How Data and Analytics Drive the Future of Electric Vehicles

Automakers around the world are driving toward a future dominated by electric vehicles. GM’s entire Cadillac lineup will be electric by 2030, every car produced by Volvo will be electric by 2030, and Honda plans to phase out all gas cars by 2040 (Source). This is just a small sampling of companies making an aggressive shift towards EVs, and to meet those aggressive goals, increasing speed throughout the product development process is essential. Test teams will play a critical role in optimizing operational performance to increase the speed and reduce friction.

When looking for areas to decrease the time it takes for test processes, many don’t turn to data first. But is disconnected data slowing you down? It’s likely taking more time than you think.

Bridging Design and Test

Many organizations experience workflows from design and test that are historically disjointed. It’s often accepted as status quo, but when looking to increase performance you must look at every piece of the puzzle with a critical eye.

It’s essential for design and test to streamline communication. For most organizations, there is room to improve collaboration—especially when it comes to EV battery production. There is typically a disconnect between design and validation. Teams are given increasingly shorter timelines to conduct a wide range of tests. Such pressure makes it progressively easier to miss something when all the data back and forth from design and test isn’t utilized. This will lead to inevitable issues when the product hits consumers.

To meet critical deadlines and deliver an ultimately superior battery, the circle of data between design and test needs to fully close. Even if the groups are currently working “well,” the isolation within each group blocks maximum performance. Connecting hardware, processes, and people to software that distributes key data to both teams is crucial.

It’s Not Just about Deadlines

While it’s of course important to meet targets, more variables than timelines play into analytics. At a certain point, automakers must release their battery cells to meet the EV targets—whether the product is perfect or not. Teams must accomplish as much as possible within the limits they’ve been given. If you’re not using your data to the maximum level, validation will inevitably be less thorough.

Without keeping operational performance top of mind, you’re not going to have time to rigorously test every aspect. Most of the problems that show up in the field could have been prevented with the right test if there was enough time and resources. Data that’s visible to everyone is part of those requirements. With it, teams can:

  • Compare simulated data with real data from the validation lab
  • Connect production test and in-field data
  • Increase throughput in production from data
  • Reduce scrap to help with supply chain issues
  • Track OEM and supplier quality during the manufacturing process
  • Share more information throughout the entire product lifecycle
  • Maximize the quality of test within allotted timelines

Ending Data Silos

Oftentimes, design and validation teams stick to their own data from their own teams even though the product passes from one phase to the next. Analytics and lifecycle solutions work against this status quo to close silos and provide data that’s visible to everyone. They allow teams to continue what they do best with the added benefit of ensuring that everyone can make insightful decisions based off the data provided in each stage of the product journey.

It may seem like a small nuance, but collaboration through data across teams creates a large impact. For example, take an organization that has 10 test cells. They run those 10 cells 24x7 but must use two days of a design engineer’s time to gather, download, analyze, and report the data. If this same team employed a lifecycle solution, they could automate the data collection and make it available on demand for easier processing across teams. This gives time back to run the test cells and collect more data for informed conclusions about the battery.

In other scenarios, the equipment required might not be producing data files in a way that can be connected across the board. It may require a design engineer (versus a technician) to gather and analyze. When the data coming from various equipment isn’t streamlined, team members can spend valuable time formatting files in a way they can glean insight. Using analytics and lifecycle solution software addresses these common bottlenecks, giving time back to engineers to conduct additional tests and improve battery design.

Get Started

The best way to begin to address how your data can increase operational performance is to critically look at your current workflows. Are things flowing effortlessly, or have you simply become used to the status quo? Automating data collection and gaining visibility through the right solution will unlock a level of performance your team didn’t previously realize existed.

NI empowers test engineers to gain control of their test with systems that match their speed, adapt to their needs, and deliver the data insights to eliminate blind spots and meet safety and performance expectations.

Learn more about electric vehicle test at NI and the possibilities you can achieve with data.

Just for Fun!

The definition of testing:

"To tell somebody he is wrong is criticism. To do so officially is called testing.” –Gaurav Khurana