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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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Avoid Battery Explosions and Fires – With Right Data and Better Designs
Modern Li Ion batteries contain hazardous chemicals and heat up during use – this combination always has the potential to cause fires and explosions. This presentation will focus on improving the understanding of how such incidents occur, what can be done to avoid them and how the risk can be minimized during early stage design.
The solution lies in knowledge of the heat generation rate during normal use, and information about safe boundaries such as temperature, discharge rate & overcharge in realistic situations that represent actual conditions of use. Data from commercial batteries of different types, including videos of batteries undergoing thermal runaway, will be used to illustrate these points.
A relatively new technique will also be discussed with data, which allows total heat output during discharge to be measured on-line and this can be used both for design and battery modelling. Examples of the data will be provided.
This webinar will focus on the following key topics:
• Why battery fires and explosions occur
• How to design safer batteries through understanding of heat generation
• Video evidence of batteries under explosive conditions
• How better thermal management systems can be designed – based on heat measurement from isothermal calorimetry
• Laboratory instruments suitable for testing and data generation
Presenter
Dr. Jasbir Singh – Managing Director at Hazard Evaluation Laboratory
Jasbir is a chemical engineer specializing in thermal hazards and calorimetry, traditionally for the chemical industry but now increasingly involved in battery safety, especially Li-ion EV and related types.
A graduate of Imperial College (London), where he undertook PhD into combustion and explosions, his experience includes many years in process design for the chemical and petrochemical industries. He is currently developing test methods and instruments for use in design of battery thermal management systems.
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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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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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