Coulomb AI provides a predictive battery analytics platform that utilizes data-centric AI to monitor and optimize battery performance throughout their lifecycle. The software addresses issues of unreliable range, unexpected battery failures, and accelerated degradation, ultimately reducing operational downtime and total cost of ownership for electric vehicle operators.
Funding
$145K raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
Founders
Product
Problem
Electric vehicle (EV) operators face challenges with unreliable range predictions, unexpected battery failures, and accelerated degradation, leading to operational downtime and increased costs. Traditional battery monitoring relies on lab conditions, failing to provide real-time analytics that reflect actual usage and environmental factors. This lack of comprehensive data hinders proactive maintenance and optimization strategies.
Solution
Coulomb AI offers a data-centric AI platform that provides predictive battery analytics to monitor and optimize battery performance throughout its lifecycle. The platform delivers real-time insights into battery health, identifies key factors affecting failure and downtime, and suggests corrective actions to ensure uptime. By uncovering stress factors impacting battery health, Coulomb AI helps prevent premature aging, reduce total cost of ownership, and facilitate the transition of batteries to second-life applications through precise residual value determination. The platform integrates data from various stages of the battery lifecycle, ensuring consistent and high-quality data for monitoring, analysis, and optimization at scale.
Target Audience
The primary customers are battery manufacturers, battery-as-a-service providers, automotive OEMs, and fleet operators who seek to optimize battery performance, extend battery life, and reduce operational costs.
Features
- Real-time analytics on battery performance, identifying key factors affecting failure and downtime.
- Predictive maintenance recommendations to avoid breakdowns and ensure uptime.
- Identification of stress factors affecting battery health to prevent premature aging.
- Battery lifecycle data integration for monitoring, analysis, and optimization.
- Residual value determination for transitioning batteries to second-life applications.
- Range prediction based on real-time analytics, addressing the issue of unreliable range estimates.
- Battery Health Assessment product for in-depth analysis.
- Battery Observability Platform for real-time monitoring.