You will get a Physics-Informed Transformer for Battery Health Forecasting
Project details
This project focuses on predicting battery health, remaining useful life, and early signs of degradation before they turn into bigger problems. It applies to lithium ion batteries used in EVs, energy storage systems, or lab testing.
The models are physics informed, using Equivalent Circuit Model (ECM) parameters such as internal resistance and open circuit voltage as physical constraints, rather than relying on pattern matching alone. This grounds the predictions in how batteries actually behave and degrade over time, leading to more reliable results, even on data the model hasn't seen before.
What sets this approach apart is the combination of a technical background in Energy Engineering with hands on deep learning work on real battery and power sector data, ensuring the models are built with genuine understanding of the physical system, not just statistical accuracy.
The models are physics informed, using Equivalent Circuit Model (ECM) parameters such as internal resistance and open circuit voltage as physical constraints, rather than relying on pattern matching alone. This grounds the predictions in how batteries actually behave and degrade over time, leading to more reliable results, even on data the model hasn't seen before.
What sets this approach apart is the combination of a technical background in Energy Engineering with hands on deep learning work on real battery and power sector data, ensuring the models are built with genuine understanding of the physical system, not just statistical accuracy.
Machine Learning Tools
MATLAB, NumPy, Open Neural Network Exchange, pandas, PyMC, Python, Python Scikit-Learn, PyTorch, scikit-learnWhat's included
| Service Tiers |
Starter
$120
|
Standard
$215
|
Advanced
$520
|
|---|---|---|---|
| Delivery Time | 6 days | 10 days | 15 days |
Number of Revisions | 1 | 2 | 3 |
Number of Model Variations | 1 | 2 | 3 |
Number of Scenarios | 1 | 2 | 3 |
Number of Graphs/Charts | 3 | 6 | 10 |
Model Validation/Testing | |||
Model Documentation | - | ||
Data Source Connectivity | - | - | |
Source Code |
Optional add-ons
You can add these on the next page.
Additional Model Variation
(+ 3 Days)
+$55
Additional Graph/Chart
(+ 1 Day)
+$12About Manish
Data analytic | Energy Engineering, Power System
Jaipur, India - 3:33 pm local time
Steps for completing your project
After purchasing the project, send requirements so Manish can start the project.
Delivery time starts when Manish receives requirements from you.
Manish works on your project following the steps below.
Revisions may occur after the delivery date.
Data Review
Review the battery data you provide and check it's usable for modeling (checking for gaps, formatting issues, or missing fields).
Data Preparation
Clean and structure the data, including calculating relevant ECM parameters (e.g., internal resistance, open circuit voltage) needed for the physics-informed model.