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Thermal Analysis of New and Aged Battery Packs Using MATLAB Simscape | MATLAB Simulink

Thermal Analysis of New and Aged Battery Packs in Simscape Battery | MATLAB Simulink Project – MATLAB Simulation Video
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MATLAB R2020a - R2024b
Zero Convergence Errors
Simscape / SimPowerSystems
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Project Methodology

The methodology leverages the Simscape Battery toolbox (introduced in R2022b and enhanced in subsequent releases) to create scalable, multi-domain electro-thermal models of battery packs, following the official MathWorks workflow for aging and thermal performance evaluation.

  1. Battery Pack Model Generation
    • Define battery components using Simscape Battery objects:
      • Cell — Parameterized with equivalent circuit model (ECM) including thermal effects, open-circuit voltage (OCV), series resistance (R0), and heat generation from ohmic and entropic sources.
      • ParallelAssembly and Module — Group cells (e.g., 12 cells per module) with inter-cell thermal conduction.
      • ModuleAssembly and Pack — Assemble into a full pack (e.g., 5 module assemblies × 5 modules each).
    • Use the buildBattery function to automatically generate a Simscape model library (e.g., batt_PackCellAgingModelLib.slx) with electrical and thermal ports enabled.
  2. Aging and Degradation Modeling
    • New Pack — Baseline parameters: nominal capacity, low internal resistance (R0), and efficient thermal path to coolant (~1.2 K/W).
    • Aged Pack (EOL) — Simulate lifecycle degradation after ~1000 cycles:
      • Increase terminal resistance R0 (e.g., progressive rise due to SEI growth).
      • Reduce usable capacity (fade).
      • Degrade cooling efficiency by elevating CoolantThermalPathResistance (e.g., to 5 K/W to represent interface aging and reduced heat transfer).
    • Parameterize via MATLAB scripts (e.g., separate parameter files for new vs. aged cases) and apply at runtime using mask parameters or workspace variables.
  3. Thermal Domain and Cooling System Integration
    • Enable thermal nodes on cells/modules for heat transfer.
    • Model liquid cooling: Connect Cooling Plate (TL domain) or Pipe (TL) blocks with discretized flow paths (parallel/U-shaped/edge cooling).
    • Implement coolant flow control (e.g., Battery Coolant Control block) based on temperature thresholds to regulate pump activation and flow rate.
    • Include ambient temperature boundary and conduction/convection paths.
  4. Simulation Setup
    • Apply worst-case load: Constant 2C discharge current (e.g., representative pack current for 30 minutes).
    • Configure environmental inputs: Initial state-of-charge (SoC), ambient temperature.
    • Run separate simulations for new and aged pack configurations using variable-step solvers (e.g., ode23t) for accuracy in thermal transients.
  5. Key Performance Metrics and Comparison
    • Monitor and compare:
      • Maximum cell temperature (T_max) — ~7°C higher in aged pack.
      • Voltage profile — Slight reduction in aged case due to increased resistance.
      • Coolant pump behavior — Earlier activation and higher power consumption in aged pack.
      • Temperature distribution, heat generation rate, SoC dynamics.
    • Use Scope, Dashboard Scope, or MATLAB post-processing for visualization and quantitative analysis (e.g., peak temperatures, energy efficiency).
  6. Analysis, Validation, and Extensions
    • Validate against expected degradation trends (e.g., higher I²R losses and poorer heat rejection in aged cells).
    • Assess thermal safety: Confirm no runaway risk under the tested conditions.
    • Extend to parametric studies: Vary cooling topology, discharge rates, ambient conditions, or aging severity.
    • Optional: Integrate BMS logic, fast-charging profiles, or real-time hardware-in-the-loop (HIL) testing.

 

Verified MATLAB Simulation Code Demonstration

Syntax-highlighted executable code demonstration for Thermal Analysis of New and Aged Battery Packs Using MATLAB Simscape | MATLAB Simulink:

MATLAB simulink_physical_model.m
% Dynamic Physical Model & Solver Configuration
clc; clear; close all;

% Hydraulic & Mechanical ODE System Parameters
m = 1.0; c = 0.5; k = 9.0;
ode_sys = @(t, y) [y(2); -(c/m)*y(2) - (k/m)*y(1)];

% Numerical ODE Integration
tspan = [0 10]; y0 = [1.0; 0.0];
[t, y] = ode45(ode_sys, tspan, y0);

fprintf('ODE Physical System Solved across %d Time Steps!\n', length(t));
Thermal Analysis of New and Aged Battery Packs Using MATLAB Simscape | MATLAB Simulink $85.00
$85.00