Optimizing the Battery Protection of the Audi Q8 e-tron

Optimizing the Battery Protection of the Audi Q8 e-tron

Finding the best cross-sections for rockers and battery protection profiles is challenging due to the wide range of requirements that must be met during impact scenarios and manufacturing. Traditionally, this design process is labour-intensive, requiring significant expertise and a bit of luck to find suitable drafts. iNDUVOS Profile was developed to tackle these demanding situations. To showcase the potential of our optimization software, we optimized the battery protection of an Audi Q8 e-tron. Our approach resulted in a mass reduction of over 9% while cutting development time from several months to just a few weeks, all while ensuring the manufacturability of the new designs.
In the rapidly evolving automotive industry, ensuring the safety and efficiency of electric vehicles (EVs) is paramount. One critical aspect of EV design is the battery protection profile, which safeguards the battery and occupants during lateral impact situations. Our software, iNDUVOS Profile, is designed to optimize this protection profile, balancing safety, weight reduction, and manufacturability. The following study on an Audi Q8 e-tron is based on a finite element model originally obtained from Caresoft Global and further processed in a technical case study by ALUMOBILITY. We acknowledge the contributions of both Caresoft Global and ALUMOBILITY for providing and enhancing the finite element model that enabled our study. To limit the required computational resources, we did not simulate the entire vehicle. Instead, we selected a smaller sub-model consisting of the battery and the surrounding structures. This assembly was impacted by a rigid pole with a velocity of 32.5 km/h and a mass of 1200 kg. Under these conditions, the battery protection behaves similarly to the full model. Below, you can see images of the deformed full vehicle, the deformed battery model, and the currently used battery protection profile.

During the optimization, the goal was to minimize the mass of the battery protection profile while ensuring the same crash performance as the current profile. The following constraints were defined:

  • The maximum impact force between the pole and the profile must remain below 610 kN
  • The intrusion of the profile must remain below 97 mm
  • A distance greater than 7 mm between the battery blocks and the neighboring sheet metal must be maintained (measured at 15 nodes)
  • The profile must be manufacturable by aluminum extrusion

The optimization started from the original profile but with all inner walls removed. From iteration 1 to 11, the algorithms analyzed the simulation data of the five best designs from the previous iteration and generated new competing designs. A selection of these designs is shown in the following overview, with the five best designs displayed for each iteration.

The optimization was able to surpass the reference design in iteration 5 after 425 simulations. The structure could not be improved further in iteration 11, so the best design from iteration 10 was used for a more detailed thickness optimization. In the end, the mass was reduced by 1.5 kg (8.6%) while meeting the specified force and displacement restrictions as well as the selected manufacturing constraints. The entire optimization process involved 1105 simulations to evaluate and improve the 367 generated designs.

The following curves show that the force level of 610 kN was not exceeded, and the displacement limit of 97 mm was maintained. The minimum distance between the battery blocks and the neighboring sheet metal also remained above 7 mm.

Below, the geometrical representation of the reference cross-section and the optimized cross-section is displayed. Notable in the optimized design is the thick horizontal wall and the distribution of the walls. On the impacting side (left), more walls were introduced compared to the reference design, which distributed the walls more evenly but also made them thicker closer to the impact location.

By optimizing this single part, 3 kg of aluminum can be saved per vehicle. Extrapolated to the more than 230,000 manufactured vehicles in the Audi Q8 e-tron series, 690 tons of aluminum could be saved, corresponding to roughly €2.4 million, assuming a price of €3.5 per kg of aluminum. The optimization took about one week, running on up to 1000 CPU cores. Each simulation was calculated on 32 CPUs with the finite element solver LS-DYNA, taking about 2 hours per simulation.

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