Join Program

Refer your friends for our 16 Hours Intensive Training Session !

PyBaMM Users - Physics-Informed Battery Modelling

  +91 9150544484
Training Module & Schedule
DAY - 1 DAY - 2
Foundations & Core Usage
Introduction & PyBaMM Architecture
  1. What is PyBaMM?
  2. Why battery modeling matters
  3. Real-world problems:
    • EV range issues
    • Battery degradation
    • Charging optimization
  4. PyBaMM ecosystem (user perspective):
    • Model (physics)
    • Parameters (battery data)
    • Experiment (usage pattern)
    • Solver (execution flow)
Battery Basics & Parameter Sets
  1. What happens inside a battery (simple explanation)
  2. Key outputs:
    • Voltage
    • Current
    • State of Charge (SOC)
  3. Built-in models:
    • SPM-Fast
    • DFN-Accurate
  4. Parameter sets:
    • Different battery datasets (e.g., Chen2020, Marquis2019)
Experiments & Drive Cycles
  1. Charge vs discharge
  2. Different C-rates
  3. Temperature effects
  4. Experiment class (user control system)
  5. Real-world simulations:
    • Drive cycles (dynamic current profiles)
  6. Mapping simulations to real usage
Visualization & Interpretation
  1. Plotting results
  2. Comparing simulations
  3. Interpreting graphs:
    • Voltage drop
    • Performance differences
  4. Communicating insights to stakeholders
Real-life mapping
  1. EV fast charging impact
  2. Battery degradation analysis
  3. Simulation and waveform analysis
Hands-On Exercises
  1. Change current → see voltage change
  2. Compare slow vs fast discharge
  3. Simulate:
    • Fast charging
    • High load usage
  4. Compare:
    • Two batteries
    • Two charging strategies
 8 Hours
Advanced Usage & Problem Solving
Battery Degradation & Mechanisms
  1. What is battery aging?
  2. Capacity fade
  3. Degradation mechanisms:
    • SEI layer formation
    • Lithium plating (fast charging risks)
  4. Impact on performance and lifespan
Optimization & Model Selection
  1. Best charging strategy
  2. Avoid overheating
  3. Model comparison:
    • SPM → fast, simple
    • DFN → detailed, accurate
    • SPMe → balance between both
  4. Choosing the right model for the problem
Real Use Cases & Custom Parameters
  1. EV battery usage
  2. Solar storage system
  3. Mobile battery optimization
  4. Custom parameters:
    • Modifying current, temperature, conditions
    • Using user-defined data
Capstone Project & Parameter Estimation (Intro)
  1. Project:
    • Design a battery usage strategy for an EV
Tasks
  1. Run simulations
  2. Compare results
  3. Present findings
  4. Real-world problem solving
  5. Introduction to parameter estimation:
    • Matching simulation with real data (concept only)
Activities
  1. Battery draining too fast — why?
Hands-On
  1. Run aging simulation
  2. Try different charging profiles
Bonus Content
  1. Export results to CSV
  2. Basic automation scripts
  3. Connecting to real battery data
 8 Hours

Eligibility

  • Basic understanding of Physics and Chemistry (Battery basics).
  • Familiarity with Python programming (variables, loops, and libraries).
  • Interest in learning Physics-Informed Battery Modeling using PyBaMM.
  • Exposure to numerical simulation tools is a plus.

Training Objectives

  • Introduce participants to PyBaMM architecture and simulation flow.
  • Develop proficiency in model development and parameter estimation.
  • Enable participants to simulate battery degradation and capacity fade.
  • Familiarize learners with thermal modeling and multi-model comparison (SPM vs DFN).
  • Build capability to export results and automate battery analysis tasks.

Computational Requirements

Standard Laptop/PC with minimum 8GB RAM

Software Tools

Python 3.x, Jupyter Notebook, PyBaMM Library

Understand Battery physics and simulation flow using PyBaMM.
Develop, simulate, and analyze Lithium-ion battery models.
Predict State of Health (SOH) and degradation mechanisms accurately.

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.