Learn about the speaker's decade-long experience developing battery management systems and control algorithms using Model-Based Design across a diverse application stream.

Maximize your C/C++ code quality, safety, and security with Polyspace as You Code plugin. Integrate this tool into your IDE, such as Visual Studio, Visual Studio Code, or Eclipse, to detect bugs early in the development process. Verify code quality before committing changes to your source code repository and receive instant feedback on vulnerabilities. Adhere to industry standards such as MISRA and CERT C/C++ with ease. Experience faster software delivery and higher code quality by integrating Polyspace^® code verification tools into your development environment.

In this talk, learn about the strong and varying hysteresis behavior for silicon-based lithium batteries that caused the open-circuit voltage of batteries to deviate between charge and discharge, resulting in significant impact on the SOC, SOH, and SOP algorithms. See how this issue was resolved with help from MathWorks to deliver a solution in a few months. Explore topics including the background of hysteresis and existing solutions (which is not based on SOC), test data collection for hysteresis parameter extraction approaches that separated pulse parameters and hysteresis parameters, and how Simscape^® was used to do a quick run. See how to build the discrete model and implement the embedded application technologies from Plett's hysteresis model and MathWorks toolboxes. Learn how to use MathWorks toolboxes to do parameter identification. The aim of the project was to have good battery algorithms for silicon-based lithium batteries. MathWorks toolboxes dramatically reduced the prototyping, software implementation, and MIL testing effort, and were the key success factor in this project.

Scenario harvesting is a workflow for generating digital twin data from vehicle logs utilizing Automated Driving Toolbox™ and RoadRunner.

This workflow consists of multiple data processing tasks, including:

• GPS and IMU sensor fusion to generate ego trajectory
• Ego localization using lane detections and a high-definition map
• Target vehicle trajectory creation from recorded lidar, radar, and vision sensors data

RoadRunner Scenario exports the scenarios created to OpenSCENARIO, which is used for regression testing of the advanced driver-assistance systems (ADAS) and autonomous driving (AD) algorithms.

In this talk, discover how to architect and automate a workflow with scripts that trigger runners in a DevOps platform like GitLab®. This frees time for engineers to focus on technical challenges while improving the speed and repeatability of continuous integration processes.

High currents in lithium-ion batteries may lead to significant thermal gradients depending on the cooling strategy. Above a certain level, these gradients will adversely affect performance and durability. Moreover, computing these gradients requires detailed modeling of the battery cell, making the approach unsuitable for system-level simulation. This talk describes the analysis of battery thermal gradients using a combination of finite element analysis and system-level simulation using model reduction.   

MATLAB^®, Simulink^®, and RoadRunner help engineers build automated driving systems with increasing levels of automation. In this session, you will discover new features and examples in R2022b and R2023a that will allow you to:

• Interactively design scenes and scenarios for driving simulation
• Generate 3D scenes from HD map data 
• Generate driving scenarios from recorded sensor data
• Simulate driving applications like emergency braking, lane following, lane change, platooning, and parking valet

Creating, validating, and correlating the model of a physical asset is important to building a digital twin, but modeling is only one aspect of the overall process of developing and deploying digital twins. In this presentation, we showcase a project from developing the model of an EV battery, deploying it to the cloud and connecting it to the data infrastructure, and predicting battery state of health based on data from a real-world electric vehicle fleet. Join us to learn about key considerations when planning your digital twin project.

Thermal management system design and its control play a critical role in the development of electrified powertrains. As modern thermal management systems get more sophisticated and capable, the control system design becomes exponentially more complicated and requires realistic yet fast physical models to be successful. In this talk, see field-proven examples of the latest capabilities of Simulink^® and the Simscape™ product family for modeling automotive thermal management systems and enabling control design with realistic physical plant models. Hear about modeling and parametrization of typical components in the thermal management system of an electrified powertrain and assembling them into a system-level model, which includes both coolant and refrigerant-based systems. You’ll also learn about the workflow of modeling the refrigeration cycle with different levels of fidelity and share resources available to support customers’ modeling efforts.

This session expands on “Vehicle Platooning Controller with V2V Communication,” presented at the 2022 MathWorks Automotive Conference, by adding a lateral control to follow a lane center of a curved road while maintaining a predefined space between vehicles in a platoon. The lateral control has been implemented using linearized truck-trailer lateral dynamics and model predictive control. The closed-loop control system includes vehicle-to-vehicle (V2V) communication and a high-fidelity 6-DOF vehicle dynamics model of a three-axle tractor towing a three-axle trailer. Using RoadRunner Scenario enables the creation of various test scenarios and cosimulation with the Simulink model implementing the platooning control.