In the rapidly evolving field of electrical car repair, I have dedicated my efforts to addressing the critical challenges associated with electric vehicle (EV) battery systems. As a core component, the battery system directly influences overall vehicle performance, and its faults often exhibit diversity, complexity, and隐蔽性, making traditional diagnostic methods inadequate. The shift toward digital and intelligent fault diagnosis is imperative, and through my work, I have integrated deep learning and big data analytics to develop smart diagnostic models and standardized repair processes. This approach significantly enhances the quality of EV repair and ensures operational safety. Below, I detail the key aspects of my research, emphasizing multi-source data acquisition, intelligent diagnosis, and precise repair techniques, all aimed at advancing electrical car repair practices.
The power battery system integrates multiple functional modules and control units. It centers on battery packs, managed via a main control board, and employs communication networks such as CAN bus, Ethernet LAN, and RS-232 serial ports. The battery pack consists of eight standard battery modules connected in series and parallel, each equipped with voltage acquisition and temperature monitoring capabilities. The system includes a bidirectional DC-DC converter for energy flow control, a supercapacitor for optimized energy management, and switches S1 and S2 for rapid energy response. An upper computer facilitates parameter configuration and state monitoring, supporting remote data acquisition and fault diagnosis. The core control board uses a DSP+FPGA architecture for high-speed data processing and closed-loop control. Key physical components include the control board, lithium battery modules, drive circuits, and energy storage units, reflecting a comprehensive engineering implementation.
In my analysis of system operational characteristics, I focus on electrochemical, thermal, and electrical aspects. Electrochemical properties encompass charge-discharge efficiency, internal resistance changes, and capacity degradation, characterized using electrochemical impedance spectroscopy (EIS) with a frequency range of 0.01 Hz to 100 kHz and cyclic voltammetry with scan rates from 0.1 mV/s to 10 mV/s. Thermal characteristics involve temperature distribution, heat dissipation efficiency, and uniformity, analyzed via infrared thermal imaging (resolution 0.1°C, sampling frequency 10 Hz) and computational fluid dynamics simulations. Electrical traits include voltage platforms, current ripple, and charge-discharge curves, recorded with high-precision data acquisition equipment (voltage accuracy 0.1 mV, current accuracy 0.1 A). State monitoring emphasizes State of Charge (SOC), State of Health (SOH), and State of Power (SOP), predicted using equivalent circuit models. Faults in EV battery systems are categorized into electrochemical failures, mechanical damage, thermal management anomalies, and control system faults. Electrochemical failures, such as capacity fade and increased internal resistance, result from improper charging regimes and high temperatures. Mechanical damage includes shell deformation and seal failure, often due to vibration and manufacturing defects. Thermal issues like localized overheating stem from cooling system failures, while control system faults involve sampling errors and communication interruptions caused by component aging or electromagnetic interference. These faults often interact, leading to complex composite characteristics that challenge traditional EV repair methods.
For fault diagnosis, I designed a multi-source data acquisition network incorporating voltage modules, temperature sensor arrays, Hall current sensors, and vibration accelerometers, all synchronized at specific sampling rates: voltage at 100 Hz, temperature at 1 Hz, current at 1 kHz, and vibration at 10 kHz. Data preprocessing involves wavelet denoising, median filtering, and digital filtering to eliminate power frequency interference and random noise. Standardization is applied using the formula: $$z = \frac{x – \mu}{\sigma}$$ where \(z\) is the standardized data, \(x\) is the raw data, \(\mu\) is the mean, and \(\sigma\) is the standard deviation. This ensures comparability across different units. Outlier detection employs the Local Outlier Factor (LOF) algorithm: $$\text{LOF}(p) = \frac{\sum_{o \in N_k(p)} \text{lrd}(o)}{\text{lrd}(p) / |N_k(p)|}$$ where \(\text{LOF}(p)\) is the local outlier factor for point \(p\), \(\text{lrd}\) is the local reachability density, and \(k\) is the number of neighbors. A threshold filters anomalies, and time alignment uses timestamp calibration and interpolation for data fusion. Missing data is handled via Gaussian mixture models and temporal interpolation to maintain integrity.
Feature extraction combines time-domain, frequency-domain, and information entropy characteristics to build a multidimensional feature space. Time-domain features include mean, variance, kurtosis, skewness, waveform factor, and impulse factor, calculated as: $$\text{Kurtosis} = \frac{E[(x – \mu)^4]}{\sigma^4}$$ where \(E\) is the expectation operator, \(\mu\) is the mean, and \(\sigma\) is the standard deviation. Kurtosis reflects waveform steepness, which changes notably during faults. Frequency-domain features are derived from Fast Fourier Transform (FFT) and wavelet packet decomposition for energy spectral density. Information entropy uses sample entropy: $$\text{Sample Entropy} = -\ln \frac{A(m)}{B(m)}$$ where \(A(m)\) and \(B(m)\) are pattern counts for dimension \(m\), indicating signal complexity and fault mode transitions. Additional features like inflection points and slopes from voltage curves, along with temperature gradients and thermal diffusion coefficients, form a comprehensive feature vector. Dimensionality reduction via Principal Component Analysis (PCA) and Independent Component Analysis (ICA) highlights key fault indicators.
The intelligent diagnosis model integrates Convolutional Neural Networks (CNN), Long Short-Term Memory (LSTM) networks, and autoencoders. The CNN uses multi-scale kernels (1×1, 3×3, 5×5) with batch normalization and ReLU activation, followed by max-pooling (stride 2) across six convolutional layers (channels: 32, 64, 128, 256, 512, 512). Residual connections prevent gradient vanishing. The autoencoder employs a symmetric structure, compressing features to one-fourth of the original size, with reconstruction error guiding feature retention. The LSTM module captures temporal evolution with three layers (128, 256, 512 nodes), using forget, input, and output gates with Sigmoid and tanh activations. Dropout layers (rate 0.5) prevent overfitting, and bidirectional LSTM considers forward and backward sequences. Attention mechanisms fuse temporal and spatial features, with a sequence length of 100 time steps and a sliding window of 10. Training uses the Adam optimizer (initial learning rate 0.001), cosine annealing, and a loss function combining categorical cross-entropy and reconstruction mean squared error. Regularization (L1: 0.001, L2: 0.0001) controls overfitting, and early stopping halts training if no improvement occurs in 10 epochs. Model stacking combines Random Forest, XGBoost, and LightGBM with optimized weights via grid search and 5-fold cross-validation for generalization.
| Fault Type | Traditional Method Accuracy | Intelligent Diagnosis Accuracy | Improvement |
|---|---|---|---|
| Electrochemical Fault | 82.5% | 96.3% | 16.7% |
| Mechanical Fault | 85.2% | 95.8% | 12.4% |
| Thermal Management Fault | 80.6% | 94.7% | 17.5% |
| Control System Fault | 83.1% | 96.4% | 16.0% |
For precise repair in electrical car repair, I optimized parameters covering charge-discharge, temperature, and pressure aspects. Charge-discharge parameters include cutoff voltage, charge rate, pulse frequency, and duty cycle, optimized via orthogonal experimental design. Temperature parameters involve heating temperature, holding time, heating rate, and cooling rate, refined using response surface methodology. Pressure parameters, such as assembly preload and welding pressure, are optimized with finite element analysis. A PLC-based closed-loop control ensures precision (pressure: 0.1 kN, temperature: 0.1°C). Repair efficacy is evaluated through capacity recovery rate, internal resistance change, consistency, and cycle life, with a weighted comprehensive model. Reliability verification employs accelerated life tests (high-temperature storage at 55°C for 1000 hours, temperature cycling from -40°C to 85°C for 200 cycles, vibration tests at 5-200 Hz/2g) and environmental stress screening (thermal shock, humidity cycles, mechanical impact). Weibull distribution fits test data to assess reliability, ensuring robust EV repair outcomes.
I developed an experimental platform integrating battery testing, data acquisition, and diagnosis systems. The battery tester (Xinwei) offers voltage range 0-5V (accuracy 0.1%) and current range 0-300A (accuracy 0.1%). Data acquisition uses a 32-channel card (sampling rate up to 1 MHz) with anti-aliasing filters, PT100 temperature sensor arrays (spatial resolution 50 mm), and triaxial accelerometers (bandwidth 50 kHz). Control is handled by an NI PXI platform with a real-time controller and FPGA module for synchronization. The diagnosis system runs on an industrial computer (Intel i7, 32GB RAM, NVIDIA RTX3060 GPU), with custom software for data management and analysis. Communication via CAN and RS485 buses enables device integration, creating a holistic testing environment for EV repair validation.
Repair process parameter calibration involves a three-factor, five-level orthogonal experiment (L25 array) to study the effects of charge rate (0.1C-1C), heating temperature (25°C-60°C), and pressure (0.5 kN-2.5 kN) on repair outcomes. Process monitoring tracks voltage, temperature, and pressure curves, with ANOVA determining parameter significance. Detection standards follow GB/T norms, including capacity tests, internal resistance measurements, insulation checks, and temperature rise tests, forming a graded evaluation system. A MySQL database stores repair data for traceability, ensuring consistency in electrical car repair operations.
| Evaluation Metric | Before Application | After Application | Improvement Rate |
|---|---|---|---|
| Diagnosis Time (min) | 45 | 15 | 66.7% |
| Repair Cost (per instance) | 2800 | 2100 | 25.0% |
| Return Rate | 8.5% | 2.3% | 72.9% |
| Customer Satisfaction | 75% | 95% | 26.7% |
Reliability verification includes accelerated life tests and environmental stress screening. The intelligent diagnosis system demonstrates superior accuracy across fault types, as shown in Table 1, with an average accuracy exceeding 95%. In a 12-month application at EV repair centers, results indicate a 35% increase in repair efficiency, 90% capacity recovery, 30% improvement in internal resistance consistency, and 25% cost reduction. Post-repair tracking shows a 50% decrease in capacity fade rate and a 45% reduction in internal resistance growth. Standardized repair protocols and remote maintenance platforms ensure quality stability, with mean time between failures of 720 hours and response times under 100 ms. Software reliability is confirmed through static code analysis (95% coverage) and functional tests, with database backups ensuring data integrity.
In conclusion, my research on EV battery system fault diagnosis and repair has established an intelligent model based on multi-source data and standardized repair processes. The diagnosis technique rapidly identifies diverse faults, while repair innovations enhance efficiency and reliability. Experimental validation and technical specifications form a complete system, already applied in multiple electrical car repair enterprises. Future work will explore predictive maintenance to further evolve EV repair services, supporting industry advancement and sustainability.