Using GT-AutoLion and GT-SUITE to Predict Battery Aging for Real World Applications

Over the years, lithium-ion technology has expanded into numerous applications, ranging from products as large as planes and ships to products as small as power tools and cell phones and everything in between.  Because of the inevitable degradation of lithium-ion cells, the lifespan of these products will likely be limited by the degradation of the Li-ion battery it uses.  In many instances, products may have special warranties for their battery system.  For instance, the Tesla Model S and Model X have a special battery warranty of 8 years, 150,000 miles; Dell Laptops offer 1 or 3 year battery warranties; and Makita offers 3 year warranties on most of the batteries in their power tools.

Determining how to warranty a battery is no easy task.  If the warranty is too short, there is a risk that less consumers will purchase your product; conversely, if the warranty is too long, there is a risk that there may be a significant amount of warranty claims down the road.  Mistakes in either direction are expensive.

To compound the difficulty of warrantying a battery pack, most of the companies selling products with Li-ion batteries in them do not manufacture the Li-ion cells. They simply buy cells from cell suppliers and package them in their system.  The engineers at these companies are focused on developing battery electric vehicles (BEVs), electric vertical take-off and landing (EVTOLs) aircrafts, power tools, or consumer electronics, not Li-ion cells.  Therefore, the people tasked with warrantying a battery are often not equipped with the proper information or knowledge about Li-ion technology to accurately predict battery lifetime. However, by utilizing a minimal amount of available data combined with physics-based simulation software, these engineers can predict battery lifetime, and therefore make more confident battery warranty decisions.

What Degradation Information Is Usually Available?

To help cell buyers determine how to warranty a battery, cell suppliers often quantify battery degradation in two ways: calendar degradation and cycle degradation.

Calendar degradation measures how a cell degrades while it is exposed to zero current for an extended period of time.  These are sometimes referred to as “shelf life” tests because the cells can simply be placed on a shelf, forgotten about, and periodically tested.

Cycle degradation measures how a cell degrades while being cycled between fully charged and fully discharged over and over at constant currents.

Generally, cell suppliers will inform their customers about their cell’s degradation by including calendar and cycle life data in detailed cell documentation, where calendar and cycle aging are given in plots using capacity retention (% of Beginning of Life Capacity) on the Y-axis and cycle or calendar days on the X-axis.  As seen in the example images below, both tests can be run at different temperatures.

Example Calendar and Cycle Aging Data

Neither of these tests are truly representative of what a Li-ion cell will experience in the real world, so an engineer responsible for determining the warranty of a battery in a system (such as a cell phone, hybrid vehicle, plane, etc.), may not find this data very helpful.

No BEV, EVTOL, or other product will be used in scenarios reflective of calendar or cycle aging tests.  BEVs will be used to commute back and forth to work, pick up groceries from supermarkets, and go on the occasional road trip.  EVTOLs will subject their batteries to demanding loads to transport people around large cities.  Power tools and consumer electronics may see extended periods of rest with occasional intense usage.  Additionally, due to weather patterns and varying usage and charging patterns between different consumers, the realistic demands that a battery may see in its lifetime can be very difficult to replicate in laboratory conditions.

What can be done with this information?

Simulation tools like GT-AutoLion and GT-SUITE provide a unique solution that enables engineers to use the provided cycle and calendar aging to gain meaningful insights into battery aging under more realistic scenarios.

As shown in many technical papers, physics-based models of Li-ion battery performance and aging in GT-AutoLion can be calibrated to match experimental data, such as capacity fade and resistance growth during calendar and cycle aging.

AutoLion aging models can be calibrated to Calendar & Cycle Aging Data.

Because the degradation models in GT-AutoLion are physics-based and postdictive, they can be calibrated to match this type of data and then used in other conditions to predict how Li-ion cells may age in any application.  In the case of a power tool supplier, these conditions can include typical usage patterns for various applications, including chain saws, drills, and even rotary tools.

Example: Battery Degradation of a BEV

In the case of a complex system, like a BEV, it’s important to capture interactions between the battery and other systems, such as thermal management systems, in order to accurately capture how the complete system affects battery life. For example, to predict the battery power demand of a BEV’s Li-ion battery during a drive cycle of a typical owner’s commute, a system-level model of the vehicle can be built using GT-DRIVE+. These drive cycles can then be repeatedly applied to a GT-AutoLion model, along with realistic rest times, in order to predict how a pack degrades in a real-world scenario.  On top of this, other variables can be studied to understand their effect on battery degradation, such as weather patterns and even charging patterns and strategies.

By combining the power of GT-DRIVE+ and GT-AutoLion, engineers have more meaningful aging predictions that can be quantified in terms of “Miles” or “Years of Operation” as opposed to “Cycles,” which then increases confidence when determining how to effectively warranty batteries.

Once Aging Models are calibrated, system behavior can be incorporated to predict more meaningful aging metrics.

In my next blog, I’ll discuss how GT-AutoLion predicts not only the capacity fade of a cell, but also predicts the performance of a system after a battery has begun degrading.

Written By: Joe Wimmer