In the context of global energy transition and green low-carbon development, electric vehicles (EVs) have become a critical component of modern transportation systems. Despite advancements in power battery technology, numerous challenges persist in practical applications, such as short battery lifespan, poor consistency in cell performance, thermal safety hazards, thermal runaway in battery packs, and false or missed alarms in thermal protection systems. These issues lead to frequent vehicle accidents, escalating post-incident repair and management costs, and significantly hinder the widespread adoption of EVs. As a researcher focused on enhancing EV repair processes, I have developed an integrated system that addresses these problems through a comprehensive approach combining data acquisition, modeling, artificial intelligence, and digital twin technologies. This system establishes a seamless “diagnosis-location-repair-evaluation” chain, aiming to improve the efficiency and reliability of electrical car repair operations.
The core of our system lies in a hierarchical data acquisition and multi-source fusion monitoring mechanism. We deploy high-density voltage, temperature, internal resistance, and current sensors within the power battery pack to enable per-cell data collection, ensuring no aggregation or inversion issues. Additionally, high-sensitivity thermal flow or pressure sensors are embedded to detect non-electrical parameter anomalies, such as thermal runaway. To meet engineering demands for high speed and low latency, we adopt communication protocols like CAN-FD or Ethernet. On the software side, data from various nodes—including the BMS master controller, charging management system, and vehicle control unit (VCU)—are structurally encapsulated and fused into a unified timestamped data stream. This allows edge computing gateways to perform preliminary data classification, feature recognition, and prediction processing, reducing uplink bandwidth pressure. Redundancy designs, such as sensor signal self-consistency schemes, enhance system fault tolerance, ensuring overall reliability and integrity in data acquisition for EV repair diagnostics.
To model battery behavior with physical significance, we employ mechanism-based approaches centered on thermal-electrical-chemical coupling. Specifically, we use Equivalent Circuit Models (ECM) and Electrochemical Impedance Spectroscopy (EIS) for multi-level modeling from cells to modules and packs. Parameters are derived from calibration tests, and adaptive state parameters are refined in real-time using self-learning methods like Extended Kalman Filters (EKF) or sliding mode observers. The health state is determined by analyzing residuals—the differences between measured states and model predictions. If the residual consistently exceeds a threshold, a functional fault is indicated. For instance, the state of charge (SOC) can be estimated using a discrete-time model: $$SOC_{k+1} = SOC_k – \frac{I_k \Delta t}{C_n}$$ where \(I_k\) is the current, \(\Delta t\) is the time step, and \(C_n\) is the nominal capacity. Similarly, the state of health (SOH) is modeled as: $$SOH = \frac{C_{actual}}{C_{initial}} \times 100\%$$ This model also simulates degradation phenomena like SEI growth and lithium dendrite formation, providing long-term performance predictions. Integration with edge processing units ensures real-time operation and self-calibration capabilities, crucial for accurate electrical car repair assessments.
Addressing the nonlinear, multi-operational, and multi-dimensional characteristics of battery packs, we incorporate artificial intelligence through a deep learning diagnostic engine. This engine is built on a CNN-LSTM network architecture, where Convolutional Neural Networks (CNN) extract local time-frequency features from operational signals, and Long Short-Term Memory (LSTM) networks capture temporal dependencies. To handle multiple fault types—such as thermal runaway, balance imbalances, aging, and internal short circuits—we implement a multi-task learning framework. This allows simultaneous classification and regression tasks within a single neural network, enabling fault type identification and severity assessment. Training relies on extensive labeled datasets, augmented with techniques like data augmentation, adversarial learning, and transfer learning to enhance model robustness. Deployed on cloud or System-on-Chip (SOC) platforms, the model is optimized via distillation compression for millisecond-level response times. Furthermore, collaboration with knowledge graph modules facilitates semantic-level tracing and causal inference for latent faults under abnormal conditions, advancing the intelligence of EV repair processes.
Leveraging digital twin technology, we establish a cloud-edge collaborative closed-loop diagnostic and intelligent maintenance platform. A digital twin of the real battery system is maintained in the cloud, reflecting the physical entity’s data and health status in real-time. This bidirectional mapping enables virtual and physical systems to interact dynamically. In this architecture, edge terminals upload collected data to the cloud management platform, where a digital twin engine models the battery’s real-time operational state. Based on inferences from physical models and AI, the platform provides metrics like State of Health (SOH), fault mechanisms, and Remaining Useful Life (RUL) predictions. It also generates proactive maintenance strategies, shifting from reactive “break-fix” approaches to predictive interventions. For example, the platform can issue smart alerts and dispatch service engineers preemptively, optimizing resource allocation and minimizing downtime in electrical car repair scenarios.
To optimize the diagnostic and repair workflow, we implement a graded diagnosis and layered repair mechanism. This approach ensures precise fault localization and efficient resource utilization across different severity levels. The diagnosis unit is divided into three layers: cell, module, and pack. Faults are categorized into three grades: Grade 1 (minor faults) managed via adaptive vehicle control strategies; Grade 2 (moderate faults), such as cell power imbalance or thermal management anomalies, requiring module-level interventions at repair stations; and Grade 3 (severe faults), like short circuits or thermal runaway precursors, necessitating manufacturer-level返修. In the repair process, edge gateways upload real-time diagnostic information to a maintenance management and dispatch platform, which allocates resources based on fault severity. For Grades 1 and 2, on-site actions like sensor replacement, cooling parameter adjustments, or localized equalization charging are performed. Grade 3 faults involve entire module replacements, enhancing both repair efficiency and cost-effectiveness in EV repair operations.
We further develop an intelligent maintenance platform to transition from passive, responsive repairs to predictive, intervention-based approaches. This platform integrates decision-making, resource scheduling, historical maintenance management, and continuous optimization. Edge computing nodes collect operational data and upload preliminary diagnoses to the platform, where a backend computing center performs comprehensive fault analysis and formulates repair strategies. The platform’s core functionalities include: a fault diagnosis module with deep learning models and expert rule engines for multi-level fault classification; a maintenance strategy module that intelligently dispatches resources based on fault grade, vehicle location, battery charge, and repair station status; a maintenance knowledge module storing historical records, part replacements, and fault distributions to aid technician decisions; and an OTA upgrade module for updating diagnostic algorithms and BMS management strategies. By interfacing with enterprise ERP and third-party logistics systems, the platform automates work order generation, parts dispatch, and status confirmation, creating a closed-loop EV repair ecosystem.
Post-repair, recalibrating and upgrading the Battery Management System (BMS) is essential for ensuring subsequent operational safety, control accuracy, and battery health assessment. This recalibration involves three key aspects: system parameter calibration, algorithm or strategy reconstruction, and system policy upgrades. First, after repairing faults or replacing battery modules, parameters for SOC, SOH, and State of Power (SOP) estimation models are re-identified. High-precision charge-discharge test data and manufacturer electrical models are used to build optimization models for iterative parameter identification, improving the accuracy of remaining charge and health estimates. For example, SOC estimation can be refined using a recursive least squares approach: $$\theta_{k+1} = \theta_k + K_{k+1} (y_{k+1} – x_{k+1}^T \theta_k)$$ where \(\theta\) represents parameters, \(K\) is the gain, and \(y\) and \(x\) are measurements and states. Second, BMS internal fault diagnosis algorithms are reconstructed by replacing static threshold-based methods with data-driven dynamic models. This enhances sensitivity to micro-faults, thermal anomalies, and internal short circuits, while incorporating tolerance for redundant cell operation and increased thermal management trigger sensitivity to improve robustness in electrical car repair contexts.
In practical applications, we validate the feasibility and intelligence of our EV battery fault diagnosis and repair system through empirical testing. We select a specific EV model and design a complete vehicle-edge-cloud system. The vehicle end is equipped with high-precision CAN FD communication and an intelligent BMS module, integrating multiple temperature, voltage, and current sampling points using TI BQ series chips. The edge end features a gateway with an NXP i.MX8 processor for preprocessing and local inference. The cloud end utilizes an Alibaba Cloud server (8-core, 32 GB virtual server) embedded with a PyTorch-based deep diagnosis engine and a digital twin environment simulator. Testing environments simulate real-world conditions, including urban, highway, heavy restart, and thermal soak scenarios. We employ 20 vehicles over 90 days, generating fault data through manual injection, historical log playback, and device simulation. Key metrics such as system response times, maintenance support efficiency, and work order execution are collected to evaluate performance.
The application results demonstrate significant improvements across various indicators compared to traditional repair processes. For instance, the average fault identification time is reduced dramatically due to real-time edge node diagnostics and early warnings. The integration of multi-dimensional model fusion algorithms based on AI lowers misdiagnosis rates, while intelligent scheduling and module-level repair strategies enable precise localization of faulty components. Additionally, the use of OTA remote diagnosis and the intelligent maintenance platform facilitates real-time data collection and dynamic monitoring, leading to higher user satisfaction. The following table summarizes the comparative performance metrics:
| Metric | Without System Support / Traditional Process | With Our System |
|---|---|---|
| Average Fault Identification Time (min) | 58 | 8 |
| Misdiagnosis Rate (%) | 11.7 | 2.3 |
| Average Repair Response Time (h) | 72 | 1.9 |
| Module-Level Repair Rate (%) | 42 | 93 |
| User Repair Satisfaction Score (10-point scale) | 6.3 | 9.1 |
Furthermore, the system’s impact on State of Charge (SOC) estimation is illustrated through simulation comparisons. Before implementation, SOC simulations often show inconsistencies and inaccuracies under dynamic conditions, whereas after applying our system, SOC trajectories align more closely with actual values, enhancing reliability in EV repair diagnostics. The SOC dynamics can be modeled using a combined approach: $$SOC(t) = SOC_0 – \int_0^t \frac{I(\tau)}{Q(\tau)} d\tau + \eta \Delta t$$ where \(SOC_0\) is the initial state, \(I\) is current, \(Q\) is capacity, and \(\eta\) represents efficiency factors. This improvement underscores the system’s ability to provide real-time, accurate state assessments, which are vital for effective electrical car repair and maintenance.
In conclusion, the rapid evolution of intelligent connected vehicles necessitates advanced solutions for EV battery fault diagnosis and repair. Our system, built on multi-level data acquisition, intelligently coupled diagnostic models, cloud-edge collaborative platforms, and精细化 repair strategies, establishes a new paradigm for battery health management over the full lifecycle. This approach transcends point-based fault repair by leveraging data, models, and platform synergy to achieve a shift from static to dynamic perception and from passive to proactive prediction. By enhancing real-time monitoring, intelligent strategy matching, and resource scheduling capabilities, it offers efficient and feasible technical support for the entire lifespan of EV batteries, holding substantial engineering application prospects and promotion value. As we continue to refine these methodologies, the integration of emerging technologies like digital twins and AI will further revolutionize EV repair processes, contributing to safer, more reliable electric mobility.