Showing 37–40 of 82 results
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Battery Performance Testing – Pay Attention to the Details!
The success of an energy storage project for stationary applications depends, for a large part, on how well the battery performs over time. Since there are little case studies showing real-world lifetime performance comparisons for various technologies the battery selection is primarily based on the performance data in a laboratory environment. Knowledge of the test conditions under which the data is obtained is critical to determine the suitability of the battery technology for the intended application. The influence of test conditions on the battery performance, and consequently on the battery selection process is discussed. The presentation presents real-world examples to emphasize on how subtle and often unspecified test conditions can affect the performance and lead to an un-optimized battery solution.
This webinar will focus on the following key topics:
• Battery selection process for stationary energy storage
• Primary test conditions commonly presented
• But…what’s hidden beneath the surface?
• Examples of some subtle test conditions, if ignored, may lead to an un-optimized battery solution
• How a good understanding of the product performance under different conditions can empower our customers with operating optionsPresenter
Ashok Saraswat – Director, Energy Storage Research at NEC Energy SolutionsAshok Saraswat is working as Director, Energy Storage Research at NEC Energy Solutions located in the Boston area in the United States. After obtaining Ph.D. from Indian Institute of Technology, Delhi, India, Ashok Saraswat began his career in battery systems with a focus on Li-ion batteries. He has been involved in R&D, product and process development, assembly processes, and battery applications in various market segments including consumer electronics, aerospace and stationary storage.
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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 storagePresenter
Michael Worry – CEO at Nuvation EnergyMichael 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 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 anomaliesPresenter
Michael Worry – CEO at Nuvation EnergyMichael 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 Analytics Tutorial Course 1/3: Battery Analytics and the Role of the BMS
This one-hour course will explore how various energy storage industry experts define the term “battery analytics.” It will also examine how the battery management system (BMS) is used to control the battery and provide real-time performance reporting, the lowest level of battery analytics.
This webinar will focus on the following key topics:
• The different types of battery analytics
• How a BMS works and why it is the most basic component of any battery analytics platform
• Real-time performance algorithms as the lowest level of analyticsPresenter
Michael Worry – CEO at Nuvation EnergyMichael 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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