In recent years, the rapid development of the new energy vehicle industry has led to a significant increase in production, sales, and ownership. As a core component, the performance and safety of the EV power battery are critical. However, maintenance technologies for these batteries lag behind, and the large number of retired batteries poses environmental and resource pressures. Current maintenance models are reactive, addressing faults after they occur, and lack systematic planning and quality control, resulting in low efficiency, high risks, and underutilization of the battery’s remaining value. Existing research often focuses on specific stages, missing a holistic view of the entire lifecycle, which creates information silos and disrupts the value chain continuity. Therefore, establishing a maintenance process system that spans the entire lifecycle has become an urgent industry need. This is not merely a technical optimization but a paradigm shift in industrial development, requiring proactive maintenance thinking from the design phase, intelligent prediction during usage, and value regeneration extending to the end-of-life stage, enabling effective management of the EV power battery throughout its lifecycle.
The lifecycle theory emphasizes comprehensive evaluation and management of a product from raw material acquisition, production, and use to final disposal and recycling. Applying this theory to the maintenance of EV power batteries means that maintenance is no longer an isolated, occasional event during the vehicle’s use phase but a dynamic, continuous management process that runs through the entire lifecycle. This process can be divided into three interconnected and information-sharing stages: design and manufacturing, operation and use, and end-of-life recycling. Each stage plays a crucial role in optimizing the performance and sustainability of China EV battery systems.
Design and Manufacturing: Front-Loading Maintainability Design
The starting point of lifecycle maintenance is not when a fault occurs but during the design of the EV power battery. Traditional battery design often prioritizes energy density, power performance, and cost, with insufficient consideration for subsequent maintenance, disassembly, and recycling. Introducing maintainability design concepts means incorporating backend lifecycle needs into the design and manufacturing phases, fundamentally reducing future maintenance difficulty, cost, and safety risks. Maintainability design should focus on four key aspects. First, modular design allows for easy disassembly and replacement of specific modules when individual cells fail, avoiding the need to replace the entire battery pack and significantly lowering maintenance costs. Second, standardized physical interfaces, communication protocols, and data formats facilitate third-party maintenance and secondary use, breaking down technical barriers imposed by original manufacturers. Third, integrating high-precision sensors and diagnostic interfaces during design enables real-time monitoring by the battery management system and supports offline deep diagnostics and cloud-based predictive analysis, establishing a “health record” from the outset and laying the foundation for precise maintenance. Fourth, material selection should balance environmental friendliness and recyclability, using easily separable materials and providing disassembly guidance to improve end-of-life recycling efficiency and benefits. For instance, the adoption of modular designs in China EV battery production can enhance repairability and reduce waste.
To quantify the benefits of maintainability design, consider the following table comparing traditional and lifecycle-based approaches for EV power battery systems:
| Design Aspect | Traditional Approach | Lifecycle-Based Approach | Impact on Maintenance |
|---|---|---|---|
| Modularity | Integrated packs | Modular cells and modules | Reduces replacement cost by up to 60% |
| Standardization | Proprietary interfaces | Open standards | Increases third-party repair access by 40% |
| Sensor Integration | Basic monitoring | High-precision sensors | Enables 95% accuracy in fault prediction |
| Material Recyclability | Mixed materials | Easily separable materials | Improves recycling efficiency by 30% |
Moreover, the economic and environmental impacts can be modeled using formulas. For example, the total lifecycle cost \( C_{\text{lifecycle}} \) of an EV power battery can be expressed as:
$$ C_{\text{lifecycle}} = C_{\text{design}} + C_{\text{manufacturing}} + C_{\text{maintenance}} + C_{\text{recycling}} $$
where \( C_{\text{maintenance}} \) is influenced by maintainability factors such as modularity. A simplified model for maintenance cost reduction is:
$$ \Delta C_{\text{maintenance}} = k_m \cdot M \cdot \frac{1}{D} $$
Here, \( k_m \) is a maintainability coefficient, \( M \) represents modularity level, and \( D \) is the design complexity. For China EV battery applications, higher \( M \) values correlate with lower lifecycle costs.
Operation and Use: Transition from Reactive to Proactive Maintenance
During the operation and use phase, the maintenance philosophy for EV power batteries is shifting from “repair after failure” to “warning before failure.” Traditional battery management systems primarily monitor parameters in real-time and provide basic protection but lack predictive capabilities, limiting their ability to detect gradual faults and potential risks. Building a data-driven predictive maintenance system is key to addressing this. The core of this system is advanced prediction models. In recent years, deep learning algorithms like the Transformer model have shown exceptional performance in handling long time-series data, making them highly suitable for predicting the state of EV power batteries. Research indicates that modifying the Transformer framework with strategies such as inverse Sigmoid decay sampling can achieve high-precision predictions of critical safety parameters like battery temperature, internal gas pressure, and oxygen concentration, reducing the mean squared loss function by 8.15% compared to the original model. Deploying such prediction models in the cloud enables the creation of a full lifecycle monitoring and warning micro-server. This server continuously analyzes real-time data streams from vehicles, uncovers intrinsic data relationships, 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 maintenance window, transforming reactive repairs into proactive interventions, greatly enhancing driving safety, and offering more flexible maintenance planning for owners and repair services, thereby optimizing resource allocation. For China EV battery management, this predictive capability is essential for extending battery life and ensuring reliability.
The predictive maintenance process can be summarized using mathematical models. For instance, the state of health (SOH) of an EV power battery can be estimated using a degradation model:
$$ \text{SOH}(t) = \text{SOH}_0 – \int_0^t \lambda(\tau) \, d\tau $$
where \( \text{SOH}_0 \) is the initial health state, and \( \lambda(t) \) is the degradation rate, which can be predicted using a Transformer-based model. The model’s output for parameters like temperature \( T \) can be expressed as:
$$ T_{\text{predicted}} = f_{\text{Transformer}}(T_{\text{historical}}, \theta) $$
Here, \( f_{\text{Transformer}} \) denotes the Transformer function with parameters \( \theta \), and \( T_{\text{historical}} \) is the historical temperature data. This allows for accurate forecasting of failures in China EV battery systems.
To illustrate the effectiveness of predictive maintenance, the following table shows a comparison between reactive and proactive approaches for EV power battery maintenance:
| Maintenance Aspect | Reactive Approach | Proactive Approach | Improvement |
|---|---|---|---|
| Fault Detection | After occurrence | Before occurrence | Reduces downtime by 70% |
| Cost per Repair | High (emergency fixes) | Low (scheduled maintenance) | Decreases by 50% |
| Battery Lifespan | Shortened due to stress | Extended by 20-30% | Enhances sustainability |
| Data Utilization | Limited to diagnostics | Full lifecycle analysis | Improves prediction accuracy to 95% |
End-of-Life Recycling: Extending the Maintenance Value Chain and Closing the Loop
When an EV power battery’s capacity degrades to a point where it can no longer meet the demands of electric vehicle operation, its lifecycle does not end; instead, it enters the phase of value chain extension and closure. The core task at this stage is to conduct a scientific assessment of the battery’s residual value and make a final “maintenance” decision based on that assessment—either “second-life use” or “disassembly and recycling.” This decision heavily relies on the battery’s “health record” maintained throughout its use phase. Second-life use involves repurposing retired batteries for applications with lower energy density and power requirements, such as energy storage stations, low-speed electric vehicles, or backup power for communication base stations. It is the primary way to maximize the remaining value of the battery. The successful implementation of second-life use depends on accurately evaluating the consistency, remaining capacity, and safety of modules within the battery pack, which requires a comprehensive technical standard system and an information traceability platform to ensure that battery sources are verifiable and their states are known. If the battery’s condition is no longer suitable for second-life use, it proceeds to the final disassembly and recycling stage. This involves using physical or chemical methods to efficiently recover high-value metals like lithium, cobalt, and nickel from the EV power battery. Studies show that advanced recycling processes, such as combined pyrometallurgical-hydrometallurgical techniques, can achieve emission reduction benefits of up to 153.57 kg CO2 equivalent per kilowatt-hour of battery recycled, highlighting significant environmental advantages. Building an efficient and environmentally friendly recycling network and reintegrating recovered materials into the front end of battery manufacturing are key measures for achieving closed-loop management of the EV power battery lifecycle and supporting national “dual-carbon” strategies. In the context of China EV battery recycling, this approach promotes circular economy principles.
The recycling efficiency can be modeled using formulas. For example, the carbon emission reduction \( E_{\text{reduction}} \) from recycling an EV power battery is given by:
$$ E_{\text{reduction}} = \sum_{i} m_i \cdot r_i \cdot \alpha_i $$
where \( m_i \) is the mass of metal \( i \) (e.g., lithium, cobalt), \( r_i \) is the recovery rate, and \( \alpha_i \) is the emission factor per unit mass. For China EV battery recycling, optimizing \( r_i \) through advanced methods can maximize environmental benefits.
The following table summarizes the key aspects of end-of-life management for EV power batteries:
| End-of-Life Stage | Process | Benefits | Challenges |
|---|---|---|---|
| Second-Life Use | Repurposing for lower-demand applications | Extends value; reduces waste by 60% | Need for accurate state assessment |
| Disassembly and Recycling | Recovery of metals via chemical/physical methods | Cuts emissions by 150+ kg CO2e/kWh | High cost and technical complexity |
| Closed-Loop Integration | Reusing materials in new batteries | Lowers raw material use by 40% | Requires standardized processes |
Typical Fault Maintenance Technologies for EV Power Batteries
Common faults in EV power batteries include pre-charge failure, high-voltage power-on issues, insulation faults, and excessive voltage differences or under-voltage in individual cells. Addressing these requires specialized techniques to ensure safety and efficiency. For pre-charge failure and inability to power on high-voltage systems, the cause often lies in pre-charge resistor faults, main relay issues, or external high-load short circuits. The maintenance process begins with safety power-off: disconnecting the maintenance switch, allowing residual voltage to dissipate, and using a multimeter 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 during pre-charge. If the voltage remains near zero, it indicates an interruption in the pre-charge loop, and internal components like fuses, resistors, and relays are checked in order of simplicity. If the voltage rises close to the total battery voltage but drops rapidly when the main relay engages, the fault may be in the main relay or battery management system control end, requiring inspection of relay contacts and signals. After replacing faulty parts, tests are rerun, and battery management system error codes are checked to ensure resolution. This method is critical for maintaining the reliability of China EV battery systems.
Insulation faults are particularly critical as they directly impact the safety of occupants and maintenance personnel. Diagnosis starts with reading the insulation resistance value via a diagnostic tool or insulation monitoring module. If it falls below the safe threshold, a segmented exclusion method is applied. Maintenance personnel disconnect high-voltage components like electric compressors, PTC heaters, onboard chargers, and DC converters in sequence, remeasuring insulation resistance after each disconnection to isolate the faulty assembly. If the fault persists after excluding all external components, it points to issues within the battery pack itself. The pack must be removed and moved to a dedicated repair area for airtightness testing, where dry air is injected at specific pressures to monitor for leaks from shell damage, seal aging, or water ingress in high-voltage connectors. Finally, internal structures such as high-voltage wiring, busbars, and modules are inspected individually to locate the exact fault point, and faulty components are replaced to resolve the issue. This thorough approach is essential for the safe operation of EV power batteries.
For faults involving excessive voltage differences or under-voltage in individual cells, which affect battery performance, range, and lifespan—common in long-idle or misused vehicles—maintenance varies by battery chemistry. Lithium iron phosphate batteries have a flat voltage plateau and better tolerance to slight over-discharge; they can be treated with professional balancing charging equipment for long-term, low-current equalization to restore problematic cell voltages. In contrast, ternary lithium batteries are sensitive to deep discharge, and severe under-voltage can cause irreversible internal damage; if equalization charging is ineffective or voltage differences are too large, forced charging is not recommended, and direct replacement of faulty cells or modules is advised. During replacement, operations should occur in an insulated environment, with bolts tightened to manufacturer standards using calibrated torque wrenches. After replacement, new cells or modules are coded, the battery management system is calibrated and verified, and charge-discharge tests are conducted to ensure performance recovery. These techniques are vital for optimizing the lifecycle of China EV battery units.
To summarize the maintenance steps for common faults in EV power batteries, the following table provides a concise guide:
| Fault Type | Diagnosis Method | Maintenance Steps | Key Considerations |
|---|---|---|---|
| Pre-charge Failure | Voltage monitoring during pre-charge | Check fuses, resistors, relays; replace parts | Ensure zero voltage before work; test post-repair |
| Insulation Fault | Insulation resistance measurement | Disconnect components; test airtightness; replace faulty parts | Prioritize safety; use dedicated areas for internal checks |
| Cell Voltage Imbalance | Voltage difference analysis | Equalize charge or replace cells; calibrate BMS | Adapt to battery chemistry; avoid forced charging for ternary lithium |
Mathematically, the voltage balance in an EV power battery can be described using a formula for cell voltage variance \( \sigma^2 \):
$$ \sigma^2 = \frac{1}{N} \sum_{i=1}^{N} (V_i – \bar{V})^2 $$
where \( V_i \) is the voltage of cell \( i \), \( \bar{V} \) is the average voltage, and \( N \) is the number of cells. Maintenance aims to minimize \( \sigma^2 \) through techniques like equalization, which is crucial for the longevity of China EV battery packs.
Challenges and Strategies for Lifecycle-Based Maintenance of EV Power Batteries
Despite the benefits, implementing lifecycle-based maintenance for EV power batteries faces several challenges, including technical bottlenecks, lagging standard system development, and insufficient industry chain collaboration. Efficient and safe disassembly and recycling technologies are key to closing the value chain loop. 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 technology for adaptive disassembly of different battery models is a critical future direction. Additionally, while traditional material recycling methods like hydrometallurgy and pyrometallurgy are relatively mature, they still need optimization in terms of environmental friendliness, cost control, and metal recovery rates. Exploring greener alternatives, such as physical recycling and directed cycling technologies, could address these issues. For China EV battery recycling, advancing these technologies is essential to meet sustainability goals.
Standard systems are the foundation for regulating industry development, but in the EV power battery maintenance field, standard system construction lags behind technological and market progress. Existing standards are often recommendatory or guiding, lacking mandatory and operable details in key areas like diagnostic processes, maintenance operations, quality assessment, and safety regulations, leading to uneven service quality and numerous safety hazards in the market. To address this, a layered standard system framework should be reconstructed. The top level consists of national mandatory basic standards, defining safety red lines, environmental baselines, and unified data interface requirements. The middle layer includes industry-recommended standards, providing detailed technical specifications and process guidelines for different battery chemistries (e.g., lithium iron phosphate, ternary lithium) and application scenarios (e.g., commercial vehicles, private cars). The bottom layer comprises enterprise and group standards, refining upper-level standards into specific operating procedures, internal quality control requirements, and innovative practices, ensuring effective implementation. This structured approach is vital for standardizing China EV battery maintenance.
Insufficient industry chain collaboration is another major hurdle. Achieving full lifecycle management of EV power batteries depends on seamless connectivity and information sharing across the industry chain. However, severe “information silos” exist, with poor communication among vehicle manufacturers, battery producers, maintenance companies, recycling firms, and second-life users. Key information such as design data, production details, battery management system data, maintenance records, and end-of-life status does not form an effective closed loop, severely hindering resource optimization and overall industry chain efficiency. In response, a national or industry-level traceability management platform for the entire lifecycle of EV power batteries should be established, drawing on concepts like extended producer responsibility and diversified recycling models. This platform would assign a unique “digital identity file” to each battery, recording design parameters, production batches, installation information, charging-discharging data, maintenance history, fault records, and final residual value assessment and recycling information from the outset. Technologies like blockchain could ensure data authenticity and immutability, with strict permission management mechanisms allowing legitimate participants to query and input data. This “digital battery passport” system would break down information barriers, enabling maintenance companies to access accurate battery histories for diagnostics, second-life users to conduct reliable residual value assessments, and regulators to track battery flows comprehensively, truly achieving collaborative lifecycle management. For the China EV battery industry, this represents a transformative step toward efficiency and sustainability.
The following table outlines the main challenges and corresponding strategies for lifecycle-based maintenance of EV power batteries:
| Challenge | Description | Strategy | Expected Outcome |
|---|---|---|---|
| Technical Bottlenecks | Manual disassembly; inefficient recycling | Develop automated disassembly; explore green recycling methods | Increase efficiency by 50%; reduce costs by 30% |
| Standard System Lag | Lack of enforceable standards | Implement layered standards: national, industry, enterprise | Improve service quality and safety compliance |
| Industry Chain Gaps | Information silos; poor collaboration | Establish traceability platforms with blockchain | Enhance data sharing; optimize resource use |
To model the impact of these strategies, consider the information sharing efficiency \( I_{\text{efficiency}} \) in the EV power battery lifecycle:
$$ I_{\text{efficiency}} = \frac{\sum_{j} w_j \cdot S_j}{\sum_{j} w_j} $$
where \( S_j \) is the sharing level for data type \( j \) (e.g., design, maintenance), and \( w_j \) is a weight factor. For China EV battery systems, maximizing \( I_{\text{efficiency}} \) through platforms can reduce lifecycle costs by up to 25%.
Conclusion
Building a lifecycle-based maintenance technology system for EV power batteries is of great significance and imperative. Although current challenges include technical limitations, standard development, and industry chain coordination, collaborative efforts from relevant departments, industries, and enterprises—focusing on key technology research and development, improving standard systems, and enhancing information sharing and collaboration across the industry chain—can overcome these obstacles. This will not only improve the efficiency and quality of EV power battery maintenance, reduce safety risks, but also achieve efficient resource utilization and green, sustainable industrial development, laying a solid foundation for the long-term growth of the new energy vehicle industry. The continuous advancement in China EV battery technologies will play a pivotal role in this transformation, driving global efforts toward a circular economy and carbon neutrality.