In recent years, the rapid expansion of the new energy vehicle industry has led to a massive influx of China EV batteries reaching their end-of-life, posing significant challenges in resource management and environmental sustainability. As a researcher in this field, I have dedicated my efforts to developing comprehensive methods for predicting the retirement of EV power batteries and evaluating their recycling pathways. This study addresses the uncertainties in retirement timing and the diversity in recycling options by integrating advanced modeling techniques, multi-criteria assessment frameworks, and optimization algorithms. The primary goal is to enhance the efficiency and decision-making precision in handling retired China EV batteries, thereby supporting the circular economy and reducing ecological impacts.
The retirement of EV power batteries is not determined by a single factor but results from the interplay of multiple variables, such as State of Health (SoH), cycle count, operating temperature, discharge rate, and cumulative mileage. In my analysis, I identified these key parameters through principal component analysis (PCA) to reduce dimensionality and highlight the most influential factors. For instance, SoH, often set at 80% of the initial capacity as a retirement threshold, serves as a core indicator of battery degradation. Cycle count and temperature significantly affect capacity fade and thermal stability, while data from Battery Management Systems (BMS) enable quantitative modeling. Based on a dataset spanning from 2018 to 2022, collected from operational vehicles and recycling enterprises, I constructed a multivariate regression model to quantify the impact of these variables. The correlation analysis revealed that cycle count and cumulative mileage exhibit strong negative correlations with SoH, emphasizing their dominance in retirement prediction. To illustrate, the statistical characteristics of these variables are summarized in Table 1.
| Variable Name | Mean | Standard Deviation | Correlation Coefficient (with SoH) |
|---|---|---|---|
| Cycle Count, cycles | 1125 | 312 | -0.83 |
| Average Temperature, °C | 31.7 | 4.5 | -0.69 |
| Cumulative Mileage, km | 86200 | 19800 | -0.75 |
| Discharge Rate (C-rate) | 1.3 | 0.4 | -0.56 |
| Initial Capacity, A·h | 92.6 | 3.1 | 0.12 |
For mid- to long-term forecasting of retirement volumes, I employed time series methods, specifically the Autoregressive Integrated Moving Average (ARIMA) model. Using historical data on China EV battery sales from 2015 to 2022 and accounting for a typical lifespan of 5 to 8 years, I developed an ARIMA(1,1,1) model to project annual retirement quantities. The model equation is expressed as:
$$ \Delta Y_t = \phi_1 \Delta Y_{t-1} + \theta_1 \varepsilon_{t-1} + \varepsilon_t $$
where \( Y_t \) represents the retirement volume in year \( t \), \( \Delta \) denotes the difference operator, \( \phi_1 \) and \( \theta_1 \) are model parameters estimated via least squares, and \( \varepsilon_t \) is the error term. After ensuring stationarity through the Augmented Dickey-Fuller (ADF) test, the model indicated a rising trend, with projections suggesting over 950,000 retired China EV batteries annually by 2026. This upward trajectory underscores the urgency for robust recycling systems.
To improve prediction accuracy, I incorporated a multi-source data fusion approach using Long Short-Term Memory (LSTM) neural networks. This model integrates heterogeneous data, including BMS records (e.g., cycle count, residual capacity, voltage fluctuations), GPS data (e.g., average speed, terrain variations), and environmental factors (e.g., external temperature, humidity). After standardizing the features using Z-score normalization, I trained the LSTM network to capture non-linear relationships and temporal dependencies. The loss function, Root Mean Square Error (RMSE), is defined as:
$$ \text{RMSE} = \sqrt{\frac{1}{n} \sum_{i=1}^{n} (y_i – \hat{y}_i)^2} $$
where \( y_i \) is the actual retirement label and \( \hat{y}_i \) is the predicted value. This fusion model demonstrated superior performance in dynamic scenarios, adapting to individual vehicle usage patterns and environmental conditions, which is crucial for large-scale fleet management of EV power batteries.
In evaluating different prediction models, I compared ARIMA, Support Vector Regression (SVR), and LSTM based on metrics such as RMSE, Mean Absolute Percentage Error (MAPE), and the coefficient of determination (R²). Using a consistent historical dataset from 2015 to 2022, the results, shown in Table 2, highlight LSTM’s dominance in handling complex, non-linear retirement trends of China EV batteries.
| Model Type | RMSE | MAPE (%) | R² |
|---|---|---|---|
| ARIMA | 7.61 | 14.3 | 0.861 |
| SVR | 5.47 | 10.1 | 0.908 |
| LSTM | 3.82 | 6.7 | 0.972 |
Transitioning to recycling assessment, I developed a multi-level evaluation framework comprising target, criterion, and indicator layers. The criteria encompass economic benefits, environmental impact, safety assurance, and resource recovery rates, with specific indicators like unit recycling cost, carbon emission intensity, thermal runaway risk, and recovery rates of nickel and cobalt. This structure, grounded in operational data and industry standards, facilitates a holistic analysis of recycling pathways for retired EV power batteries. For example, economic data were derived from financial reports of leading recycling companies in 2022, while environmental metrics were quantified using Life Cycle Assessment (LCA) methodologies.
In assessing the economic and environmental benefits, I focused on two primary recycling paths: cascaded use and regenerative recycling. Cascaded use involves repurposing retired China EV batteries for secondary applications like energy storage, whereas regenerative recycling extracts valuable materials such as nickel and cobalt. Based on data from industry sources and LCA databases, I computed key metrics, including unit recycling cost, profit margin, and carbon reduction benefits per kilowatt-hour (kW·h). The comparative results, presented in Table 3, reveal that cascaded use offers higher carbon reduction but at a greater cost, while regenerative recycling is more economically viable with superior material recovery.
| Path Type | Recycling Cost (USD/kW·h) | Profit Margin (%) | Carbon Reduction (kg CO₂e/kW·h) |
|---|---|---|---|
| Cascaded Use | 22.5 | 12.4 | 37.6 |
| Regenerative Recycling | 16.7 | 21.7 | 18.2 |
To further evaluate the lifecycle value, I applied LCA to both pathways, considering stages from transportation and preprocessing to recycling and remanufacturing. The functional unit was defined as the treatment of 1 kW·h of retired EV power batteries. Results, summarized in Table 4, indicate that cascaded use minimizes carbon emissions and conserves resources, whereas regenerative recycling excels in material value recovery, emphasizing the complementary nature of these approaches for China EV battery management.
| Path Type | Carbon Emissions (kg CO₂e/kW·h) | Resource Saving Value (USD/kW·h) | Material Recovery Value (USD/kW·h) |
|---|---|---|---|
| Cascaded Use | 48.7 | 1.8 | 1.2 |
| Regenerative Recycling | 61.3 | 1.0 | 4.4 |
For integrated decision-making, I combined the Analytic Hierarchy Process (AHP) with entropy weighting to assign quantitative weights to multiple criteria, such as carbon emissions and material recovery value. In this model, carbon emissions received a weight of 0.31, reflecting the industry’s emphasis on sustainability, while material recovery value had a weight of 0.27. This approach enables a balanced selection of recycling strategies for EV power batteries, tailored to specific technical and policy contexts.
In terms of recycling path optimization, I analyzed the entire process, including collection, state assessment, path selection, technical sorting, and final treatment. Different battery types, such as lithium iron phosphate (LFP) and ternary lithium, require tailored approaches. For instance, cascaded use prioritizes residual capacity and SoH screening for low-rate applications, while regenerative recycling focuses on efficient dismantling and purification processes. The physical structure of battery packs, like integrated designs, necessitates automated disassembly tools to ensure safety and efficiency in handling China EV batteries.
To optimize the recycling network, I simulated a regional system in the Yangtze River Delta, comprising 20 collection points, 4 transfer centers, and 2 recycling plants. Input data included annual retirement volumes, node capacities, and transportation distances, with a carbon emission coefficient of 0.072 kg CO₂e per ton-kilometer. Using optimization algorithms, I achieved an 18.4% reduction in total travel distance, a 13.2% decrease in transport emissions, and a 9.5% shortening of average调度 time. This enhanced node load distribution and alleviated bottlenecks, demonstrating the model’s practicality for regional EV power battery recycling systems.
For system scheduling, I developed a discrete-event simulation platform using Python and SimPy, incorporating a Multi-Objective Particle Swarm Optimization (MOPSO) model to optimize response time, vehicle idle rate, and plant utilization. In a scenario based on 2022 operational data from Eastern China, involving 100 collection vehicles and 8 processing stations, the simulation results showed a 16.8% reduction in idle rate, a 12.3% decrease in average task duration, and a processing capacity utilization of 92.4%. By clustering tasks and重构 paths, this协同 mechanism effectively addressed peak-period congestion and resource wastage, offering a robust solution for intelligent调度 in China EV battery recycling networks.
In conclusion, my research establishes a comprehensive framework for predicting the retirement of EV power batteries and evaluating their recycling, integrating variable identification, multi-source data fusion, and multi-criteria assessment. The models and methods developed here enhance system efficiency and decision accuracy, providing a solid foundation for managing retired China EV batteries. Future work should focus on incorporating real-time data and policy constraints to improve dynamic responsiveness and algorithm robustness, ultimately fostering greater synergy across the recycling产业链.