Idea
Battery diagnostics platform providing edge enabled, accurate, physics-aware battery diagnostics 2X better than existing solutions.
Research Paper
Core Innovation
This paper introduces Pace, which integrates physics-based battery features with raw sensor data using dilated temporal convolution and chunked attention mechanisms. Its dual-head output captures both short- and long-term degradation patterns, significantly improving prediction accuracy and enabling efficient real-time edge deployment.
Why It Matters
Battery health management is critical for safety, cost reduction, and sustainability in electric vehicles and energy storage systems. Accurate health estimation improves maintenance scheduling and extends battery lifespan, reducing operational costs and environmental impact. Scalable real-time deployment enables broad adoption across diverse battery usage scenarios.
Market Size (TAM)
$20–50B TAM for battery management solutions; $2–5B SAM from electric vehicle and energy storage sectors. Driven by EV adoption growth and grid storage expansion.
Potential Customers & Pain Points
- Electric vehicle manufacturers – Need reliable battery health monitoring
- Energy storage providers – Require cost-effective battery maintenance
- Battery management system developers – Seek accurate degradation prediction models
- Fleet operators – Want to optimize battery usage and replacement schedules.
Business Model
Subscription-based SaaS platform offering battery health analytics with tiered pricing for OEMs, fleet operators, and energy storage providers; includes edge deployment support and integration services.
Competitive Landscape
- Nuvation Energy
- Cadenza Innovation
- Twaice
- Battelle
Implementation Challenges
- Integration with diverse battery chemistries and systems
- Data privacy and security concerns in real-time monitoring
- Adoption resistance due to legacy battery management systems
Validation Strategy
- Benchmark Pace against industry-standard models on multiple public and proprietary datasets
- Pilot deployments with electric vehicle manufacturers and energy storage operators
- Demonstrate real-time edge performance on embedded devices like Raspberry Pi
- Collect user feedback to refine model accuracy and integration workflows
Research Paper Overview
Pace: Physics-Aware Attentive Temporal Convolutional Network for Battery Health Estimation
Summary
Pace is a battery health estimation model that combines raw sensor data with physics-based features to accurately predict battery degradation. It uses specialized temporal and attention modules to capture short- and long-term patterns, outperforming existing models on large datasets and enabling real-time edge deployment.