In recent years, the rapid growth of the automotive market in China has led to a significant increase in vehicle ownership, with the total number of vehicles exceeding 350 million units. Among these, the adoption of electric vehicles has surged, with the number of new energy vehicles surpassing 25 million. This expansion highlights the importance of addressing issues related to vehicle safety and maintenance, particularly for intelligent pure electric vehicles. As consumers increasingly focus on smart features and sustainability, the integration of advanced technologies in electric vehicles inevitably raises production and maintenance costs. One critical area of concern is the economic impact of low-speed collisions, which account for a substantial portion of road accidents. In 2023, for instance, China recorded 1.75 million traffic incidents, with a notable proportion involving low-speed impacts that result in minor damages. These incidents, though often overlooked, can lead to significant financial losses due to the high costs of repairing or replacing components like radar systems and other intelligent features. Therefore, analyzing the maintenance economics of electric vehicles after low-speed collisions is essential for reducing ownership costs and enhancing vehicle design.
To address this, we conducted a comprehensive study based on the CCRT 2021 revised evaluation procedures, focusing on 10 mainstream intelligent pure electric vehicle models available in the China EV market. These electric vehicles were subjected to standardized low-speed frontal and rear collision tests to evaluate their post-crash repair costs and economic performance. The primary objective was to identify components with high deformation and defect values, assess the economic feasibility of repairs, and propose design improvements using advanced optimization algorithms. By leveraging real-world test data and simulation models, we aim to provide insights that balance crashworthiness and lightweight design, ultimately contributing to the development of more cost-effective electric vehicles for consumers in China and beyond.
The test methodology adhered to the CCRT 2021 guidelines, which include evaluations of frontal and rear low-speed structural collisions. For frontal impacts, the vehicle was positioned to overlap 40% ± 25 mm with a barrier at a 10° angle, and the collision speed was set to 15 km/h. Similarly, rear impacts involved a 40% ± 25 mm overlap with the barrier at the same speed and angle. High-speed cameras recorded the tests, and pre- and post-collision measurements of vehicle gaps and chassis were taken to assess damage. The repair costs were calculated as the sum of parts, labor, and auxiliary material expenses, and the maintenance economy score was derived from the ratio of total repair cost to the manufacturer’s suggested retail price (MSRP). This ratio, denoted as R, is defined by the formula: $$ R = \frac{Y}{F} \times 100\% $$ where Y is the total repair cost and F is the vehicle’s MSRP. The scoring system, as outlined in the CCRT protocol, assigns points based on R ranges, with lower ratios yielding higher scores, reflecting better maintenance economy for electric vehicles.
The 10 selected electric vehicle models covered a price range from approximately 139,800 to 570,000 yuan, representing various segments of the China EV market. Key parameters, including vehicle type, battery technology, announced range, and air conditioning system, are summarized in Table 1. This diversity allows for a broad analysis of how different design and cost factors influence maintenance economics in intelligent pure electric vehicles.
| Model | Price Range (10k yuan) | Vehicle Type | Battery Type | Announced Range (km) | Air Conditioning System |
|---|---|---|---|---|---|
| V1 | 20-30 | Sedan | LFP | 600 | Heat Pump |
| V2 | 20-30 | Sedan | LFP | 500 | Heat Pump |
| V3 | >30 | SUV | NMC | 550 | Heat Pump |
| V4 | >30 | SUV | NMC | 400 | Heat Pump |
| V5 | >30 | SUV | NMC | 500 | Heat Pump |
| V6 | 20-30 | Sedan | NMC | 650 | Heat Pump |
| V7 | >30 | SUV | NMC | 500 | PTC |
| V8 | 10-15 | SUV | NMC | 500 | PTC |
| V9 | 15-20 | SUV | LFP | 500 | PTC |
| V10 | 15-20 | SUV | NMC | 600 | PTC |
In the data analysis phase, we observed significant variations in maintenance economy scores among the 10 electric vehicle models. The overall scores ranged from a minimum of 27.5 to a maximum of 77.5, with an average of 59 points. This indicates that while some China EV models excel in minimizing post-collision repair costs, others require substantial improvements. The distribution of scores was relatively even, with five models falling below the average, suggesting that factors beyond mere price, such as design and component integration, play a crucial role in the maintenance economics of electric vehicles.
For frontal low-speed collisions, the repair costs varied widely, from 19,303 to 80,369 yuan, with corresponding scores between 12.5 and 42.5 points. The average repair cost was 38,430 yuan. Key components contributing to these costs included the front bumper, crash beam, hood, fender, headlights, condenser, radiator, and mirror assemblies. In most cases, parts replacement accounted for over 80% of the total expense, underscoring the importance of the zero-integer ratio (the ratio of the total cost of all parts to the vehicle’s MSRP) in determining maintenance economy. For instance, models with lower zero-integer ratios demonstrated better scores, as they incurred lower relative repair costs. This relationship can be expressed mathematically as: $$ \text{Zero-integer Ratio} = \frac{\sum \text{Parts Cost}}{F} $$ where a lower ratio correlates with improved economic performance for electric vehicles. Table 2 provides a detailed breakdown of the frontal collision results, illustrating how R values translate into scores based on the CCRT criteria.
| Repair Cost Ratio R (%) | Score |
|---|---|
| R < 5.0 | 50 |
| 5.0 ≤ R < 6.0 | 47.5 |
| 6.0 ≤ R < 7.0 | 45 |
| 7.0 ≤ R < 8.0 | 42.5 |
| 8.0 ≤ R < 9.0 | 40 |
| 9.0 ≤ R < 10.0 | 37.5 |
| 10.0 ≤ R < 11.0 | 35 |
| 11.0 ≤ R < 12.0 | 32.5 |
| 12.0 ≤ R < 13.0 | 30 |
| 13.0 ≤ R < 14.0 | 27.5 |
| 14.0 ≤ R < 15.0 | 25 |
| 15.0 ≤ R < 16.0 | 22.5 |
| 16.0 ≤ R < 17.0 | 20 |
| 17.0 ≤ R < 18.0 | 17.5 |
| 18.0 ≤ R < 19.0 | 15 |
| 19.0 ≤ R < 21.0 | 12.5 |
| 21.0 ≤ R < 23.0 | 10 |
| 23.0 ≤ R < 25.0 | 7.5 |
| 25.0 ≤ R < 27.0 | 5 |
| 27.0 ≤ R < 30.0 | 2.5 |
| 30.0 ≤ R | 0 |
Similarly, for rear low-speed collisions, the repair costs ranged from 4,475 to 22,323 yuan, with scores between 10 and 45 points. The average repair cost was 12,669 yuan. Components such as the rear crash beam, bumper, door panels, tail lights, tires, and wheel arches were frequently damaged. Again, parts replacement dominated the expenses, accounting for over 85% of the total. The lower absolute costs compared to frontal collisions are attributed to the reduced number and complexity of affected parts. However, the relative cost ratio R remains a critical metric, as it reflects the economic burden on owners of electric vehicles. Table 3 summarizes the rear collision scoring based on R values, emphasizing that even minor damages can lead to significant financial impacts if not addressed in the design phase.
| Repair Cost Ratio R (%) | Score |
|---|---|
| R < 1.0 | 50 |
| 1.0 ≤ R < 1.5 | 47.5 |
| 1.5 ≤ R < 2.0 | 45 |
| 2.0 ≤ R < 2.5 | 42.5 |
| 2.5 ≤ R < 3.0 | 40 |
| 3.0 ≤ R < 3.5 | 37.5 |
| 3.5 ≤ R < 4.0 | 35 |
| 4.0 ≤ R < 4.5 | 32.5 |
| 4.5 ≤ R < 5.0 | 30 |
| 5.0 ≤ R < 5.5 | 27.5 |
| 5.5 ≤ R < 6.0 | 25 |
| 6.0 ≤ R < 6.5 | 22.5 |
| 6.5 ≤ R < 7.0 | 20 |
| 7.0 ≤ R < 7.5 | 17.5 |
| 7.5 ≤ R < 8.0 | 15 |
| 8.0 ≤ R < 8.5 | 12.5 |
| 8.5 ≤ R < 9.0 | 10 |
| 9.0 ≤ R < 9.5 | 7.5 |
| 9.5 ≤ R < 10.0 | 5 |
| 10.0 ≤ R < 10.5 | 2.5 |
| 10.5 ≤ R | 0 |
To delve deeper into the analysis, we applied a multi-objective optimization approach using the NSGA-II algorithm, implemented via Isight software. This method allows for the simultaneous consideration of crashworthiness and lightweight design, which are critical for enhancing the maintenance economy of electric vehicles. The optimization model was built around design variables such as the dimensions and materials of key components like crash beams and energy absorption boxes. The objective functions aimed to minimize deformation and repair costs while maintaining vehicle mass within acceptable limits. Mathematically, this can be represented as: $$ \min \left[ f_1(\mathbf{x}), f_2(\mathbf{x}) \right] $$ where $$ f_1(\mathbf{x}) = \text{Total Deformation} $$ and $$ f_2(\mathbf{x}) = \text{Vehicle Mass} $$ subject to constraints like $$ g(\mathbf{x}) \leq 0 $$ for structural integrity. Here, $\mathbf{x}$ denotes the vector of design variables. Through iterative simulations, we generated a Pareto front of optimal solutions, balancing the trade-offs between crash performance and weight reduction.
For example, in one optimization case, the compression distance of the energy absorption box was reduced from 123.4 mm to 116.5 mm, effectively decreasing the impact on critical components and minimizing repair needs. This improvement was achieved without significantly increasing the vehicle’s mass, thus supporting the lightweight goals for electric vehicles. The optimization also enhanced sectional force and plastic strain capacity, as described by the equations: $$ F_s = \frac{\Delta E}{\Delta d} $$ where $F_s$ is the sectional force, $\Delta E$ is the energy change, and $\Delta d$ is the deformation distance. Similarly, the plastic strain $\epsilon_p$ was optimized to absorb more energy while preventing failure: $$ \epsilon_p = \int \dot{\epsilon}_p \, dt $$ where $\dot{\epsilon}_p$ is the plastic strain rate. These refinements demonstrate how intelligent design can lower the zero-integer ratio and improve the overall maintenance economy for China EV models.
In conclusion, our analysis of 10 intelligent pure electric vehicles reveals that maintenance economics after low-speed collisions are influenced by three primary factors: vehicle design, zero-integer ratio, and MSRP. Designs that protect key components, such as headlights and radar systems, from minor damages can significantly reduce repair costs. Moreover, models with lower zero-integer ratios tend to perform better economically, as seen in popular China EV models with ratios around 10-13%. The application of multi-objective optimization using NSGA-II provides a viable pathway for automakers to enhance crashworthiness and lightweight properties simultaneously, leading to more affordable ownership experiences. However, further validation is needed to extend these findings to high-speed collision scenarios. As the electric vehicle market continues to evolve, prioritizing maintenance economy in design will be crucial for sustaining consumer trust and promoting the adoption of sustainable transportation solutions globally.
This study underscores the importance of continuous innovation in the electric vehicle sector, particularly in the China EV landscape, where competition and consumer expectations are high. By integrating real-world test data with advanced optimization techniques, we can drive progress toward safer, more economical, and environmentally friendly electric vehicles. Future work should explore the integration of more complex variables, such as battery protection and autonomous system durability, to further refine maintenance strategies for the next generation of intelligent pure electric vehicles.