In light of the recent EV fire incidents in different parts of the country, the Government of India has brought in a fresh set of recommended safety norms for both existing and new EVs. These norms will help prevent battery explosions, which are a biggest deterrent to consumer confidence.
The new set of measures: Amendment 2 to AIS 156 for battery safety will come into effect from October 1, 2022 and will go a long way in ensuring better safety of electric vehicles on the road.
What’s new in Amendment 2 to AIS 156?
One critical part in the new amendment is that EV manufacturers will now have to perform a stringent test where they have to initiate thermal runaway in a cell of a pack, and demonstrate that it does not lead to an explosion. In a previous post, we explained how intelligent design can enable this.
The new standard poses a challenging question to the EV manufacturer. When I perform the test on my current pack how will I ensure it does not explode? And why is this important?
- If the pack explodes, it means going back to the drawing board and redesigning the pack
- If the pack needs to be redesigned, you should ensure that the explosion is avoided in the test, yet the entire pack is not “over-designed”
Additionally, the new standards have the potential to increase battery pack design expenditure significantly. To avoid this, design best practices should be followed early in the design process.
How oorja can help
Oorja offers an application where Thermal Runaway (TR) can be initiated at a cell of your choice, and TR propagation at the pack level can be analyzed. You can then change the cell spacing, casing thickness or other relevant design parameters and see how TR propagates, thus ensuring design iterations are reduced without compromising on safety or quality.
Fig 1. shows onset of TR which propagates to neighbouring cells, while Fig 2, where the inter cell spacing has been increased by a factor of 10 shows that the TR event does not spread as rapidly and therefore ensures successful completion of the AIS-156 TR test.
Figure 1. TR Propagation Prediction for a pack which will fail the AIS 156 TR test
Such predictive modeling can help you speed up optimization your pack designs.
Figure 2. TR Propagation Prediction for a modified pack which will pass the AIS 156 TR test
Learn how oorja can help you design safer packs by providing accurate and fast predictive models with minimal data needed and no need for laborious parameter identification necessary to use traditional modeling tools. Contact us now.