Batteryze provides a cloud-based platform for battery health diagnostics using AI, machine learning, and physics-based modeling. This technology monitors battery performance and predicts aging to optimize capacity utilization during use. The platform also enables battery circularity by accurately assessing the remaining useful life for repurposing or recycling applications.
Funding
Funding not disclosed
Founders
Product
Problem
Battery aging is complex and influenced by various internal and external factors, making it difficult to accurately estimate a battery's remaining useful life (RUL). Existing battery management systems (BMS) can control operating conditions, but the industry lacks non-invasive solutions for dynamically assessing the impact of aging on battery health. This unpredictability hinders optimal battery utilization, safety, and the potential for second-life applications.
Solution
Batteryze offers a cloud-based diagnostics platform that employs physics-based modeling and real-time data to actively monitor battery health and predict aging effects. By creating a digital twin of the battery, the platform captures the relationship between materials and stress factors, enabling accurate modeling of battery behavior. The platform's models are fed with real-time usage data, allowing for the prediction and optimization of aging behavior for different battery profiles. This enables predictive maintenance, extends battery life, and facilitates informed decisions regarding recycling or repurposing.
Target Audience
The primary target audience includes businesses and organizations that utilize batteries in their operations, particularly those focused on electric vehicles, energy storage systems, and other applications where battery health and longevity are critical.
Features
- Cloud-based platform providing a virtual copy of the battery through a digital twin.
- Physics-based models capture the relationship between battery materials and stress factors.
- Real-time data ingestion for accurate prediction and optimization of aging behavior.
- Predictive maintenance capabilities to optimize battery utilization and safety.
- Assessment of battery state of health (SOH) and remaining useful life (RUL).
- Determination of suitability for materials recycling or second-life applications.