A “Who’s Who” Guide to Battery Modelling Software in 2026
Part I
Over the weekend, our friends at the Volta Foundation released their 2025 Annual Battery Report. It’s a serious 767 page adventure collated by over 120 battery professionals. Two of those contributors, Daniel Cogswell and Andrew Weng, today bring you a ‘Who’s who’ of battery modelling, as a crash course intro to help those who might not know where to start.
In his 2022 book, “Chip War: The Fight for the World’s Most Critical Technology,” Chris Miller wrote about how software and design tools were central to U.S. competitiveness in integrated circuit design even as semiconductor manufacturing shifted abroad. Miller argued that economic competitive advantage shifts over time: early on, manufacturing and fabrication tools (e.g., lithography, cleanrooms, process control) were critical, but as factories became more global and foundry models matured, it was software and design methodologies that became the arena for value creation. Fast forward to today: firms like Nvidia are powering the AI revolution with advanced GPU architectures that are all designed using proprietary software ecosystems.
The story of the semiconductor industry foreshadows many of the challenges and opportunities facing the battery industry today. As the battery industry matures, value creation will move downstream towards the design of advanced battery systems and architectures.
To enable this value creation, software is central. But what does the battery software landscape look like today? What are the state-of-the art tools? What types of problems can they solve? If you wanted to explore the various tools out there, where do you even start?
At the core of battery software are modeling and simulation tools – those that are grounded on the physics of how batteries behave, those that support simulations across multiple length and time scales, and those that make the user experience seamless rather than a chore.
This two-part guide will help you navigate the battery modeling software landscape in 2026. We assume you want to build your own battery models and simulations but are unsure about where to start. Part one will begin by describing a few use cases: what are things one does with battery modeling software? We will then compare major battery simulation software vendors: which one is suitable for which use case?
Then, in part two, we will share thoughts and observations about software based on our experience developing and running battery simulations as part of research projects that span academia, industry, and consulting.
The Battery Software Pyramid
It is useful to think of battery software as existing along different levels of a pyramid. At the “basement level” are low-level numerical solver libraries. Battery simulation code exists on the next level; these software packages implement the various equations that describe battery dynamics and let the low-level libraries handle finding the solutions to these equations. One level above this is the User Interface (UI) which allows a non-programmer to interact with the battery simulation software. At the very top of the pyramid are software-as-a-service (SaaS). Here, users don’t need to bother learning code or user interfaces: they simply request for simulations - while providing necessary assets such as test data and/or battery design parameters - and receive the requested simulation results.
Each battery software tool originated at one level of this pyramid. For example, COMSOL Multiphysics, a popular finite element simulator that supports battery modelling, has been developed primarily at the user interface level (L2). Meanwhile, PyBaMM, a popular open-source battery modelling framework, exists primarily as a programming interface (L1). Over time, companies may opt to expand their coverage to more levels of the pyramid.
Use Cases: What Does One Do With Battery Software?
The battery simulation platform you choose to use will largely depend on your goals. (There probably isn’t a “one-size-fits-all solution,” at least not yet.) Here, we outline some of the most common use cases for battery simulation software. Each use case will impose different requirements for what the software needs to do. Let’s take a look at some of these different use cases from the perspective of the battery value chain.
Material design. Material design includes developing novel material systems for lithium-ion batteries, such as new electrode and electrolyte systems; engineering electrode microstructure; optimizing new classes of battery systems altogether, such as flow batteries; or developing new precursor material processing techniques, such as for lithium extraction.
Cell design. After cell chemistry is set, cell performance can still be tailored through cell design. For example, a graphite/NMC cell can either be a “power cell” or an “energy cell” depending on electrode loadings and thicknesses. The goal of cell design is to provide a set of electrode specifications that satisfy certain performance requirements. The most commonly accepted modeling framework for cell design is the Doyle-Fuller-Newman model (DFN), which predicts the effect of various cell design parameters on cell performance characteristics such as energy and rate capability. These input-output relations are highly non-linear and thus cannot easily be obtained from an Excel spreadsheet. A simulation based approach here helps the designer quickly iterate through design variants to identify the set of designs that can satisfy the needed performance requirements.
Pack design. After a cell design is fixed, the cells are typically integrated into a battery pack. Designing a battery pack requires considering the electrical and thermal characteristics of individual battery cells within the context of pack-level electrical and thermal network topology. The electrical networks will generally consider additional sources of resistances such as those from busbars and cell-to-pack welds. The thermal networks are determined by the pack cooling architecture, including the coolant flow and thermal conduction pathways. Since pack topology is geometry-dependent, accurate battery pack simulations will generally require some degree of finite element modeling. A key challenge in pack-level modeling is how the finite element simulations that predict pack-level behavior can be coupled to cell-level models without slowing down the simulation.
Reliability design. Reliability design is something every cell manufacturer thinks about. How do you design for a product to last 10 years before the product is even built and sold? Battery degradation models can have an important role to play here. Degradation is present in every battery system, and with an accurate degradation model, a designer can better predict future battery product risks, such as potential warranty costs, for each cell design variant and product use case.
The Battery Modelling Software Landscape
As contributors to the 2025 Battery Report, we mapped the growing array of software tools designed to simulate everything from individual cells to full battery packs. Illustrated in the figure below, these tools vary across a wide range of length and time scales, as well as in their user interaction models. Yet this simulation-focused view is just one part of a much larger software ecosystem that includes cloud-based fleet analytics, real-time battery management system (BMS) firmware, and AI-driven platforms for high-throughput materials discovery and manufacturing optimization.
In Part II of this article we will share some observations about the capabilities and ideal use cases for several of these important battery simulation software packages. Stay tuned.
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