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GP-ME-02VUMAAMechanicalReady

EV battery-pack thermal simulation

A reduced-order thermal simulation comparing four EV battery-pack cooling layouts across driving, charging, and hot-ambient conditions.

EV battery-pack thermal simulation project visual
GP-ME-02VUMAA · Mechanical
  • Python
  • NumPy
  • SciPy
  • Matplotlib
  • Jupyter

Project definition

Problem statement

EV battery cells generate heat during discharge, regenerative operation, and fast charging. Cooling route, ambient temperature, cell variation, and coolant heating affect both peak temperature and cell-to-cell uniformity.

The engineering problem is to compare cooling layouts with one transparent transient model while accounting for the energy used by active cooling.

Project objectives

  • Model twelve coupled cells with separate core and surface temperatures.
  • Represent passive cooling, air crossflow, one liquid route, and two liquid routes.
  • Compare urban, highway, and fast-charge loading at 25, 35, and 45 C ambient.
  • Measure peak temperature, cell spread, coolant outlet temperature, and cooling energy.
  • Verify heat-flow direction, state-of-charge bounds, and pack energy balance.

Project structure

Project components

01

Cell model

Calculates core and surface temperatures, heat generation, state of charge, and cell variation.

02

Cooling layouts

Defines passive, air, liquid serpentine, and dual-route heat removal.

03

Load profiles

Provides urban pulse, highway sustained, and fast-charge current histories.

04

Experiment

Runs all thirty-six controlled cases and retains the complete result matrix.

05

Evidence

Writes CSV, JSON, temperature plots, layout comparisons, and energy checks.

Methodology

Project workflow

  1. 01
    Select a case

    Choose the current profile, cooling layout, and ambient temperature.

  2. 02
    Calculate heat

    Current, resistance, temperature, and state of charge determine the cell heat input.

  3. 03
    Solve temperatures

    The model integrates core, surface, neighbor, and routed-coolant heat transfer.

  4. 04
    Measure results

    Peak temperature, spread, cooling energy, outlet temperature, and energy residual are calculated.

  5. 05
    Compare layouts

    All layouts are compared under the same load and ambient condition.

Demonstration scenario

The thirty-six-case study compares all four cooling layouts under the same urban, highway, and fast-charge profiles. The retained hot fast-charge case reaches 56.10 C with passive cooling and 50.96 C with dual-route liquid cooling.

Engineering

Tools and method

Tools
The project uses Python, NumPy, SciPy, Matplotlib, Jupyter for subject analysis, simulation, and results.
Thermal model
Python and NumPy for the twelve-cell thermal network and routed heat flux.
Numerical solution
SciPy for adaptive integration of the coupled transient equations.
Experiment evidence
CSV and JSON outputs with Matplotlib figures generated directly from retained results.
Verification
Automated checks for physical invariants, commands, evidence generation, and numerical conservation.

Testing

Evaluation

Evaluation measures

  • Peak core and surface temperature
  • Maximum cell-to-cell temperature spread
  • Time above 40 C
  • Coolant outlet temperature and cooling energy
  • Generated, rejected, and stored energy balance
  • Automated tests, coverage, and dependency audit

Project boundaries

  • Parameters are illustrative and are not fitted to a commercial cell.
  • The model covers normal charge and discharge heat transfer only.
  • It does not model thermal runaway, crash damage, coolant leakage, or certified abuse tests.
  • Results are comparative engineering evidence, not pack approval or safety certification.

Included

  1. 01Complete Python source code
  2. 02Passive, air, liquid serpentine, and dual-route cooling models
  3. 03Thirty-six-case experiment with CSV, JSON, and eight result figures
  4. 04103-page project report in PDF and editable Word formats
  5. 0516-page setup and usage guide in PDF and editable Word formats
  6. 0644 annotated references and sourced literature material
  7. 0750 automated tests with 99 percent statement coverage

Project record

No information is collected on this page.

Permanent project ID
GP-ME-02VUMAA
Catalogued
21 Aug 2026
Completed
24 Aug 2026
Verified
24 Aug 2026
Demonstration
Included in repository

Handover

After purchase

  1. 01
    Payment is confirmed

    The project is marked unavailable and cannot be purchased again.

  2. 02
    Repository access is granted

    The buyer's submitted GitHub account receives access to the private repository.

  3. 03
    The purchase record is delivered

    The certification sheet is prepared from the reviewed buyer details and sent privately by email.