TACKLING CHALLENGES IN BATTERY DESIGN
Events
Stay connected with the pulse of innovation at oorja Events. Join us for captivating conferences, workshops, and webinars featuring industry leaders and thought-provoking discussions on battery design, engineering solutions, and the future of hybrid simulations.
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Upcoming Events
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Workshop
Accelerated degradation prediction of Li-ion batteries using hybrid simulations (Physics + ML)
Reliable degradation estimation is crucial for Li-ion battery design and maintenance. Inaccurate predictions during the design phase can lead to misjudgments of on-road performance and expensive warranty claims. Conventional methods to predict degradation are complex, time-consuming and incomplete. These methods require extensive tedious cycler experiments and lengthy computations. However, all such efforts do not accurately estimate battery degradation for real-world conditions.
oorja uses an innovative hybrid approach to quickly and reliably predict degradation in Li-ion batteries. This approach combines the principles of physics with limited cycler data to reliably predict cell and pack level degradation across various C rates, operating temperatures, and real-world drive cycles.
Key Highlights
Predict cell and pack level degradation on the drive cycles
Provide reliable capacity fade warranty on the battery pack
Reduce dependence on tedious battery cycler experiments
Upto 8X faster predictions for > 95% accuracy using Hybrid Approach
Webinar Agenda
Introduction to oorja (5 Minutes)
Demo of degradation prediction+ Live Poll ( 30 Minutes)
Question and Answers (20 Minutes)
Session Closure + Live Poll ( 5 Minutes)
60 min / Webinar Duration
This webinar provides valuable insights into oorja’s hybrid approach to quickly and reliably predict li-ion battery degradation and its potential benefits for battery design and maintenance.The webinar is most suitable for battery design leaders.
Vineet Dravid
Vineet founded oorja to simplify design for complex engineering problems. Armed with a Ph.D. in Mechanical Engineering from Purdue University, Vineet has worked on cutting-edge simulation and mathematical modelling technologies for two decades.
8th December,2023
03:30 PM JSTRECORDINGS
Previous Events
Engage with oorja via online and onsite events
Workshop
25 Apr. 2022
Understand and mitigate thermal runaway
Quickly simulating thermal runaway (TR) in various battery packs and mitigating it remains a big challenge for the EV industry. Using oorja’s patent-pending battery pack design technology, one can accurately simulate TR, its propagation speed, and design mitigation strategies to prevent battery fires.
In this webinar, you will learn
- The different ways to simulate battery packs for TR at the design stage
- How oorja’s hybrid (physics + ML) battery pack design approach can help users accurately simulate the spread of TR across the pack and immediately predict self-heating at the cell level to achieve AIS 156 & 038 compliance the first time around.
- Insights on battery pack development using oorja and NexiGo technologies.
Join this 45 min Webinar to check out best practices that can help minimize the risk of explosions and accelerate the design of safer battery packs.
Vineet Dravid
Vineet founded oorja to simplify design for complex engineering problems. Armed with a Ph.D. in Mechanical Engineering from Purdue University, Vineet has worked on cutting-edge simulation and mathematical modelling technologies for two decades.
Naga Penmesta
Naga Penmesta is the Co-Founder & Chief Technology Officer of NexiGo Energy. With a vision to increase global adoption of sustainable energy, Naga is currently working on the design and development of highly efficient battery packs using innovative packing methods.
Recording
8 Feb. 2023
Predict battery degradation for real world conditions
Degradation prediction in Li-ion batteries is a challenging and highly nonlinear problem. Traditional physics-based approaches and tools need several designs, electrochemical, degradation parameters, and long computation times, which make it a tedious and expensive approach.
Degradation prediction in Li-ion batteries is a challenging and highly nonlinear problem. Traditional physics-based approaches and tools need several designs, electrochemical, degradation parameters, and long computation times, which make it a tedious and expensive approach.
To solve the challenge quickly and accurately, oorja has developed a Hybrid approach (Physics + ML) to predict cell and pack level degradation at various C rates, operating temperatures, and real-world drive cycles.
Key Learning Objectives
Predict cell and pack level degradation on the drive cycles
Provide reliable capacity fade warranty on the battery pack
Reduce dependence on tedious cycler experiments
Upto 8X faster predictions for > 95% accuracy
Vineet Dravid
Vineet founded oorja to simplify design for complex engineering problems. Armed with a Ph.D. in Mechanical Engineering from Purdue University, Vineet has worked on cutting-edge simulation and mathematical modelling technologies for two decades.