Using Synthetic Data to Accelerate Battery Software Development

Battery Management Systems (BMS) are becoming the nerve centers of modern energy storage systems, with the global BMS software market poised to grow from $8B to $39B in the next decade. Yet, developing and validating BMS software comes with a major hurdle: lack of accessible, application-specific battery data for testing. Real-world battery data is difficult, expensive, and time-consuming to collect—often taking several months to cover the right operating conditions, failure scenarios, and degradation patterns. Join us for a deep dive into how oorja is solving this challenge through synthetic data generation. Our hybrid modeling approach—combining physics-based models with data-driven techniques—enables the generation of high-fidelity synthetic battery data, including fault behaviors such as voltage imbalance, thermal events, sensor faults, and the elusive “knee” phenomenon. In this session, we’ll explore:

1. Why synthetic data is a game-changer for BMS testing and validation

2. The core challenges of modeling battery behavior and how we overcome them

3. A real case study: Generating synthetic fault data for a 12S10P NMC battery pack.

4. What synthetic data can (and can’t) do, and when to use it.

5. We’ll showcase outputs from our Battery 360 interface and walk through a fault-injected dataset generated in just 48 hours—compared to 6 months of experimental testing.

Whether you’re building next-gen BMS software, working on fault-tolerant pack designs, or looking to stress-test your ML models, this webinar will show you how synthetic data can become your new superpower.