Virtual sensor modeling is a powerful technique for mimicking the behavior of a physical sensor when the signal of interest cannot be directly measured, or when a physical sensor adds too much cost and complexity to the design. For example, when developing a Battery Management System (BMS) for electrified systems, having an accurate value of the Battery State of Charge (SOC) is a critical design element. AI-based techniques can be applied as alternatives or supplements to Kalman Filters and other well-known techniques. This example demonstrates the integration of AI models into system-level design, the execution of validation and verification, and the trade-off management between different deployment objectives.
This webinar presents a workflow offering end-to-end solutions for designing, training, validating and verifying, compressing, and deploying AI-based virtual sensor models to embedded processors within a single environment.
Highlights
- Integrate AI models into Simulink for system-level simulation, verification, and simulation-based testing
- Apply formal verification techniques to assert neural network behavior
- Compress the AI model for memory footprint reduction and execution speedup
- Generate library-free C code from AI models and performing PIL tests
- Profile code performance and evaluate design and model selection tradeoffs
- Design and train AI-based virtual sensors using MATLAB
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