How do your battery cells age and why?

Under­stand it with batemo!


Avoiding battery aging and ensuring the battery lifetime is a major task during battery cell and battery system devel­op­ment. Mastering battery aging is complex: You must cope with 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: 
Answering these questions reliably in the avail­able devel­op­ment time is very hard.


You need a tool and workflow to master battery aging. Batemo combines a unique battery modeling technology with an efficient method­ology to physi­cally identify and predict battery aging. This combi­na­tion leads to success. The under­lying idea is to use optimized routines to identify aging physi­cally along the different trajec­to­ries of the aging tests. On that basis you under­stand how your cells age and why. Because we integrate the aging identi­fi­ca­tion into the batemo cell model, you simulate the full behavior of aged cells under all tested scenarios. And this what makes aging predic­tion possible – physics-based. 


Running aging tests takes months if not years. The Batemo Cell Model runs fast and paral­lelized on clusters. You simulate hundreds of aging scenarios for your appli­ca­tion – overnight. 


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


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. 

Our method­ology for battery aging is very thorough and based on the idea to under­stand the separate physical processes that cause the battery to age.

  • 1st

    Create the Batemo Cell Model and use the deep physical insights to plan the aging tests optimally. 

  • 2nd

    Conduct the aging exper­i­ments and design inter­me­diate charac­ter­i­za­tion such that physical aging identi­fi­ca­tion is possible. 

    This is a proper basis!
  • 3rd

    Learn from the aging and physical identi­fi­ca­tion data to under­stand how your cells age and why. 

  • 4th

    Corre­late the aging exper­i­ments with the triggered aging mecha­nism and build a physical aging predic­tion model.

In this method, step one to step three are diffi­cult but well under­stood at Batemo. The fourth step of aging predic­tion is the hardest problem of battery modeling and an unsolved issue in the battery commu­nity. This is true for academia, corpo­rates and us. With Batemo, you are on the techno­log­ical edge of what is possible today.




With the Batemo Cell Model you generate maximum infor­ma­tion with minimum aging testing. This speeds up devel­op­ment and reduces the time to market of your battery. 


Using the Batemo Cell Models you under­stand battery aging. You design battery cells that are less prone to aging. Your battery system is backed by physical aging simula­tions making it robust against the relevant aging scenarios. This is how you extent the battery lifetime.

Lower Cost

With Batemo you under­stand battery aging physi­cally. Then you fully utilize your batteries without damaging them. This is how you reduce oversizing and produc­tion cost. Just look at your annual cost for cell procure­ment and subtract 10%. 


Let’s take the first step!