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

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Introduction

As electric vehicles (EVs), renewable energy storage, and portable electronics continue to drive demand for high-performance lithium-ion batteries, effective thermal management becomes essential for ensuring safety, efficiency, cycle life, and compliance with end-of-life (EOL) warranty requirements. Over repeated charge-discharge cycles, battery cells undergo aging phenomena such as solid-electrolyte-interface (SEI) layer growth on the anode, which increases internal resistance, reduces capacity, generates more heat via ohmic losses, and degrades thermal coupling to the cooling system. These changes result in elevated cell temperatures, accelerated degradation, and potential safety risks during high-power operation.

This project focuses on the thermal analysis of new and aged battery packs using Simscape Battery in MATLAB Simulink. By modeling a representative 400V lithium-ion battery pack (e.g., modular architecture with parallel assemblies, modules, and liquid cooling), the simulation compares electro-thermal behavior under constant-current discharge (e.g., 2C rate) for both fresh (new) and aged (EOL after ~1000 cycles) configurations. Key insights include approximately 7°C higher maximum cell temperature in the aged pack, earlier coolant pump activation, increased pump power demand, slightly lower terminal voltage due to elevated resistance, and degraded heat transfer to the coolant (thermal resistance rising from ~1.2 K/W to 5 K/W). These results confirm thermal safety at EOL while highlighting the importance of robust design margins in battery thermal management systems (BTMS) for EVs and grid storage.

This Simscape Battery-based approach enables virtual prototyping, sensitivity analysis of aging effects, and optimization of cooling strategies — ideal for final-year engineering projects, research in battery degradation, EV BMS development, and thermal runaway prevention studies.

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.