SimLionics provides a physics‑based simulation and digital‑twin platform for lithium‑ion batteries that combines state estimation, thermal and safety modeling, and AI‑driven analytics across the battery lifecycle.
Funding
Funding not disclosed
Founders
Product
Problem
Battery development cycles are long and costly, with inconsistent performance, safety concerns, and unclear warranty risks due to limited ability to interpret complex battery data into actionable intelligence.
Solution
SimLionics offers a physics‑based simulation and digital‑twin platform that integrates state estimation, thermal and safety modeling, and AI‑driven analytics across the battery lifecycle. The solution creates predictive models that accelerate design optimization, enable early anomaly detection, and support autonomous control strategies such as fast‑charge protocols and SOH‑based power management. Continuous self‑learning agents refine predictions from operational data, providing predictive maintenance forecasts, warranty risk assessments, and use‑pattern insights. By delivering deep data interpretation and real‑time decision support, SimLionics helps battery engineers and operators improve performance consistency, reduce safety hazards, and lower development costs.
Target Audience
Primary customers are battery manufacturers, electric vehicle OEMs, energy‑storage system developers, aerospace battery integrators, and portable electronics companies that require advanced modeling, monitoring, and AI‑driven optimization of lithium‑ion batteries.
Features
- Physics‑based simulation and digital‑twin environment for lithium‑ion battery design and validation
- Comprehensive state estimation algorithms covering SOC, SOH, SOE, SOP, SOT and charge‑imbalance detection
- Thermal and safety modeling with early anomaly detection and cyber‑physical risk management
- Autonomous control agents for fast‑charge protocols, voltage/current/temperature limits, and power optimization
- Predictive maintenance and lifecycle forecasting using agentic AI and continuous model self‑learning
- Warranty risk and financial impact analytics derived from deep usage pattern intelligence
- Design‑of‑experiments support and deep data interpretation tools for accelerated development cycles