Battery state of charge (SOC) is a critical signal for a battery management system (BMS). Yet, it cannot be directly measured. Virtual sensor modeling can help in situations like this, when the signal of interest cannot be measured or when a physical sensor adds too much cost and complexity to the design. 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.
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.
