Multi-Physics Battery Pack Design in Two Seconds
A Three-Layer Coupled Framework for Integrated Battery Pack Optimisation Across Vehicle Dynamics, Cell Chemistry, and Pack Architecture
What this whitepaper argues
Battery pack design for electric vehicles is a constrained multi-objective optimisation problem spanning three coupled physical layers: vehicle energy demand, cell electrochemistry, and pack architecture. Today these layers are analysed with separate tools, and a single design iteration takes one to two weeks. The manual transfer of data between tools introduces errors and obscures critical cross-layer tradeoffs.
PBPF-IDO solves all three layers as a single coupled optimisation in approximately two seconds. It evaluates five calibrated chemistries (LFP, NMC811, NCA, sulfide solid-state, and stabilised Li-S) on a Pareto frontier and identifies the lightest viable pack, together with a ranked sensitivity analysis that tells engineers which design parameter to change next for the biggest improvement.
This whitepaper sets out the three-layer framework, explains the input and output specification, walks through a real 500 km premium passenger EV worked example producing a 425 kg NMC811 pack at £6,859, and describes the validation approach. It is written for battery engineers, engineering managers, and technical decision makers evaluating design tools.
The three layers, at a glance
Layer A
Vehicle Energy Demand
Aerodynamic drag, rolling resistance, drivetrain losses, auxiliary loads, and the circular mass dependency resolved in one pass.
Layer B
Cell Chemistry Evaluation
Five calibrated chemistries evaluated against seven figures of merit: energy, power, cycle life, cost, mass, volume, and TRL.
Layer C
Pack Optimisation
Pareto frontier ranking, recommended pack selection, and binding-constraint sensitivity analysis across all input parameters.
Inside the document
What you will read
- ✓Why battery pack design currently takes one to two weeks per iteration and how coupled multi-physics optimisation eliminates the bottleneck
- ✓The three-layer framework: vehicle energy demand, cell chemistry evaluation, and pack architecture search
- ✓Full input specification: ten parameters, all with sensible defaults, configurable in under five minutes
- ✓The four outputs: energy budget, recommended pack, Pareto frontier, and ranked sensitivity analysis
- ✓Five calibrated chemistries: LFP, NMC811, NCA, sulfide solid-state, and stabilised lithium-sulfur with TRL and application context
- ✓Worked example: 500 km premium passenger EV scenario producing a 425 kg NMC811 pack at £6,859 with full sensitivity ranking
- ✓Validation approach: physics consistency, chemistry calibration, and cross-validation against known production vehicles
- ✓Pricing, deployment architecture, and how to get started
Who should read this
This whitepaper is written for battery engineers, pack designers, engineering managers, consultancy leads, and technical decision makers evaluating design tools for electric vehicle programmes. It is non-mathematical: no formulations, no source code, no calibration coefficients. The document describes what PBPF-IDO does, what it produces, and how it is validated, giving readers enough technical depth to evaluate the tool for their organisation without exposing proprietary methodology.
- ✓Battery engineers at EV startups and challenger OEMs
- ✓Pack design teams at Tier-1 automotive suppliers
- ✓Engineering leads at electric aircraft and eVTOL developers
- ✓Battery engineering consultancies serving multiple clients
- ✓University and government research laboratory leads
- ✓OEM advanced engineering teams evaluating next-generation chemistries
- ✓CTOs and VPs of Engineering making tooling procurement decisions
- ✓Investors and partners conducting technical due diligence on battery design technology
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