AI on Every Battery
Our edge AI combines deep learning with first-principles physics to deliver dependable, real-time insights into power, energy, and degradation — enabling adaptive control to extend lifetime, reduce fast-charging time, and optimize performance.
Industry’s Battery Management Challenges
Diagnostics
Inaccurate estimation of key battery states, such as SoC and SoH
Leads to poor system visibility and inefficient control
Increases operational risks across all battery-powered industries
Prognostics
Existing solutions fail to predict future battery degradation accurately
Leads to over-conservative operation or unexpected failures
Results in inefficient replacement cycles and lost asset value
Optimization
Battery usage is often governed by static, rule-based control
Lacks real-time adaptation to changing conditions
Limits lifetime, efficiency, and overall system performance
Cognivity AI BMS
Our BMS represents the shift to Software 2.0 — where intelligence is learned from data, not hard- coded through rigid rules. Unlike traditional BMS software, Cognivity’s decentralized AI adapts in real time to the unique, path-dependent degradation of each battery. Since no two batteries age the same, on-board AI is essential for delivering precise diagnostics and optimal control based on actual usage and conditions.
Silicon-ready & scalable
Runs on any hardware — from embedded controllers to future low-power AI chips
Prune for speed or scale for precision, with no redesign required
Deployable without compromising performance or compatibility
End-to-end optimization
Learns across all layers — estimation, prediction, and control
Continuously adapts beyond the limits of static rule-based code
Enables dynamic, context-aware battery management
Beyond static code
Outperforms traditional software in managing complex battery aging
Adapts to real-world conditions where static models fall short
Maintains performance and reliability over time
Constant, stable runtime
Fixed FLOPS — predictable and consistent compute
No bottlenecks or dynamic resource allocation
Engineered for maximum reliability and real-time stability
Reliable & safe AI
Powered by a decentralized architecture with local intelligence
Keeps data on the battery — no cloud dependency
Ensures accurate control through physics-informed AI
TrinityBMS
From Cloud-Centric to Battery-Centric
The future of energy intelligence runs on the edge.
Clearly!
Cognivity’s layered AI software architecture runs directly on the battery — enabling real-time, high-accuracy insights into power, energy, and health. The system adapts locally, coordinates across fleets, and continuously improves — all without exposing sensitive data. The result: safer, more efficient operation with full data sovereignty.
Overcoming Challenges in Edge AI
Designed for silicon readiness and scalability, our AI runs on any hardware — from embedded controllers to next-gen low-power AI chips. Through hardware-targeted model optimization, we deliver real-time performance without compromising model awareness or functionality.
