Collins said experience with more mature vehicle technologies like combustion engines has proven that engineering simulation can play a vital role in accelerating development by guiding experimental testing and reducing inefficient trial-and-error testing. ANSYS software has the fundamental capabilities to create a "virtual battery" that would allow researchers to visualize and understand the transport of lithium ions, heat, fluid flow, and electrical currents within the battery cells.
"By automating parametric what-if studies, ANSYS simulation can not only reduce battery cost through improved design cycle times, but it also facilitates the design innovation needed to attack all of the other challenges," Collins said. Also, some of the details of the phenomena to be examined are expensive or even infeasible to observe directly via experimentation.
Over the last year, the GM/ANSYS/ESim team has prototyped and validated three electrochemistry modeling approaches. In simplified terms, Collins said those approaches are algebraic equations that assume the battery cell is an equivalent electric circuit (with resistors, capacitors, etc.), fitting the charge/discharge behavior to a set of characteristic curves that vary with the state of charge and temperature, and using a transport model that tracks the concentration of lithium within the thin porous electrodes.
The other achievement was a prototype of a co-simulation feature, which blends battery multiphysics and system simulation technologies. Specifically, a system simulation model of the entire battery back based on lumped parameters communicates at run-time with a finite-volume model of a cell based on detailed electrochemistry. Collins said the goal of running two types of simulation solvers simultaneously in a coupled fashion is to deliver a flexible array of options to balance model fidelity versus simulation cost, addressing a range of modeling needs.
As part of its role, ANSYS will develop and deliver CAE tools to predict battery performance at the individual cell level, as well as the whole battery pack. The CAEBAT deliverables will be rolled out in a series of releases to the project team and partners. However, plans call for ANSYS to deliver a commercialized offering in the next couple of years.
Beth, this is encouraging news. The application of CAE to this problem in a very targeted way should help in the development of new and better products. ANSYS has a lot of experience in related areas. Are there other CAE vendors involved?
@Naperlou: Good question. My guess is there must be many more CAE packages and capabilities involved, even some homegrown stuff that is specific to the EV battery problem. I think ANSYS sees this area as a big opportunity and is thus staking out some turf and aligning with partners to dig deep on the research.
When I started reading this story, I assumed I would be reading about thermodynamic modeling of the battery pack. I'm pleasantly surprised to see thow much of the mlecular performance can be modeled by CAE, all the way down to the lithium ion transport. Great story, Beth.
For industrial control applications, or even a simple assembly line, that machine can go almost 24/7 without a break. But what happens when the task is a little more complex? That’s where the “smart” machine would come in. The smart machine is one that has some simple (or complex in some cases) processing capability to be able to adapt to changing conditions. Such machines are suited for a host of applications, including automotive, aerospace, defense, medical, computers and electronics, telecommunications, consumer goods, and so on. This radio show will show what’s possible with smart machines, and what tradeoffs need to be made to implement such a solution.