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DTSTART;TZID=Asia/Kolkata:20250123T200000
DTEND;TZID=Asia/Kolkata:20250123T210000
DTSTAMP:20260501T054828
CREATED:20250409T110356Z
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UID:12200-1737662400-1737666000@oorja.energy
SUMMARY:Identifying the Goldilocks Zone: Perfecting Parameters for Li-ion Battery Modeling
DESCRIPTION:« All Events\n 				\n				\n				\n				\n					\n	Identifying the Goldilocks Zone: Perfecting Parameters for Li-ion Battery Modeling				\n				\n				\n				\n					\n	\n	Date & Time:	\n			January 23\, 2025\n\n	\n\n	  @  \n\n\n8:00 pm\n\n		\n\n\n\n	\n	  -  \n\n9:00 pm\n\n\nIST\n	\n				\n				\n				\n				\n					\n	\n				\n				\n				\n				\n									Beginning of Life (BoL) parameter calibration is the cornerstone of reliable digital twins for lithium-ion batteries. Yet\, identifying the “Goldilocks Zone” of parameters—where predictions are both accurate and reliable—remains a formidable challenge. Parameters must be fine-tuned across the battery’s entire lifecycle to ensure dependable safety assessments and performance predictions. However\, the process of extracting and optimizing these parameters is far from straightforward. Having an incorrect parameter set can lead to significant modeling errors\, jeopardizing the reliability of simulations. That’s where oorja’s cutting-edge Battery Application Suite comes in. Leveraging a hybrid approach\, our software tackles this complexity head-on by extracting insights from HPPC data to simulate real-world battery behavior under diverse operating conditions. Join us in this session\, designed for battery researchers\, engineers\, and industry professionals\, where we explore how oorja’s robust BoL multi-parameter optimization is simplifying + expediting battery modeling\, empowering users to achieve accurate real-world results. Key Highlights: 1. Tackling BoL Parameter Estimation Challenges (Kinetic/ Transport): Learn how obtaining the right set of BoL parameters is pivotal for effective physics-based modeling. 2. How oorja has Simplified + Expedited the Process: Learn the ease of handling experimental data with the oorja interface\, reduced time for optimization\, and our optimization techniques. 3. Practical Applications and Use Cases: Understand how parameter optimization is central to advancements in lab testing\, cycle life prediction\, and degradation modeling—paving the way for robust simulation models. Why Attend? Learn how the oorja Battery Application Suite simplifies parameter extraction and experimental data handling\, reduces optimization time\, and ensures robust model calibration. 								\n				\n				\n				\n					\n	\n	Date & Time:	\n			January 23\, 2025\n\n	\n\n	  @  \n\n\n8:00 pm\n\n		\n\n\n\n	\n	  -  \n\n9:00 pm\n\n\nIST\n	\n				\n				\n				\n				\n					\n			\n		Mode	\n				\n		Online (Zoho)			\n			\n				\n				\n					\n				\n		\n					\n				\n				\n									\n					\n						\n									View Webinar Recording\n					\n					\n				\n								\n				\n				\n				\n							\n			\n		\n						\n				\n					\n				\n		\n					\n				\n				\n					Meet The Speakers				\n				\n					\n				\n		\n					\n		\n				\n				\n																														\n				\n				\n				\n					\n						\n		Prashant Kumar Srivastava	\n			CTO\, Oorja\n	\n				\n				\n				\n				\n									Prashant has fifteen years of experience in the Computer-Aided Engineering (CAE) field and holds a PhD in Aerospace and Astronautical Engineering from the Indian Institute of Science (IISc).  								\n				\n				\n		\n				\n				\n																														\n				\n				\n				\n					\n						\n		Dr. Raghunathan K	\n			Scientific Advisors\n	\n				\n				\n				\n				\n									Dr Raghunathan is a chemical engineer\, with over 15 years’ experience in the chemical industry followed by 17 years at General Motors R&D in the battery cell area. He retired as a GM Technical Fellow in 2022 and returned to India. In India\, he is a Professor of Practice at IIT Madras.								\n				\n				\n		\n				\n					\n				\n		\n					\n				\n				\n					\n	\n		\n\n	\n	Add to calendar	\n		\n	\n\n		\n			\n									\n	Google Calendar\n\n									\n	iCalendar\n\n									\n	Outlook 365\n\n									\n	Outlook Live\n\n							\n		\n\n		\n	\n\n				\n				\n				\n				\n					\n	\n		\n\n				Physics Informed ML to Reduce Accelerated Life Testing for Li-Ion Batteries by 75%	\n\n\n		\n	\n		Deciphering Fast Charging Dynamics: Achieving the Optimal Charging Strategy for EVs
URL:https://oorja.energy/event/identifying-the-goldilocks-zone-perfecting-parameters-for-li-ion-battery-modeling/
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