AI with Model-Based Design: Virtual Sensor Modeling
Virtual sensor modeling is a resourceful technique that may be used to mimic the behavior of a physical sensor. Developing a virtual sensor (also known as soft sensor) can help in situations when the signal of interest cannot be measured, or when a physical sensor adds to much cost and complexity to the design. For example, when developing a Battery Management System (BMS) for an Electric Vehicle, laptop, etc., having an accurate value of the Battery State of Charge (SOC) is a critical element in the design. However, it may not be directly measured. Deep learning and machine learning techniques can be used as alternatives or supplements to Kalman filters and other well-known virtual sensing techniques. These AI-based virtual sensor models must integrate with other parts of the embedded system. In the case of a BMS, an AI-based SOC virtual sensor must be integrated with power limitation, fault detection, and cell balancing algorithms. Development of such a large and complex system requires integration, implementation, and testing of different components while minimizing expensive and time-consuming prototyping with actual hardware. Model-Based Design is a proven approach to accomplish this.
In this session, you will learn how to develop virtual sensor models using feedforward neural networks, LSTMs, decision trees, and other AI techniques. Using the example of BMS SOC estimation, you will learn how to integrate AI models into Model-Based Design, so that you can test your design using simulation and implement it on an NXP S32K3xx board using automatic code generation. You will see how to evaluate and manage AI tradeoffs that span from model accuracy to deployment efficiency.
- Designing and training machine learning components with Statistics and Machine Learning Toolbox
- Designing and training deep learning components with Deep Learning Toolbox
- Importing trained TensorFlow models into MATLAB
- Integrating machine learning and deep learning models into Simulink for system-level simulation
- Generating library-free C code and performing PIL tests
Please allow approximately 45 minutes to attend the presentation and Q&A session. We will be recording this webinar, so if you can't make it for the live broadcast, register and we will send you a link to watch it on-demand.
About the Presenter
Lucas García is a Senior Product Manager for Deep Learning at MathWorks. Lucas is a mathematician with over 15 years of professional experience working in the computer software industry and research. He joined MathWorks in 2008 as a customer-facing engineer and has worked with engineers and scientists across industries (aerospace, automotive, industrial automation, energy and utilities, oil and gas, robotics, etc.) to help them tackle real-world problems in Artificial Intelligence. Lucas holds a PhD in Applied Mathematics from Universidad Complutense de Madrid and Universidad Politécnica de Madrid. His research is focused on how neural networks can be used to solve combinatorial optimization problems.