Artificial Intelligence, Automated Driving and Model-Based Design Using MATLAB and Simulink


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MathWorks is happy to announce a full-day complimentary seminar focusing on system design, development and analysis using MATLAB/Simulink. The technical sessions have been planned around the upcoming technologies like Digital Twin, IOT, Predictive Maintenance, Automated Driving, Electric Vehicles, Battery Modeling Deep Learning, Code Generation and Software Development in the industry.


Through the multiple sessions, you will learn how to:

  • Build a digital twin for a system, why and when to build it
  • Use techniques to preprocess data, extract relevant features and build predictive models using machine learning
  • Use deep neural networks on image, signal and text data
  • Build system simulation models along with synthetic scenarios for testing your automated driving algorithms
  • Customize readily available HEV and EV models for your vehicle architectures
  • Design power electronics control and battery management systems
  • Develop system architecture and algorithm model from the requirements
  • Generate and optimize C code from Simulink models

Who Should Attend

Engineering professionals involved in automotive system design, development, and test, including:

  • New Technology / R&D Teams
  • Alternate Propulsion / HEV-EV Teams
  • Diagnostics and Prognostics Teams
  • Advanced Engineering Teams
  • Function Developers, HIL Test Engineers
  • Embedded Software Developers, Electrical-Electronics Teams

There is no fee for this seminar. Please note this seminar is not designed for students. If you are a student, please do not register for this seminar. You will be required to show a professional ID card to gain access to the seminar.


Time Title
9:30-10:00am Registration


MathWorks Keynote: Adopting AI in Today’s Engineering World


Customer Keynote by Dr. Saravanan Muthiah, GM – Advanced Technology (Auto & Farm) Mahindra & Mahindra Ltd


Integrating Digital Twin and AI for Predictive Maintenance

  • Concepts of digital twin and overview on building a twin
  • Identifying key condition indicators for building a condition monitoring algorithm
  • Applying MACHINE LEARNING for developing predictive models / RUL models


Tea Break


Deep Learning and Reinforcement Learning Workflows in AI

  • Automating preparation and labeling of training data
  • Interoperability with open source deep learning frameworks
  • Training deep neural networks on image, signal, and text data
  • Tuning hyper-parameters to accelerate training time and increase network accuracy
  • Generating multi-target code for NVIDIA®, Intel®, and ARM®


Automated Driving System Design and Simulation

  • Create synthetic scenarios and test sensor fusion and control algorithms using system simulation
  • Improve simulation fidelity with gaming engine integration, vehicle dynamics modeling, and automated scenario creation from recorded data
  • Design LIDAR, vision, radar, and sensor fusion algorithms with recorded and live data


Lunch Break


Model-Based Design Workflow for Application Software Development

  • Capture, view, analyze, and manage requirements
  • Develop system architecture and algorithm model from the requirements
  • Gain confidence in design by doing early verification and validation of Algorithm Models
  • Use Logical and Temporal Assessments to translate informal text requirements into unambiguous readable assessments
  • Generate and Optimize C Code from Simulink Models
  • Back-to-back testing of generated code and model for consistency
  • Understand how Embedded Coder enables Classic and Adaptive AUTOSAR Code Generation

Vehicle System Simulation for Powertrain Electrification

System level simulation of Hybrid and Electric vehicles for component sizing, design trade-off studies and controller design

Power Electronics System Design and Implementation

  • Modeling power electronics systems from schematics, component sizing
  • Design control algorithm based on time/frequency domain specification.
  • Implementation of the power electronic controls on an embedded processor.

Developing Battery Management Systems Using Simulink

  • Different approaches for battery modeling: From battery cell to pack
  • Modeling temperature dependence
4:00-4:15pm Wrap-up

Product Focus

Registration closed