Design Software &
Engineering Tools
Battery pack design cannot be practised with intuition alone. This chapter maps the full software ecosystem — CAD, thermal simulation, FEA, CFD, battery modelling, and digital twins — and gives beginners a clear, prioritised roadmap of what to learn first.
Battery pack design is not a discipline that can be practised with pen, paper, and intuition alone. The systems involved — electrochemical cells interacting thermally, electrically, mechanically, and chemically over tens of thousands of operating hours — are too complex, too coupled, and too unforgiving of error to design reliably without computational tools. A battery pack that fails in the field can injure or kill people.
Software tools serve three distinct purposes in battery pack design:
Simulations identify design problems early — when they are inexpensive to correct — rather than during physical testing, when tooling has been committed and prototypes built. A design problem discovered in simulation costs a few hours of engineer time. The same problem discovered in a prototype costs weeks and potentially millions of rupees in rework.
CAD tools are the foundation of battery pack engineering. Every physical component must be modelled in CAD before it can be manufactured, assembled, or validated. The CAD model is the single source of truth for the pack's geometry and the starting point for every downstream simulation and manufacturing process.
| Tool | Primary Use | Difficulty | Prevalence | Best For |
|---|---|---|---|---|
| SolidWorks | 3D solid modelling, assembly, drawings | Moderate | Very High | Startups, SMEs, tier-2, EV makers |
| CATIA | Large assemblies, vehicle integration, PLM | High | OEM-specific | Premium automotive OEMs |
| Creo | Solid modelling, manufacturing integration | Moderate–High | Industrial sectors | Defence, aerospace, industrial |
| AutoCAD | 2D drafting, schematics, layout drawings | Low–Moderate | Universal | 2D documentation across all sectors |
Simulation tools allow the designer to virtually test the battery pack design under the conditions it will experience in service — before any physical hardware exists. The value lies in identifying design problems early, when they are inexpensive to correct.
The accuracy of any simulation depends critically on the accuracy of its input data. Garbage in, garbage out applies with particular force to battery thermal simulation. Cell heat generation rates must be measured through calorimetric testing of the specific cells being used — not estimated from generic data. An unvalidated simulation, no matter how sophisticated the tool, should be treated as an estimate rather than a prediction.
Finite Element Analysis is a tool that battery designers need to understand at a conceptual level — sufficient to set up meaningful analyses, interpret results correctly, and critically evaluate simulation outputs — even if they do not perform advanced FEA themselves.
Computational Fluid Dynamics simulates fluid behaviour — coolant flow through cold plates and cooling channels — to optimise thermal management performance. CFD addresses questions that cannot be answered by thermal conduction simulation alone.
Battery modelling tools go beyond structural and thermal simulation to model the electrochemical and electrical behaviour of cells and packs — determining how the battery performs, degrades, and responds to different operating conditions over its lifetime.
Simulation is not a replacement for physical testing — it is a complement to it. The relationship between simulation and physical testing is one of progressive validation: simulations guide design decisions and predict outcomes, physical tests confirm simulation accuracy and reveal phenomena that simulations do not capture.
Design Decision
Run Simulation
Physical Test
Compare & Correlate
Validated Model
Every major simulation used in a development programme should be correlated against physical test data — measuring the physical pack under defined conditions, comparing simulation results to measured results, and refining the model until results agree within acceptable tolerance.
Once a simulation model has been validated against test data, it becomes a powerful tool for design optimisation. A virtual DOE uses the simulation to systematically evaluate the effect of design variables — TIM thickness, cold plate channel geometry, cell spacing, busbar cross-section — on performance metrics, identifying optimal design parameter combinations far more efficiently than physical testing alone could achieve.
A simulation model that has not been correlated against physical test data should be treated as an estimate, not a prediction. The sophistication of the simulation tool does not substitute for model validation. Many costly design errors in battery engineering have resulted from accepting simulation results at face value without physical correlation.
A digital twin is a live, continuously updated computational model of a specific physical battery pack — not a generic pack-type model, but a model of a specific pack with its specific cell characteristics, usage history, and degradation state. It consumes real-time sensor data and uses this data to produce predictions specific to that individual pack.
sensor data
commands
For someone entering the battery design industry without existing software skills, the question of which tool to learn first is one of the most practically important career decisions they will make. The following priority sequence applies to the majority of entry-level and junior battery pack design roles.
If you are starting from zero today, the single most valuable action is to download SolidWorks (student edition or trial), find a battery module reference design, and spend 90 days modelling every component from scratch. This will teach you more about battery pack design — and make your CAD skills more real and demonstrable — than any amount of passive reading. Portfolio projects built in SolidWorks are the most direct path to a first battery design role.
