In the last episode of Source De[Code], listeners got a glimpse into the ways in which digital twins are assisting auto manufacturers in developing safe, reliable autonomous vehicles (AV). This week, Ben shifts gears and, through his conversation with guest Albert Groebmeyer, shows listeners how Big Data is helping engineers accelerate innovation in electric vehicle (EV) battery design. Electric vehicles are poised to overtake their gas-fueled cousins in popularity and dependability within the decade. As automakers and consumers prepare for the EV revolution, it is up to engineers to design vehicles and components that are both cost-effective and dependable in the long term.
I come from a family of automotive engineers. Growing up, I would watch my grandfather build gadgets in the garage including, notably, a motorized shoe shiner. He built a motor and attacked thick metal dowels with thick bristled brush wheels that would spin to them. While he was fiddling with it– testing it, taking it apart, fiddling with some component or another, and putting it back together again– he would explain to my brother and me what he was doing and why. Each component in any design serves a critical function. If any piece is not working properly, the design can fail and potentially lead to injury.
My grandfather’s commitment to engineering went even further than just his profession and building gadgets in the garage to avoid claustrophobic family gatherings. During the long summers spent at the family cottage, my grandfather and I would clamber into his old truck and he would point out the engineering marvels all around us– bridges, tunnels, wind turbines, solar panels, and once, the shoulder of a country road to call my attention to the significance of the road’s slope for vehicle safety. I’d be lying if I said I listened in rapt awe when he waxed poetic about engineering feats or that I absorbed any of his knowledge during those adventures. That said, listening to the last two episodes of Source De[Code] resurfaced memories of our conversations and his clear commitment to engineering a safer future for his grandchildren.
BIG DATA: THE MOST VALUABLE INFINITE RESOURCE
If I were to paint my grandfather’s portrait, it would be of him at a table scattered with sheets of graph paper filled with notes, numbers, and figures. Everywhere he went, he had pads of graph paper and mechanical pencils he would use to note his thoughts on design optimization for whatever captured his whimsy in the moment. Twenty some-odd years later, I realize that these notes were data points he had observed in his environment that may or may not have been put to work in the garage he had transformed into a hobbyist lab. I wonder what he would think of the vast stores of data we can capture today through the Internet of Things (IoT), our cell phones, wearable tech, credit cards, and other ubiquitous creature comforts embedded with technology.
Data is captured everywhere all the time. As storage for that data becomes easier to afford and maintain, data sets are captured and stored in a digital purgatory waiting to be released for some higher purpose. Depending on how it is sliced, diced, and analyzed, the same large data set can be used to gain deeper insights into human behavior, how well an item functions, or to identify opportunities to optimize something on both micro and macro levels.
“Data is the currency of the modern age,” explains Source De[Code] host, Ben Coffin. Unlike other currencies whose value is determined by its scarcity, data is an ever-growing, infinite resource whose value is only uncovered when it is analyzed. I have never thought about data in this way, but what I consider that my banking, personal identification, and memories in the form of pictures and video are stored as data, it becomes crystal clear that data is indeed precious beyond measure.
Data only holds its value, however, if it is consistently accurate, trustworthy, and secure. “You have to be able to trust the data from suppliers, data tracking, and measurement instruments,” says episode guest Albert Groebmeyer. For the automakers and engineers developing electric vehicles (EV) and batteries, the data they mine is used to develop products that must withstand the test of time and carry their passengers safely to whatever their destination is. Because engineers rely on partners and vendors for components and materials, the data sheets that accompany those items lay the foundation for whatever research and development comes next. The accuracy of the parameters outlined in those data sheets is important for any manufacturing derived from the environment and can impact the success of the end result.
From there, important environmental data is created at the machine level– in the mixing process, electrode manufacturing, and assembly. Once the device prototype is assembled, you capture electrochemical data. This is “new data from the cell based on current, voltage– things you can get out of the cell like capacity, fast charging capabilities, resistance– all of which can be used for development,” Groebmeyer explains.
These data springs come together to create a lake that engineers dive into to extract insights and analytics which assist them in identifying correlations within established parameter sets that accelerate the R&D process. Excellent data analytics software and precision test and measurement instruments enable engineers to wade through the data lake and find the parameters that will produce the best battery cell possible. Leveraging data in this way accelerates time to market and keeps costs down.
UNLOCKING THE TRUST QUOTIENT WITH DATA
From the design process to the consumer’s decision to purchase an EV model, trust in the data behind the product is implicit. Inaccurate measurements or making decisions based on the wrong parameters will lead you down the wrong path. To mitigate this, data tracking is critical. During the development process, everything is meticulously documented and tracked. For EV batteries, their speed, pressure, humidity, and all other parameters are accounted for in detail. This ensures that when a defect is identified, the development teams can retrace their steps to find the root cause and correct the issue whenever it arises, even years after the vehicle leaves the lot.
Despite the sticker prices for EVs coming down, they are–like all car purchases– a long-term and large investment. Equipment failures and unanticipated component issues will inevitably arise from time to time. The data tracking done during the design phase allows manufacturers to identify which vehicles are affected by identified issues so they can contact affected owners and ensure the flawed components are replaced and the owners can continue to enjoy their investment for years to come. Trust in data is trust in the product itself– it is in essence, as Ben says in his summary of the importance of data accuracy, “our ability to trust that the car will charge safely and last for a decade or more.”
My grandfather’s exacting attention to detail and his ability to describe what works and doesn’t during our long summer drives have shaped who I am today. Because of him, I have an immense amount of respect and deep-seated trust in engineers, their processes, and the products they develop. In my work at Keysight and in listening to the engineers interviewed on Source De[Code], I understand more every day that those qualities I so admired in my grandfather are not unique to him. Rather, they are hallmark traits shared by all engineers. High-caliber data serves their scrupulous eye for detail and gives engineers the ability to make and act on more finely tuned observations now and down the line.
ABOUT THE GUEST: ALBERT GROEBMEYER
Albert Groebmeyer has worked in the battery industry since 2013. After joining Keysight in 2019, he has managed several projects in the field of cell production and testing. Currently, Albert serves as the portfolio planner for cell test solutions at Keysight.
What was the ‘aha’ moment that started you down your path or influenced your journey to where you are now?
During my time at university, two main points about battery research sparked my passion. First, battery research, from a science perspective, is super interesting because it requires multi-directional thinking and interdisciplinary collaboration. Second, batteries allow for green energy storage in so many different applications ranging from small portable devices up to EVs and huge stationary energy storage systems which all come with unique requirements.
If you hadn’t chosen your current profession, what would you have pursued instead? Why?
If I had not focused on the development of battery technology, I am quite sure I would still have found myself in and around chemical engineering. For me, doing something completely different is really hard to imagine.
Where can we find you when you’re not innovating the future of technology?
You can find me spending time with my kids on playgrounds, cycling the forest paths with my family, or playing a game of pickup basketball with old friends.
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