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Addressing Engineering Challenges of Vehicle Electrification With Model-Based Systems Engineering
The concern for the environment and energy savings is changing the way we think about transportation. Wide spreading vehicle electrification – not only through Electric Vehicles (EV) and Hybrid Electric Vehicles (HEV), but also electrification in conventional vehicles – has become a common trend of the industry and the upcoming battlefield to install new leading positions. Accounting for costs, reliability, safety, performance, customer acceptance, infrastructure and design process makes manufacturers and suppliers facing new engineering challenges that need to be addressed in a very short time-frame.
Technologies used for electrification are causing a growing complexity in systems and components, and producing vehicles designed right, first, at reasonable costs make the implementation of collaborative mechatronic system simulation a decisive and mandatory step in the engineering process.
This webinar will focus on the following key topics:
• What are the global trends and challenges of vehicle electrification?
• What are the available technologies for reducing CO2 emissions?
• What are the benefits of stop & start and regenerative braking systems?
• How to characterize battery and optimize its thermal management?
• How do energy storage architectures impact battery aging?
Presenter
Himanshu Kalra – Application Engineer, Siemens
Himanshu Kalra is an Application Engineer with Siemens PLM Software. He graduated with his Masters of Science degree in Mechanical Engineering from Michigan Tech University and his Bachelors in Mechanical Engineering from Institute of Management and Technology, India. He works with Model Based Systems Engineering (MBSE) Simulation tools to model and analyze vehicle electrification strategies, including thermal management, battery characterization and the impacts on battery ageing. He also has an experience working with technologies used for reducing emissions on internal combustion engines.
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Battery Analytics Tutorial Course 2/3: Data Capture and Trend Reporting
This one-hour webinar is Part 2 of a 3-part series. Battery management systems take large amounts of sensor data readings on a continual basis as part of their functionality. Battery analytics involves leveraging battery performance data for tasks such as identifying issues that can reduce battery life, flagging behavior that can negatively impact energy storage system performance, and predicting remaining cell and pack life.
This webinar will focus on the following key topics:
• Sensor data capture, aggregation and manipulation into performance reports
• Real-life examples will be shared, where aggregated historical data was analyzed and anomalous behaviors were identified
• Also shared will be the inspections and testing of the pack to identify the cause of the anomalous behavior, and the discovery and resolution of the problems that caused the anomalies
Presenter
Michael Worry – CEO at Nuvation Energy
Michael Worry founded Nuvation in 1997 and has grown the company over 21 years into a thriving electronic products and engineering services firm with offices in Sunnyvale, California and Waterloo, Ontario Canada. He is the CEO of Nuvation Energy, a provider of battery management systems and engineering services for large-scale energy storage systems.
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Battery Selection Tutorial Course 2/3: Beyond the Standards: Device-Specific Testing
After choosing your cell and manufacturer (Part 1 of this series), most likely, they will have passed the tests of various standards organizations. However, depending on your operating environment, you may need to go above and beyond the baseline to ensure your product operates as intended. This webinar is Part 2 in a three-part series and will review a variety of factors to consider in your device-specific testing, including designing tests to predict the outcomes of various user-abuse scenarios, understanding the mechanisms of gas generation, capacity retention based on different voltage windows, and what happens if you need to cycle your cells outside of their operating range (outside in an Arizona summer or Minnesota winter, for example).
This webinar will focus on the following key topics:
• User-abuse scenarios to prevent against
• Causes and effects of various gas generation mechanisms
• Voltage limits
Presenter
Exponent – a multidisciplinary engineering and scientific consulting firm with significant experience in various aspects of battery design, safety testing and failure analysis.
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Battery Analytics Tutorial Course 3/3: Predictive Modelling, Machine Learning, and AI
This one-hour webinar is Part 3 of a 3-part series. It moves from a discussion of data capture and trend reporting explored in Part 2 to predictive modeling, machine learning, and artificial intelligence as the next levels of battery analytics.
We will examine how machine learning and artificial intelligence can be implemented to identify hidden correlations between disparate data and energy storage system performance, and also independently take pre-emptive action to increase ESS reliability and battery life.
Real-life examples will be shared where predictive models could have flagged anomalous behaviors that were experienced in the field, and led to corrective actions to mitigate unplanned costs and labor.
This webinar will focus on the following key topics:
• Coming to Terms – Understanding the differences between machine learning, artificial intelligence, deep learning, and rule-based systems
• Predictive Modeling Approaches – using data mining and probability to forecast outcomes
• What’s Next – How AI and Machine Learning will impact large-scale battery energy storage
Presenter
Michael Worry – CEO at Nuvation Energy
Michael Worry founded Nuvation in 1997 and has grown the company over 21 years into a thriving electronic products and engineering services firm with offices in Sunnyvale, California and Waterloo, Ontario Canada. He is the CEO of Nuvation Energy, a provider of battery management systems and engineering services for large-scale energy storage systems.
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