Showing 1–4 of 6 results
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Battery Ageing – How Modeling is Used to Predict Battery Life
Battery modeling and simulation makes it possible to analyze multiple operating conditions and design parameters for batteries and other electrochemical systems and processes. By developing mathematical models you can begin to understand the interaction of electrochemical and chemical processes in the battery and how these processes affect the performance and life of the battery.
In this presentation, we will take a look at the benefits of modeling and simulation in the design, selection, and operation of a lithium-ion battery. We will especially take a look at how modeling can be used together with testing. These results provide manufacturers and application experts with the data to not only predict battery life but to analyze the implications of design parameters and operating conditions to better understand the limitation of the battery.
This webinar will focus on the following key topics:
• Benefits of modeling and simulations in the design, selection, and operation of a lithium-ion battery
• Implications of design parameters and operating conditions with respect to experimental observations of battery performance, aging, and battery safety
• How battery modeling can be used together with testingPresenter
Tom O’Hara – Global Business Manager, Intertek
Tom O’Hara is the global business manager / advisory services for Intertek’s energy storage programs. Aside from his consulting role, Tom supports U.S. and European marketing and sales efforts and APAC CTIA certification efforts. As a 30-year veteran of the battery technology field, Tom has worked in Energizer Battery’s R&D sector and consulted with several start-up battery companies. He is also the co-inventor of the world’s first successful mercury-free zinc air button cell and holds seven U.S. patents. He obtained both a B.S. and M.S. in chemistry from Wake Forest University in North Carolina.
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Battery Modeling – Electrical and Thermal Models
Energy storage systems are widely used in many applications where the integration of such systems requires a proper design and sizing. To ensure a reliable design and operation of these systems for the above-mentioned applications, a system management including battery management and thermal management is indispensable. Such kind of system-level supervisors are based on efficient modeling approaches that include electro-thermal models. Electro-thermal model includes different models with different precision, where the higher model accuracy requires a higher computational effort and cost. In this webinar, different modeling methods based on the latest findings are explained and reviewed.
This webinar will focus on the following key topics:
• Battery thermal solutions: existing systems and trends
• Electrical behavior modeling
• Thermal behavior modeling
• 1D thermal model
• 3D thermal modelPresenter
Aymen Souissi – Thermal Management Expert at Avesta Battery & Energy Engineering (ABEE)Aymen Souissi is a thermal Management Expert at Avesta Battery & Energy Engineering (ABEE), where he is working on different European projects on battery modeling and thermal management. Aymen is a mechanical engineer with a master’s degree in the fields of thermo-fluid dynamics and automotive technology from the University of Stuttgart in Germany. Prior to joining ABEE, he worked as thermal management engineer on different industrial projects at Bertrandt AG, where he was deeply involved in the development of battery systems.
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Low Data Machine Learning for Predicting Lithium-ion Battery Aging
Meeting the demand for reliable energy storage, this work presents a machine-learning model for precise cycle life prediction in lithium-ion batteries (LIB). It explores battery aging features, utilizes data-driven methods for health assessment, and applies machine learning to predict cycle life. To address data limitations, synthetic data generation is employed, enhancing prediction accuracy. The presentation concludes by demonstrating the practical deployment of the proposed ML model on a battery management system, showcasing its potential impact on power usage efficiency. Discussions cover crucial aspects such as battery aging, data-driven health measurement, and the model’s versatility in handling accidental effects during operation.
This webinar will focus on the following key topics:
• Unveiling Battery Aging: identifying key aging features
• Data-Driven Insights: machine learning for battery state of health assessment
• Cycle Life Precision: machine learning in Lithium-Ion battery predictions
• Addressing Data Gaps: synthetic data for enhanced prediction accuracy
• Real-World Impact: practical deployment of ML on battery management systemsPresenter
Meghana Sudarshan – Ph.D. Candidate at Purdue UniversityMeghana Sudarshan is currently pursuing a Ph.D. from the School of Aeronautics and Astronautics at Purdue University. Her research focuses on developing data-driven models agnostic battery management systems in UAVs and electric vehicles for predicting degradation of COTS (Commercial Off-The-Shelf) Li-ion Batteries as a function of operation parameters.
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Low Data Machine Learning for Accelerated Degradation Prediction of Lithium-ion Batteries
Meeting the demand for reliable energy storage, this work presents a machine-learning model for precise cycle life prediction in lithium-ion batteries (LIB). It explores battery aging features, utilizes data-driven methods for health assessment, and applies machine learning to predict cycle life. To address data limitations, synthetic data generation is employed, enhancing prediction accuracy. The presentation concludes by demonstrating the practical deployment of the proposed ML model for accelerated degradation prediction (for battery cell development and manufacturing feedback) and onboard deployment of low data AI on in-operation energy management. Discussions cover crucial aspects such as battery aging, data-driven health measurement, and the model’s versatility in handling accidental effects during operation.
This webinar will focus on the following key topics:
• Accelerated degradation based on low data AI for battery development for targeted applications
• Data-driven insights: machine learning for battery state of health assessment
• Prediction of rejection thresholds during cell manufacturing for application oriented cell development
• Prediction of targeted C-Rates for specific device applications
• Real-world impact: practical deployment of low data ML during real time device operationPresenter
Dr. Vikas Tomar – Professor at Purdue UniversityProf. Tomar’s interests lie in directed cell development using low data AI and vertical integration of targeted cells in c-rate and energy density specific devices. His research group has published extensively in topics related to developing data-driven models for agnostic BMS in UAVs and EVs, predicting degradation of COTS Li-ion batteries. The technology is now part of a startup, Primordis Inc., focused on launching vertically integrated Li-ion cells in autonomous systems within the framework of autonomous energy intelligence using an ASIC technology.
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