How do you minimize the impacts of aging in your batteries and systems?

We show you how it is done by
understanding cell Aging

Challenge

Avoiding battery aging and ensuring the lifetime is a major task during battery cell and system development. Mastering battery aging is complex: You must account for nonlinearly coupled cell reactions in the timescale of milliseconds to aging mechanisms that take months. Thus, battery aging testing becomes lengthy. This is a major challenge, especially when technology evolves fast – like in the battery business.

Battery Aging

How much energy can the battery deliver in total?

How much energy can the battery deliver instantaneously?

This is true for many aspects of battery aging and battery lifetime. Let’s make some examples:

Which battery components age under what conditions and why?
What measures can I take to avoid aging and extent lifetime?
How can I optimize the cell design to extent the lifetime?
How does battery aging change when I go from cyclic aging to calendar aging?
What is the right approach to predict battery aging?

Answering these questions reliably in the available development time is very hard. State-of-the-art battery aging simulation methods are based on correlation: In these methods, aging is predicted by correlating the battery state-of-health (SOH) with measurement data via data fitting or AI training.

But: Be careful, this only works accurately where data was fitted.

A Battery is more than a capacity and a pulse resistance.

Correlative approaches are risky because the fundamental principles are not covered correctly!

This is especially true for the extrapolation range, which is exactly the one you need.

Solution - Battery Aging Model

You need a tool and workflow to master battery aging. Batemo combines a unique battery modeling technology with an efficient methodology to physically identify, simulate, and predict battery aging. This combination leads to success by making electrochemical aging states accessible at all stages of your product development process. We use optimized routines to identify the aging mechanisms non-invasively along different trajectories of aging tests. Because we integrate the aging identification into the batemo cell model, you can simulate the full behavior of aged cells under all tested scenarios and beyond. This is what makes aging prediction possible – physics-based.

Fast

Running aging tests takes months if not years. Get generic evolutions to simulate cell aging right away. The data integrates into the Batemo Cell Models and give you the best guess about the aging behavior of your cell before tests are avilable.

Physical

Getting battery aging right requires all all relevant electrical, chemical, thermal and physical processes inside the cell to be precisely described. This is what the Batemo Cell Model does – for the fresh and aged battery.

Accurate

The Batemo Cell Model is the most accurate battery cell model – guaranteed! We always demonstrate the validity both for fresh and aged battery cells to quantify accuracy.

This is great but let’s get specific: Below, you see the graphical user interface of the batemo cell model. With one click, you can put the physical, validated model in an arbitrary aging state. This means that all information from a test matrix with 3- to 4-digit GBs is available in seconds, exactly for the test that is of interest. How does the cell perform after 650 cycles and 40°C in my system? By aggregating the necessary information, you can answer this question immediately.

development method

Mastering battery aging requires simulations and tests to go hand-in-hand. A digital development workflow needs to be backed up by tests, anything else is dubious. With Batemo, however, you test specifically and the system is secured by predicting intermediate states. This is done several times throughout the development. In this way, you ensure that nothing happens in the field afterwards. In the essence, what you do is always using all information you have about battery aging, which is the path to success.

Benchmarking

Start now! With a physics-based translation of the degaradation modes of Batemo’s extensive aging database, we derive Generic Evolutions of your cell. In this way, you get the best educated guess about the aging behavior of your cell and you can start designing your system now.
Qualification

Test specifically! The predictions must be validated by tests, anything else is unreliable. Enhance the aging prediction continuously by your own qualification tests.
Development

Be sure! Use the information from the qualification tests and the aging predictions for system development enabling smarter design, longer lifespan, and more efficient battery management.
Validation

Check & Learn! Compare validation results with models and understand deviations. Reveal the root causes of battery degradation and understand how your cells aged and why.

Advantages

Using the batemo cell model combined with physical aging identification makes your cell and battery development simulation-based, making it faster, lower cost while leading to better batteries. This is how we generate value and contribute to your success.

Faster

With Batemo, you generate maximum information with minimum aging testing. This speeds up development and reduces the time-to-market of your battery. Just think of the lost revenue caused by a one-month delay in your approval cycle and multiply it by a factor of 6-12.

Better

Using the Batemo Cell Models you design batteries that are less prone to aging. Your battery system is backed by physical aging simulations making it robust against the relevant aging scenarios. You significantly increase the value of your product by extending its lifetime.

Lower Cost

With Batemo, you fully utilize your batteries without damaging them. This is how you reduce oversizing, unplanned cell failure and maintenance. In this way, you can save 6- to 7-digit amounts annually with moderate initial investment.