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AI for Electrification

Apply artificial intelligence (AI) techniques to the development and operations of electrical technologies

With MATLAB^® and Simulink^®, you can model complex behaviors of electrical components and increase simulation speed by creating AI-based reduced order models (ROMs). You can create, train, and test AI-based virtual sensors and control strategies for motors, batteries, power converters, energy management systems, electric vehicles, and grid systems. MATLAB and Simulink help you to ensure safe and efficient operations of electrical systems by integrating AI-based energy forecasting and adopting AI-based predictive maintenance.

Free white paper: AI Techniques for Electrical Technologies

AI for Electrical Technology Development

Reduced Order Modeling

You can use AI and data-driven methods to create reduced order models for physical components (such as brushless motors and motor loads) or physical systems and use these models in your design. These methods help you significantly speed up simulations while still capturing the essential behavior of your system.

MATLAB, Simulink, and Simscape™ enable you to:

Create physics-based simulation models of systems, run simulations, and generate synthetic data for training AI models
Select from a library of prebuilt AI models and evaluate model performance by running multiple experiments
Directly integrate AI models in Simulink for model validation and testing by running simulations

A conceptual demonstration showing how a physics-based model of a passenger car in Simulink can be replaced by a AI-based reduced order model

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Reduced-Order Model for a PMSM Using Deep Learning

Examples

Reduced-Order Model for a Motor Load Using Deep Learning in Simulink

Virtual Sensor Modeling

When implementing power electronics controls, you can use AI to develop virtual sensor models to provide critical signals. Virtual sensors have no recurring bill-of-material cost, they are non-invasive, and they have no maintenance needs.

With MATLAB, Simulink, and Simscape, you can:

Create physics-based models of systems, run simulations, and generate synthetic data for training AI-based virtual sensors
Select from a library of prebuilt AI models and evaluate model performance by running multiple experiments
Directly integrate virtual sensor models in Simulink within physical system models for validation
Generate readable, efficient C/C++ code for embedded devices

A Simulink model showing a battery model and a detailed breakout of the deep neural network module for state-of-charge (SOC) estimation.

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Control Strategy

MATLAB and Simulink help you adopt AI-based, high-performance controls of complex, nonlinear, multi-input multi-output (MIMO) systems that require little prior knowledge of the physics of the plant. You can:

Train AI-based control algorithms against simulated environments in Simulink
Accelerate training by running simulations in parallel
Consult toolbox examples for iterative tuning of design parameters
Directly integrate trained models in Simulink for simulation-based verification

A system diagram showing the Field-Oriented Control (FOC) system of a PMSM (Permanent Magnet Synchronous motor) with a reinforcement learning agent as the current controller.

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Field-Oriented Control of PMSM Using Reinforcement Learning

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Train TD3 Agent for PMSM Control

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DNN control Based MPPT Techniques for 40 kWatt Wind Power System

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AI for Electrical System Operations

Energy Forecasting

MATLAB and Simulink can help reduce the efforts required to implement an AI-based energy forecasting system to predict electricity supply, demand, and pricing and mitigate uncertainties and risks in power system operations. You can:

Automate data preprocessing with the Data Cleaner app and Live Editor Tasks
Choose from a variety of built-in machine learning and deep learning models with little or no manual coding
Design deep neural networks interactively with the Deep Network Designer app and manage deep learning experiments with the Experiment Manager app
Use parallel computing to accelerate the AI training process
Operationalize and deploy the model to production cloud environments with MATLAB and Simulink in the Cloud

A screenshot of an energy forecasting system built in MATLAB.

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Long Term Energy Forecasting with Econometrics in MATLAB

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Data Analytics with MATLAB Webinar Files

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Predictive Maintenance

With MATLAB, Simulink, and Simscape, you can monitor the condition of assets and estimate their remaining useful life to minimize unplanned downtime, reduce operational costs, and ensure the reliability and safety of power systems.

Build physics-based models of electrical systems using Simscape Electrical, inject faults, and run simulations to generate synthetic data for training predictive maintenance models
Extract, visualize, and rank features to design condition indicators for monitoring power system health using the Diagnostic Feature Designer app
Identify root cause of failures and predict time-to-failure using prebuilt AI models such as classification, regression, and time-series models