Leading energy companies are using Collimator to stay ahead of their competition and get the most out of their energy systems. From designing high-performance BMS systems to optimizing efficiency using a digital twin, Collimator accelerates innovation for their engineering teams.
Model your electrification project in hours - not days - using our reusable function blocks, e.g., battery management systems, battery cells, inverters
Use our system design tools including PID control and MPC, automated tuning, model linearization, linear analysis, etc. to gain insights into your system faster
Build data driven or physics based digital twins rapidly using system identification or machine learning
Derisk earlier by testing thousands of design concepts before narrowing down your specifications and system architecture
Simulate performance of your system before it goes into production including thermal effects, SOC, etc.
Optimize your energy production based on different demand scenarios, energy prices, weather forecast, energy demand
Generate high quality embedded code directly from your model and have any changes to the system’s design propagate down to the target hardware
Directly embed trained neural networks in your model to get end-to-end insight into your system performance. For example, AI and ML applications could include energy price predictions to proactively accelerate/decelerate production, predictive maintenance, etc.
Use the system you designed as a digital twin to simulate and test updates to your embedded or PLC software, train your ML, etc.
Stream data from production to evaluate efficiency, make performance improvements, or predict when the maintenance crew should be alerted
Automatically generate C code for your embedded system controller
Continuously build, test and deploy updates to embedded systems
Import neural networks to deploy in your system design
Reduce risk using modeling, simulation and end-to-end traceability
Generate and export synthetic data to train neural networks
Simplify data exploration and insights gathering through automated tests