#MATLAB 2019A KEEPS SAYING PROCESSING SOFTWARE#

Keeping System Documentation Current With the Increasing Pace of Agile Product Development Using examples from a recent program, Cummins shares how MathWorks tools are being applied in support of continuous product improvement efforts. Evaluation of model fidelity, identification of customer clusters, noise factor robustness testing, system simulation, and evaluation of performance and diagnostic capability use a number of tools to deliver efficient virtual product validation. System simulation (engine, aftertreatment, and controls) use cases for performance and diagnostic validation using MathWorks tools are demonstrated in this presentation. This simulation data is used to provide guidance to programs on controller tuning and to inform decisions on system performance and diagnostic robustness. Using these models, Cummins can perform both sub-system and system-level performance characterization and robustness testing, which is typically expensive, difficult, or impossible to accomplish in the real world.

To help meet these technical challenges, Cummins is utilizing MathWorks tools to perform simulations both in a hardware-in-loop (HIL) environment as well as using a fully virtual model-in-loop (MIL)/software-in-loop (SIL) environment with engine and aftertreatment plant models. The combination of tightening global emissions standards, increased customer expectations with regards to reliability, and demands for improved fuel economy from production line to Emissions-Useful-Life (EUL) presents a complicated set of technical challenges across all their product lines. Cummins manufactures and supplies engines ranging in displacement from 2.8L to 95L using a variety of fuels to a diverse customer and applications base across the globe. is the worldвЂ™s largest independent diesel engine manufacturer, celebrating their 100th year of operation in 2019. System Simulation for Robust Calibration and DiagnosticsД¬ummins Inc.

This talk also discusses leveraging the Simulink code-generation feature to generate C-code and its feasibility for real-time in-vehicle SOH estimation. As we have now started getting real data, we are validating this analytics stack. This enabled us to build the data pipelines and the analytics stack without having extensive vehicle data. We pushed this data to the cloud, then had the data pipelines pick this data and do all the downstream processing. We took the approach to leverage a calibrated Li-Ion Cell Chemistry model to generate the required data in various driving conditions. While building battery state-of-health pipelines, it is difficult to capture real data from the vehicle in various driving conditions. The vehicle model is comprised of a Simulink В® based electric vehicle model that includes Li-Ion cell chemistry-based battery models. This talk gives an overview of battery state-of-health (SOH) estimation and prognostics modeling that uses data generated from the vehicle model in the cloud. This presentation discusses the basic approach and development steps, and gives examples.Д«uilding Battery State-of-Health Estimation Pipelines for Electrified Vehicles

#MATLAB 2019A KEEPS SAYING PROCESSING SOFTWARE#

A Firmware-Update-Over-The-Air (FOTA) as well as adding dedicated software applications (Software-Update-Over-the-Air, SOTA) is supported. This approach enables the introduction of customer-specific functionality, on short notice and at any time worldwide. The ecosystem supports a uniform function development approach throughout the lifecycle of the vehicle, from concept through aftermarket.

To change this situation, Bosch develops an open ecosystem starting from the development environment in the cloud up to the global management, licensing, and distribution of software applications, with new simulation and model-based design approaches woven into the workflow. Later, new functionality or application updates can only be done in the service workshop or by specialist on site, if at all. Today, software features are defined during development. Innovative use cases in the field of autonomous, electrified, and connected commercial vehicles, as well as changing usage, lead to the need to add functionality after production. Keynote: Transforming Software Development for Future Autonomous, Electrified, and Connected Commercial Vehicles

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