In recent years, the global electric vehicle (EV) industry has experienced exponential growth, driven by advancements in technology and increasing environmental awareness. As a core component of EVs, the power battery plays a critical role in determining vehicle performance, safety, and sustainability. However, the development of repair technologies for these batteries has lagged behind, leading to significant environmental and resource pressures from retired batteries. Traditional repair models often react passively to failures, lacking systematic planning and quality control, which results in low efficiency, high risks, and underutilization of the battery’s remaining value. Existing research tends to focus on isolated aspects, missing a holistic view of the entire lifecycle, thereby creating “information silos” and fragmenting the value chain. Therefore, establishing a repair process system that spans the entire lifecycle has become an urgent industry need. This represents not just a technical optimization but a paradigm shift in industrial development, requiring maintenance considerations to be integrated from the design phase, intelligent predictions during usage, and value regeneration at the end-of-life stage. Such an approach enables effective management of EV power batteries throughout their lifecycle, enhancing EV repair practices and supporting sustainable development.
The lifecycle theory emphasizes a comprehensive evaluation and management of products from raw material acquisition, production, and use to final disposal and recycling. Applying this theory to the field of electric car repair, particularly for power batteries, means that repair activities are no longer isolated, occasional events during the vehicle’s use phase. Instead, they form a dynamic, interconnected management process that runs from cradle to grave. This process can be divided into three interrelated stages: design and manufacturing, operation and use, and end-of-life recycling, each with seamless information exchange. By adopting this perspective, we can address the complexities of EV repair more effectively, ensuring that every stage contributes to the overall efficiency and sustainability of electric car repair systems.
Design and Manufacturing: Front-Loading Repairability
The starting point for lifecycle-based repair does not begin when a fault occurs but at the design stage of the power battery. Traditional battery designs often prioritize energy density, power performance, and cost, with insufficient consideration for subsequent repair, disassembly, and recycling processes. Introducing the concept of repairability design means integrating end-of-life requirements into the initial design and manufacturing phases, fundamentally reducing future repair difficulties, costs, and safety risks. This proactive approach is essential for advancing EV repair methodologies and making electric car repair more accessible and efficient.
Key aspects of repairability design include modular architecture, standardization, diagnostic integration, and material selection. For instance, modular design allows for easy disassembly and replacement of individual cells or modules, minimizing the need to replace entire battery packs and significantly lowering repair costs. Standardization of physical interfaces, communication protocols, and data formats facilitates third-party repair and secondary use, breaking down technical barriers imposed by original manufacturers. Additionally, integrating high-precision sensors and预留 diagnostic interfaces during design enables real-time monitoring by battery management systems (BMS) and supports offline deep diagnostics and cloud-based predictive analytics. This establishes a “health profile” for the battery from the outset, laying the groundwork for precise EV repair. Moreover, material selection should balance environmental concerns and recyclability, using easily separable and recyclable materials with clear labeling and disassembly guidelines to enhance end-of-life recycling efficiency.
| Aspect | Description | Impact on EV Repair |
|---|---|---|
| Modular Design | Enables replacement of specific modules instead of entire packs | Reduces repair costs and time for electric car repair |
| Standardization | Uniform interfaces and protocols for compatibility | Facilitates third-party EV repair and interoperability |
| Diagnostic Integration | Sensors and ports for real-time monitoring and analysis | Enhances predictive maintenance in electric car repair |
| Material Selection | Use of recyclable and separable materials | Supports sustainable end-of-life EV repair processes |
From a mathematical perspective, the benefits of repairability design can be quantified using lifecycle cost models. For example, the total cost of ownership (TCO) for an EV battery can be expressed as:
$$ \text{TCO} = C_{\text{initial}} + C_{\text{maintenance}} + C_{\text{repair}} + C_{\text{end-of-life}} $$
where \( C_{\text{initial}} \) is the initial cost, \( C_{\text{maintenance}} \) is maintenance cost, \( C_{\text{repair}} \) is repair cost, and \( C_{\text{end-of-life}} \) is end-of-life processing cost. By optimizing repairability, we can minimize \( C_{\text{repair}} \) and \( C_{\text{end-of-life}} \), leading to a lower TCO and more sustainable EV repair practices. This underscores the importance of embedding repairability into design, as it directly influences the economics of electric car repair.
Operation and Use: Transition from Passive to Predictive Maintenance
During the operation and use phase, the repair philosophy for EV batteries is shifting from “fix-after-failure” to “warn-before-failure.” Traditional BMS primarily focuses on real-time parameter monitoring and basic protection but lacks predictive capabilities, limiting its ability to detect gradual faults and potential risks. To address this, building a data-driven predictive maintenance system is crucial. Such systems leverage advanced algorithms to forecast battery health, enabling proactive EV repair interventions and reducing downtime in electric car repair scenarios.
The core of this system lies in sophisticated prediction models. In recent years, deep learning algorithms like the Transformer model have demonstrated exceptional performance in handling long-term time-series data, making them highly suitable for predicting battery states. For instance, research shows that by modifying the Transformer framework and incorporating strategies like inverse Sigmoid decay sampling, it is possible to achieve high-precision predictions for key safety parameters such as battery temperature, internal gas pressure, and oxygen concentration. The mean squared error loss function can be reduced by up to 8.15% compared to the original model. Deploying such prediction models on cloud platforms enables the creation of a lifecycle monitoring and warning micro-server. This server continuously analyzes real-time data streams from vehicles, uncovers intrinsic data correlations, predicts future battery health states and remaining useful life, and provides early warnings for sudden safety events like thermal runaway or electrolyte leakage. This approach significantly advances the repair window, transforming reactive repairs into proactive interventions, thereby enhancing safety and allowing for more flexible maintenance planning for EV owners and repair shops.
Mathematically, the predictive model can be represented using time-series forecasting equations. For example, the state of health (SOH) prediction can be modeled as:
$$ \text{SOH}(t) = f(\mathbf{X}(t), \theta) + \epsilon(t) $$
where \( \text{SOH}(t) \) is the state of health at time \( t \), \( \mathbf{X}(t) \) is a vector of input features (e.g., voltage, current, temperature), \( \theta \) represents the model parameters, and \( \epsilon(t) \) is the error term. The function \( f \) can be implemented using a Transformer-based neural network, which captures long-range dependencies in the data. The training objective is to minimize the loss function, such as mean squared error:
$$ \mathcal{L} = \frac{1}{N} \sum_{i=1}^{N} (\text{SOH}_{\text{predicted}, i} – \text{SOH}_{\text{actual}, i})^2 $$
This predictive capability is revolutionizing EV repair by enabling condition-based maintenance, where repairs are scheduled based on actual battery degradation rather than fixed intervals, optimizing resource allocation in electric car repair operations.
| Maintenance Type | Description | Advantages for Electric Car Repair |
|---|---|---|
| Reactive | Repair after failure occurs | Simple but leads to high downtime and costs |
| Preventive | Scheduled maintenance at fixed intervals | Reduces failures but may involve unnecessary EV repair |
| Predictive | Maintenance based on real-time data and predictions | Minimizes downtime and optimizes EV repair resources |
End-of-Life Recycling: Extending the Repair Value Chain
When a power battery’s capacity degrades to a point where it can no longer meet the demands of EV operation, its lifecycle does not end; instead, it enters a phase where the repair value chain is extended and closed. The core task at this stage is to conduct a scientific assessment of the retired battery’s residual value and make a final “repair” decision—either “grading use” or “disassembly recycling”—based on the evaluation. This decision heavily relies on the battery’s “health profile” accumulated throughout its use phase, which is integral to efficient EV repair management.
Grading use involves applying retired batteries to scenarios with lower energy density and power requirements, such as energy storage systems, low-speed electric vehicles, or backup power for communication base stations. It is the primary pathway for maximizing the remaining value of batteries. Successful implementation depends on accurate assessment of module consistency, remaining capacity, and safety within the battery pack, necessitating a robust technical standard system and information traceability platform. This ensures that batteries are traceable and their status is known, facilitating informed decisions in electric car repair and recycling.
If the battery condition is unsuitable for grading use, it proceeds to disassembly and recycling. This involves using physical or chemical methods to efficiently recover valuable metals like lithium, cobalt, and nickel. Studies indicate that advanced recycling processes, such as pyrometallurgical-hydrometallurgical combined recycling, can achieve significant environmental benefits. For example, per kilowatt-hour of battery recycled, this method can reduce carbon emissions by up to 153.57 kg CO₂ equivalent. Building an efficient and eco-friendly recycling network and reintegrating recovered materials into the battery manufacturing front-end are key steps in achieving closed-loop lifecycle management and supporting global carbon neutrality goals, thereby enhancing the sustainability of EV repair ecosystems.
The economic and environmental benefits of recycling can be quantified using formulas. For instance, the carbon reduction benefit \( B_{\text{CO}_2} \) from recycling can be calculated as:
$$ B_{\text{CO}_2} = \sum_{i} m_i \times \text{EF}_i \times \eta_i $$
where \( m_i \) is the mass of metal \( i \) recovered, \( \text{EF}_i \) is the emission factor for producing metal \( i \) from virgin sources, and \( \eta_i \) is the recovery efficiency. Additionally, the residual value \( V_{\text{residual}} \) of a retired battery can be estimated as:
$$ V_{\text{residual}} = \max(V_{\text{grading}}, V_{\text{recycling}}) $$
where \( V_{\text{grading}} \) is the value from grading use and \( V_{\text{recycling}} \) is the value from recycling. This decision-making process is critical for optimizing the end-of-life phase in electric car repair, ensuring that resources are utilized effectively.
| Option | Description | Benefits for EV Repair |
|---|---|---|
| Grading Use | Reuse in less demanding applications | Extends battery life and reduces waste in EV repair |
| Recycling | Recovery of valuable materials | Supports circular economy and lowers electric car repair costs |
Typical Fault Repair Techniques in EV Batteries
In the context of EV repair, several common faults require specific diagnostic and repair strategies. Understanding these is essential for effective electric car repair, as they impact safety, performance, and longevity. Below, we discuss key fault types and their repair methodologies, emphasizing the importance of a systematic approach in EV repair practices.
Pre-charge Failure and High-Voltage Power-On Issues
Pre-charge failure and inability to power on the high-voltage system can prevent vehicle startup, often caused by faults in pre-charge resistors, main relays, or external high-voltage load short circuits. In EV repair, the first step is to ensure safety by disconnecting the power, opening the维修 switch, and allowing residual voltage to dissipate. A multimeter is used to confirm zero voltage in the high-voltage circuit. Then, a dedicated diagnostic tool is connected to activate the high-voltage system and monitor voltage changes on the high-voltage bus during pre-charge. If the voltage remains at or near zero, it indicates an interruption in the pre-charge circuit. Technicians should follow a “simple-to-complex” sequence to inspect internal high-voltage fuses, pre-charge resistors, and pre-charge relays for faults. If the voltage rises close to the total battery voltage during pre-charge but drops rapidly to zero when the main relay engages, the fault may lie with the main relay or BMS control端. In such cases, checking the main relay contacts and BMS signals is necessary. After replacing components, retesting and clearing BMS fault codes are crucial to ensure resolution, highlighting the precision required in electric car repair.
Insulation Faults
Insulation performance is critical for the safety of high-voltage systems, directly affecting the well-being of occupants and repair personnel. For insulation faults in EV repair, diagnosis begins with reading the insulation impedance value via a diagnostic tool or insulation monitoring module. If it falls below the safety threshold, a segmented exclusion method is applied. Repair personnel disconnect high-voltage components—such as electric compressors, PTC heaters, onboard chargers, and DC-DC converters—in a standard sequence, measuring insulation resistance after each disconnection to isolate the faulty assembly using an “exclusion approach.” If the fault persists after excluding all external components, it points to the battery pack interior. The pack must be removed and transferred to a dedicated repair area for airtightness testing. Injecting dry air at specific pressures and monitoring pressure changes helps identify issues like case damage, seal aging, or water ingress in high-voltage connectors. Finally, internal structures are inspected step by step—high-voltage wiring, busbars, and modules—to locate the exact fault point. Faulty parts are replaced to resolve the issue, underscoring the methodical nature of electric car repair for safety-critical systems.
Excessive Cell Voltage Difference or Under-voltage
Excessive voltage differences or under-voltage among individual cells can impair battery performance, range, and lifespan, commonly seen in vehicles that have been idle for long periods or misused. In EV repair, the approach varies based on the battery chemistry. For lithium iron phosphate (LFP) batteries, which have a flat voltage plateau and better tolerance to slight over-discharge, professional balancing charging equipment can be used for low-current, long-duration equalization charging to attempt recovery of problematic cell voltages. For ternary lithium batteries, which are sensitive to deep discharge and may suffer irreversible damage from severe under-voltage, if equalization charging is ineffective or voltage differences are too large, forced charging is not recommended. Instead, direct replacement of faulty cells or modules is advised. During replacement, operations should be conducted in an insulated environment, with bolts tightened to manufacturer specifications using a calibrated torque wrench. After replacement, new cells or modules are coded, the BMS is calibrated and verified, and charge-discharge tests are performed to ensure performance restoration. This tailored approach is vital for effective electric car repair across different battery types.
| Fault Type | Common Causes | Repair Techniques in Electric Car Repair |
|---|---|---|
| Pre-charge Failure | Faulty resistors, relays, or short circuits | Sequential inspection and component replacement |
| Insulation Faults | Component failure or moisture ingress | Segmented testing and airtightness checks |
| Cell Voltage Issues | Long-term inactivity or misuse | Chemistry-specific equalization or replacement |
In mathematical terms, the diagnosis process for these faults can be modeled using decision trees or fault tree analysis. For example, the probability of a fault \( P(F) \) can be expressed as:
$$ P(F) = \sum_{i} P(C_i) \times P(F | C_i) $$
where \( P(C_i) \) is the probability of cause \( C_i \), and \( P(F | C_i) \) is the conditional probability of fault \( F \) given cause \( C_i \). This probabilistic approach aids in streamlining EV repair diagnostics, making electric car repair more efficient and reliable.
Challenges and Strategies in Lifecycle-Based EV Battery Repair
Despite the potential benefits, implementing lifecycle-based repair technologies for EV batteries faces several challenges. Addressing these is crucial for advancing EV repair practices and ensuring the sustainability of electric car repair ecosystems. Key issues include technical bottlenecks, lagging standard systems, and insufficient industry chain collaboration, all of which impact the efficiency and safety of EV repair operations.
Technical Bottlenecks
Efficient and safe disassembly and recycling technologies are pivotal for closing the value chain loop in EV batteries. Currently, the disassembly of used battery packs relies heavily on manual operations, resulting in low efficiency and high safety risks. Developing automated, flexible intelligent disassembly production lines—using machine vision and force feedback technologies to adapt to different battery pack models—is a key future direction. Additionally, while traditional material recycling methods like hydrometallurgy and pyrometallurgy are relatively mature, there is room for improvement in environmental friendliness, cost control, and metal recovery rates. Exploring greener alternatives, such as physical recycling and directed循环 technologies, could overcome these limitations. These advancements would significantly enhance the capabilities of EV repair by making end-of-life processes more sustainable and cost-effective for electric car repair networks.
Lagging Standard System Construction
Standards are the cornerstone of industry规范化, but in the field of EV battery repair, standard system development lags behind technological and market progress. Existing standards are often recommendatory or guidance-based, lacking mandatory and operable details in critical areas like diagnostic procedures, repair operations, quality assessments, and safety specifications. This leads to uneven service quality and numerous safety hazards in the market. To address this, a hierarchical standard system framework should be reconstructed. The top level should consist of national mandatory basic standards, defining safety red lines, environmental baselines, and unified data interface requirements. The middle layer should include industry recommendatory standards, providing detailed technical specifications and process guidelines for different battery chemistries (e.g., LFP, ternary lithium) and application scenarios (e.g., commercial vehicles, private cars). The bottom layer should comprise enterprise and group standards, refining upper-level standards into specific operating procedures, internal quality control requirements, and innovative practices. This structured approach ensures effective implementation and raises the overall standard of electric car repair.
Insufficient Industry Chain Collaboration
Achieving full lifecycle management of EV batteries depends on seamless connectivity and information sharing across the industry chain. However, “information silos” are prevalent, with limited communication among vehicle manufacturers, battery producers, repair enterprises, recycling companies, and grading use entities. Key information—such as design data, production details, BMS data, repair records, and retirement status—fails to form an effective closed loop, severely hindering resource optimization and overall chain efficiency. In response, a national or industry-level power battery lifecycle traceability management platform should be established. Drawing on concepts like extended producer responsibility and diversified recycling models, each battery should be assigned a unique “digital identity file.” From production onward, data on design parameters, manufacturing batches, installation information, charging-discharging data, maintenance records, fault history, residual value assessments, and final recycling should be recorded in real time and uploaded to the platform. Blockchain technology can ensure data authenticity and immutability, with strict permission management granting legitimate participants access to query and input interfaces. This “digital battery passport” system would dismantle information barriers, enabling repair companies to access accurate battery histories for diagnostics, grading use firms to conduct reliable residual value assessments, and regulators to track battery flows comprehensively. Ultimately, this fosters true lifecycle collaborative management, revolutionizing EV repair and electric car repair processes.
| Challenge | Description | Strategy for Electric Car Repair |
|---|---|---|
| Technical Bottlenecks | Manual disassembly and inefficient recycling | Develop automated systems and greener technologies |
| Standard Lag | Lack of enforceable repair standards | Implement hierarchical standard frameworks |
| Chain Collaboration | Information silos and poor data sharing | Establish traceability platforms with blockchain |
From an optimization perspective, the benefits of collaboration can be modeled using game theory or network analysis. For instance, the overall efficiency \( E \) of the EV repair ecosystem can be expressed as:
$$ E = \alpha \cdot T + \beta \cdot S + \gamma \cdot C $$
where \( T \) represents technical advancement, \( S \) standard compliance, and \( C \) collaboration level, with \( \alpha, \beta, \gamma \) as weighting factors. Maximizing \( E \) requires balanced improvements across all areas, highlighting the interconnected nature of challenges in electric car repair.
Conclusion
Constructing a lifecycle-oriented repair technology system for EV power batteries is of immense significance and an inevitable trend. Although current challenges—such as technical limitations, standard inadequacies, and industry chain coordination issues—pose obstacles, collaborative efforts from relevant departments, industries, and enterprises can overcome them. By focusing on key technology research and development,完善 standard systems, and enhancing information sharing and synergy across the chain, we can make strides forward. This will not only improve the efficiency and quality of EV repair, reduce safety risks, and achieve efficient resource utilization but also support green, sustainable development for the EV industry. As we advance, integrating lifecycle thinking into every aspect of electric car repair will lay a solid foundation for long-term industry growth, ensuring that EVs remain a cornerstone of modern transportation.
In summary, the evolution of EV repair hinges on a holistic, data-driven approach that spans the entire battery lifecycle. By embracing innovations in design, predictive maintenance, and recycling, we can transform electric car repair into a more sustainable and efficient practice, ultimately benefiting consumers, businesses, and the environment alike.