AI on every batteri - powering the future
Our edge AI solution delivers real-time insights into battery characteristics like available power and energy, while also offering prognostics toanticipate future degradation. This enables adaptive control strategies and system optimization using deep reinforcement learning, helping to extendlifetime, reduce fast-charging time, and ensure smarter operation underdynamic conditions.
Industry's Battery Management Challenges
Diagnostics
Inaccurate estimation of key states like State of Charge (SoC) and State of Health (SoH) leads to poor system visibility, inefficient control, and operational risks across industries.
Prognostics
Current solutions can’t reliably predict future degradation paths. This leads to over-conservative usage, unexpected failures, or inefficient asset replacement cycles.
Optimization
Battery usage (e.g. charging) is typically controlled by static rules, not optimized in real time. This limits lifetime, efficiency, and performance under variable conditions.
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.
Cognivity AI BMS
Silicon-ready & scalable:
Runs on any hardware, current or future low-power AI chips. Prune for speed, scale for precision — no redesign needed.
End-to-end optimization:
Learns across all layers — estimation, prediction, control — beyond what static code can achieve.
Smarter than hand-coded logic:
Outsmarts traditional software in managing complex, real-world battery aging.
Constant, stable runtime:
Fixed FLOPS, no bottlenecks, no dynamic allocation — maximum reliability.
Reliable & Safe AI:
Powered by decentralized architecture, where data stays on the battery and physics-informed machine learning ensures accurate, physically meaningful control.
TrinityBMS
From Cloud-Centric to Battery-Centric
The future of energy intelligence runs on the edge. Clearly!
Overcoming challenges in edge AI
Designed for silicon readiness and scalability, our AI runs on any hardware — from today’s embeddedcontrollers to next-generation low-power AI chips. With support for pruning and quantization, themodels can be compressed for real-time performance. But we go further: each deployment iscarefully tuned to preserve broad conceptual awareness, ensuring functionality isn’t sacrificed forspeed. The result is a balanced, efficient system — ready for edge deployment without compromise.
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 inreal 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.