As the automotive industry shifts towards electrification, the EV battery pack has become the sole power source for new energy vehicles. However, its placement at the vehicle underside exposes it to high-speed impacts from road debris during operation, posing significant safety risks such as short circuits or fires due to shell penetration. This study focuses on enhancing the safety of the EV battery pack under forward bottom-scraping conditions, a critical but often overlooked scenario. Through finite element simulation and real-world testing, I analyze the structural weaknesses and propose an effective bottom-protection design. The goal is to mitigate intrusion and ensure occupant safety, addressing a gap in current standards like GB 38031—2020, which lacks specific requirements for bottom-scraping incidents.
The forward bottom-scraping condition simulates a vehicle colliding with a road protrusion while moving forward. Based on industry guidelines such as T/CASE 244—2021, the parameters are set as follows: the impactor is a rigid hemispherical fixture with a diameter of 150 mm, made of 45 steel; the scraping direction aligns with the vehicle’s forward motion (X-direction); the overlap is 30 mm vertically (Z-direction) from the lowest point of the EV battery pack shell to the highest point of the fixture; the impact speed is 30 km/h; and the initial contact point is randomly selected at a weak spot on the battery pack. These conditions replicate real-world scenarios where the EV battery pack is vulnerable to debris strikes.
To analyze the EV battery pack’s response, I developed a detailed finite element model using HyperMesh as the pre-processor and LS-DYNA as the solver for nonlinear dynamic simulation. The EV battery pack is integrated into the full vehicle model, mounted on longitudinal beams via bolts, to ensure realistic boundary conditions. The modeling process involves defining element types, material properties, and connection methods, with a focus on accuracy and computational efficiency.
The EV battery pack components, including the upper shell, lower shell, brackets, and supports, are modeled with shell elements using a mesh size of 5 mm, while the internal battery cells are represented with hexahedral elements. This approach minimizes triangular elements to less than 5% of the total, ensuring precision. The material properties are assigned based on real-world data, with the battery cells modeled as compressible foam (MAT63) to capture deformation under load, unlike previous studies that used mass points. The table below summarizes the material attributes for key components of the EV battery pack.
| Component | Material | Simulation Material Type | Element Type | Thickness (mm) | Elastic Modulus (MPa) | Poisson’s Ratio | Density (kg/mm³) |
|---|---|---|---|---|---|---|---|
| Upper Shell | HC340LA | Piecewise Linear (Type 24) | Shell | 1.0 | 207,000 | 0.3 | 7.85e-6 |
| Lower Shell | HC340LA | Piecewise Linear (Type 24) | Shell | 1.5 | 207,000 | 0.3 | 7.85e-6 |
| Battery Cells | N/A | Compressible Foam (Type 63) | Hexahedral | N/A | 500 | 0.01 | 2.5e-6 |
| Installation Brackets | PP | Piecewise Linear (Type 24) | Shell | 3.0 | 1,620 | 0.38 | 1.05e-6 |
Connections within the EV battery pack, such as bolts and welds, are simulated using rigid elements and spot welds, respectively, given that the scraping forces are below their failure thresholds. The impactor is positioned at the transverse center of the EV battery pack, with a speed of 30 km/h and friction coefficients set to 0.2 for both static and dynamic conditions. The simulation captures geometric, material, and contact nonlinearities, essential for accurate crash analysis.
After running the simulation, I verified the model’s reliability by examining energy conservation and mass scaling. In LS-DYNA, hourglass energy and interface slip energy must be controlled to avoid inaccuracies. The energy change curves show that the total energy stabilizes at 69,795 J, with hourglass energy at 10.5 J (0.015% of total) and interface slip energy at 1,054.14 J (1.51% of total). Both are below the 5% threshold, indicating minimal numerical errors. The energy balance equation can be expressed as:
$$E_{\text{total}} = E_{\text{kinetic}} + E_{\text{internal}} + E_{\text{hourglass}} + E_{\text{slip}}$$
where \(E_{\text{total}}\) is constant, confirming model stability. For mass scaling, which adjusts time steps for computational efficiency, the added mass is monitored. The time step for shell elements is calculated as:
$$\Delta t = \frac{LS}{c}$$
with the wave speed \(c\) given by:
$$c = \sqrt{\frac{E}{\rho(1 – \nu^2)}}$$
and the characteristic length \(LS\) for quadrilateral elements as:
$$LS = \frac{A}{\max(L_1, L_2, L_3, L_4)}$$
where \(A\) is the element area and \(L_i\) are side lengths. The mass increase peaks at 89.847 kg, or 4.51% of the total vehicle mass of 1,990 kg, within the acceptable limit of 5%. This ensures the simulation results are physically meaningful while maintaining computational speed.
The deformation analysis reveals critical insights into the EV battery pack’s safety. Under the forward bottom-scraping impact, the Z-direction intrusion of the battery pack shell reaches 46.51 mm, as shown in displacement cloud plots. This exceeds the 30 mm clearance between the shell and internal electrical components (e.g., battery management systems), posing a high risk of short circuits or thermal runaway. The intrusion process occurs in two phases: initial contact at 45 ms causes significant deformation, followed by a secondary impact at 65 ms as the vehicle rebounds, compounding the damage. This highlights the vulnerability of the EV battery pack to bottom impacts and underscores the need for protective measures.
To address this, I designed a bottom-protection structure inspired by existing solutions in the automotive industry. The structure consists of a protective bar made of QSTE500 steel with a thickness of 2.0 mm, installed along the longitudinal beams below the EV battery pack. It acts as a first line of defense, lowering the lowest point of the vehicle to deflect or absorb impacts from road debris. The design prioritizes simplicity, manufacturability, and ease of installation, facilitating maintenance and replacement. The protective bar is integrated into the finite element model, and the simulation is repeated under identical scraping conditions.
The improved simulation shows enhanced safety performance. The energy analysis indicates a total energy of 70,268 J, with hourglass energy at 11.33 J (0.016%) and interface slip energy at 1,163.21 J (1.66%), both within acceptable limits. Mass scaling results in an added mass of 83.388 kg, or 4.17% of the total mass, consistent with standards. Most importantly, the Z-direction intrusion of the EV battery pack shell is reduced to 29.75 mm, a 36% decrease from the original design. This is below the 30 mm safety threshold, significantly lowering the risk of internal damage. The impact width on the shell is approximately 133.172 mm, aligning with expectations from the protective bar’s geometry.
The effectiveness of the bottom-protection structure is validated through real-world testing conducted at an automotive proving ground. The test follows T/CASE 244—2021 specifications, with an impact speed of 29.881 km/h and the same hemispherical fixture. The protective bar successfully intercepts the impact, as evidenced by the damage pattern on its surface. Post-impact inspection shows no leakage or fire in the EV battery pack after a 2-hour observation period, confirming the structure’s ability to safeguard the internal modules. The test results correlate well with simulation data, such as the impact width of about 13 cm versus 133.172 mm in the model, reinforcing the accuracy of the finite element approach.
In conclusion, this study demonstrates the importance of addressing forward bottom-scraping scenarios for EV battery pack safety. Through detailed simulation and experimental validation, I show that the proposed bottom-protection structure effectively reduces shell intrusion by 36%, mitigating the risk of electrical failures and enhancing occupant safety. The integration of compressible foam material for battery cells and rigorous model verification adds robustness to the analysis. Future work could explore optimization of the protective bar’s geometry or material for weight reduction, but the current design offers a practical and reliable solution. As EVs become more prevalent, such improvements are crucial for ensuring the durability and safety of the EV battery pack in diverse driving conditions.