因, such as high temperature, over-voltage, or SOC突变, ensuring the model maintains coherent logic for China EV battery fault recognition.
For instance, in experiments involving EV power battery data, the parameter prediction model achieved an RMSE of less than 0.05 for voltage predictions and an MAE of 0.02 for temperature forecasts, demonstrating high precision. Early identification accuracy exceeded 90% for high-risk faults, with consistent performance across multiple fault scenarios. This underscores the model’s reliability in providing前瞻性 warnings for China EV battery systems.
In conclusion, our research develops a data-driven framework for fault diagnosis and early warning in EV power battery systems, addressing common fault evolution characteristics. Through optimized data preprocessing, including screening high-relevance alarm information and feature dimensionality reduction, we enhance the structural expressiveness of input data, improving model perception of fault behaviors. The diagnostic model, integrating alarm classification and neural network structures, achieves accurate identification of fault states at various risk levels. The parameter prediction model learns evolutionary patterns from operational sequences, showing stability and前瞻性 in forecasting key variables, validating the feasibility of mining potential fault trends from historical data. This technical framework exhibits strong adaptability in real vehicle scenarios, offering reliable support for the safety management of China EV battery systems. Future work will focus on extending the model to more diverse operating conditions and integrating real-time updates for continuous improvement in EV power battery maintenance.
Throughout this study, we emphasize the importance of data-driven methodologies in advancing the reliability of China EV battery technologies. By leveraging neural networks and time-series analysis, we contribute to the growing body of knowledge on EV power battery fault management, paving the way for smarter, safer electric vehicles. The integration of these approaches not only enhances diagnostic accuracy but also promotes sustainable development in the new energy vehicle sector, aligning with global trends in automotive innovation.