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PyBaMM Developer - Physics-Informed Battery Modelling

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Training Module & Schedule
DAY - 1 DAY - 2 DAY - 3
CORE FRAMEWORK & EXPRESSION TREE
Introduction to PyBaMM Developer Architecture
  1. Understand PyBaMM’s internal architecture
  2. Learn how mathematical equations are represented symbolically
  3. Overview of PyBaMM pipeline
  4. Introduction to symbolic computation
  5. Core classes:
    • pybamm.Symbol
    • pybamm.Parameter
    • pybamm.Variable
  6. Expression tree structure (parent-child relationships)
  7. How equations are constructed internally
Advanced Parameter Handling
  1. Parameter class overview
  2. FunctionParameter usage
  3. InputParameter for runtime updates
  4. Sensitivity analysis basics
  5. Parameter substitution during simulation
  6. Enable dynamic and runtime parameter control
Building a Simple ODE Model (Non-Battery)
  1. Creating a model from scratch
  2. Defining variables and equations
  3. Example: Cooling system ODE
  4. Setting initial and boundary conditions
  5. Learn PyBaMM syntax without battery complexity
PyBaMM Pipeline Deep Dive
  1. Model definition
  2. Geometry setup
  3. Mesh generation
  4. Discretisation process
  5. Solver execution
  6. Understand the full simulation pipeline
Debugging Basics & Troubleshooting
  1. Common error types
  2. Interpreting error messages
  3. Basic debugging techniques
  4. Fixing common issues
Hands-On Exercises
  1. Modify parameters dynamically
  2. Run simulations with varying inputs
  3. Build and solve a simple ODE model
  4. Inspect model objects
  5. Print equation trees
  6. Modify variables and parameters
  7. Introduce small errors and debug
 ~8.5 Hours
PDEs, SUBMODELS & PHYSICS EXTENSIONS
PDE Model Development
  1. Diffusion equation fundamentals
  2. Domain concepts:
    • Negative electrode
    • Separator
    • Positive electrode
  3. Boundary conditions
Submodel Architecture
  1. Concept of submodels
  2. Structure of PyBaMM models
  3. Interaction between submodels
  4. Plug-and-play architecture
Developing Custom Physics (Submodels)
  1. Writing a custom submodel
  2. Example implementations:
    • SEI growth
    • Lithium plating
  3. Integrating submodels into existing models
  4. Extend PyBaMM with new physics
Discretisation & Mesh Generation
  1. Finite Volume Method
  2. Mesh creation
  3. Spatial methods
  4. PDE to algebraic system conversion
  5. Discretisation settings in PyBaMM
  6. Mesh quality considerations
Integration Lab
  1. Attach custom submodel to DFN
  2. Run simulations
  3. Compare results with base model
Hands-On
  1. Implement a simple diffusion PDE
  2. Implement a basic degradation model
 ~8.5 Hours
SOLVERS, PERFORMANCE & REAL-WORLD DEVELOPMENT
Solver Internals
  1. ODE vs DAE systems
  2. CasadiSolver
  3. IDAKLUSolver
  4. Time-stepping methods
  5. Solver settings and parameters
Debugging & Error Handling
  1. Common solver errors
  2. Convergence issues
  3. Stability problems
  4. Debugging workflow
Performance Optimization
  1. JAX acceleration
  2. Parameter sweeps
  3. Model simplification techniques
  4. Improve simulation efficiency
Developer Workflow & Contribution
  1. Testing with pytest
  2. Documentation with Sphinx
  3. GitHub workflow
  4. Pull request structure
Final Project & Review
  1. Project:
    • Build a custom battery model including:
      1. Degradation effects
      2. Thermal behavior
      3. Custom parameters
  2. Activities:
    • Code review
    • Q&A session
    • Career guidance
Hands-On Exercises
  1. Fix broken simulations
  2. Implement a simple degradation model
  3. Optimize a simulation
 8 Hours

Eligibility

  • Fundamental Science: In-depth understanding of Electrochemical Systems and Thermodynamics relevant to secondary-cell battery architectures.
  • Software Engineering Foundations: High proficiency in Pythonic development, encompassing complex data structures and numerical computing libraries.
  • R&D Research Focus: Professional commitment to mastering Physics-Informed Mathematical Models using the PyBaMM open-source framework.
  • Analytical Expertise: Hands-on exposure to Numerical Methods and PDE Solvers (FVM, FEM) is highly advantageous for high-fidelity modeling.

Training Objectives

  • Symbolic Architecture: Master expression trees and PyBaMM's core modular engine.
  • Dynamic Control: Implement real-time parameter sensitivity for complex battery cycles.
  • Physics Modeling: Build custom PDE submodels for SEI growth and aging.
  • Solver Optimization: Optimize performance using JAX and advanced solver integration.
  • Dev Workflows: Master pytest and professional Open Source contribution strategies.

Computational Requirements

Standard Laptop/PC with minimum 8GB RAM

Software Tools

Python 3.x, Jupyter Notebook, PyBaMM Library

Architect and Implement Custom Physics Submodels within the PyBaMM framework.
Master Symbolic Expression Trees and Numerical Solver Integration for Battery R&D.
Contribute to Open-Source Battery Modeling with Production-Grade Python Workflows.

Career Advantage

Exclusive placement support and industry exposure for PyBaMM learners.

Capstone Project

A comprehensive 4-week project that you can showcase in your GitHub repository.

Skilled@ AI Assessment

Unlimited skilled@ AI assessments to sharpen your skills until you achieve your best score.

Mock Interviews

Dedicated mock interview session by a hiring manager from a EV company.

Real Interviews

Minimum of two real-time industry interviews from the EV ecosystem companies.

EV Ecosystem & Hiring Partners

Explore the EV Industrial Landscape

Tesla
BYD
NIO
Rivian
Lucid Motors
VinFast
XPeng
Hyundai Motor Company
Volkswagen Group
General Motors

Tata Motors
Mahindra Electric
Ola Electric
Ather Energy
TVS Motor Company
Bajaj Auto
Hero MotoCorp

Simple Energy
Euler Motors
Ultraviolette Automotive
Tork Motors
Revolt Motors
Pravaig
Emflux Motors
Matter Motor Works
Bounce Infinity
River Mobility

Okinawa Autotech
Pure EV
BGauss
ESprinto
Altigreen
Omega Seiki Mobility
Keto Motors
Ampere Vehicles
Strom Motors

Exide Industries
Amara Raja Energy & Mobility
Log9 Materials
Sun Mobility
Exponent Energy
ChargeZone
Bosch India
Continental Automotive
Sona Comstar
Tata Agratas
Statiq
Delta Electronics

Yulu
Zypp Electric
BluSmart
EVeEZ
Bounce Infinity (Fleet)

We have partnered with interview partners from the Battery, EV, and Energy Storage Sectors.

Interview opportunities will be offered to participants who demonstrate readiness through a completed GitHub repository and a qualifying AI assessment score.