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  • Writer's pictureNicholas Yiu

System-level success: Battery cell selection for optimal performance


lots of batteries

So many cells, and so much to do!

Batteries are an essential component of modern-day life. From powering our mobile phones, laptops, and electric vehicles, batteries have become an indispensable part of our lives. However, with so many types of batteries available in the market, it can be challenging to choose the best one for your electric application. Say you’ve decided to use cylindrical cells in your application. There are over 500 models available on the market, and even if you could down-select a particular brand, there are still dozens to pick from. Samsung alone has 38 cylindrical cells.


The battery market is flooded with numerous brands, types, and chemistries. As a result, there is an ever-growing volume of suppliers to vet and cells to evaluate, making it difficult and costly for manufacturers to make an informed decision. In addition, there’s always a trade-off when it comes to picking cells. Where does one even begin when it comes to selecting the right cells to explore?


If you look at any battery on the market today its performance is a balance of the four variables of the corners of the tetrahedron. Energy density, Power density, capital cost, operating cost of batteries.
“If you look at any battery on the market today its performance is a balance of the four variables of the corners of the tetrahedron.” Dan Steingart.


Streamlining data to accelerate decision making

About:Energy has collaborated with ThermoAnalytics to demonstrate how the Voltt database can be used with 3D thermal simulation software TAITherm to aid in the selection of battery cells from hundreds of commercially available alternatives.

Logos of ThermoAnalytics and About:Energy

In an example case study, we explored five commercially-relevant battery types from the Voltt to determine the difference in performance using simulation.

  • LG M50L

  • Samsung 50S

  • Samsung 50E

  • Samsung 48X

  • Lishen LR21700SF

From the initial evaluation of these cells from datasheets, it’s challenging to decipher which may be best for our given application. In this example, we explored thermal heating profiles, usable capacity, pack weight, and estimated cost.



Walkthrough of the model

Opening up our thermal modelling software in TAITherm, we start with a model setup of 444 cells in a pack combined with fluid streams for cooling and joule heating for the bus bars, so we’re ready to model the thermal profile of the entire battery pack.


Assumptions behind the TAITherm model
Fig. 1: Introducing the TAITherm model

Traditionally, one of the major sources of errors in thermal modelling is how thermal parameters don’t accurately reflect the actual parameters of different commercial cells. Rather, modelling software uses generic parameters which are designed to apply to every single cell on the market, which leads to inaccurate models!


Screenshot of the About:Energy Battery library, The Voltt
Fig. 2: Screenshot of the Voltt database

By using the Voltt, we have access to a suite of accurate battery parameters and models in order to generate side-by-side comparisons to observe the effects of using different cells, with cell models available in various electrical and electrochemical formats. This enables us to use first principles and the fundamental laws of the universe to efficiently select a cell for optimal thermal performance.


Transient thermal profiles of packs with different cells in operation for the LG M50L, Samsung 50S, Samsung 50E, Samsung 48X, Lishen LR21700SF
Fig 3: Transient thermal profiles of packs with different cells in operation

Besides thermal performance, we’re also able to cross-compare different metrics that are available on the Voltt platform that are equally important to the end user, including usable capacity, pack weight, and cell cost, among other things. Metrics like usable capacity can also be compared against different temperature profiles.


Bar Chart Comparing the cells on various metrics for the LG M50L, Samsung 50S, Samsung 50E, Samsung 48X, Lishen LR21700SF.
Fig. 4: Comparing the cells on various metrics

To make it useful with testing teams, users have the capability to input their own drive cycles, ultimately creating a 3D thermal and electrical digital twin for the battery pack system and enabling customers to make data-driven decisions in cell selection and pack design.


Inputting specific drive cycles to observe exact performance behaviour of different cells

Inputting specific drive cycles to observe exact performance behaviour of different cells
Fig. 5: Inputting specific drive cycles to observe exact performance behaviour of different cells

Navigating the complex world of battery cells and their varying properties can be a daunting task. By leveraging cutting-edge tools like TAITherm and the Voltt platform, we can adopt a system approach for cell selection. This allows pack-level performance to be simulated, optimising critical properties such as thermal performance, usable capacity, pack weight, and cell cost, and ultimately make data-driven decisions for cell selection and pack design. Armed with this information, users can confidently choose the best battery cells for their application.

About:Energy Logo

If you missed the webinar hosted by ThermoAnalytics, you can watch the recording here.


We’re excited to work with battery system developers. If you're keen to learn more, please fill out our contact form and we'll be in touch soon. Alternatively, don't hesitate to reach out to us directly at sales@aboutenergy.co.uk.


About:Energy is a leading battery software company headquartered in London. The company was founded in 2021 by Gavin White and Kieran O’Regan, two researchers from Imperial College London and the University of Birmingham respectively. About:Energy has focused on building a portfolio of battery measurement and modelling capabilities to provide a software solution for battery design.


About:Energy provides organisations with the tools to streamline their R&D, reducing time-to-market and enhancing battery system performance. About:Energy’s data informs better decision-making across the value chain, from mine to end-of-life. These activities include battery system design, lifetime prediction, and cell optimisation. Customers include organisations across the automotive, manufacturing and aerospace industries.

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