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About Oorja

Our Founders are passionate about simplifying Product Design, and believe that doing so will automatically solve aspects of Energy Crisis, Sustainability, Climate Change and the Future of the world. They possess deep expertise in simulation and mathematical modelling and have a proven track record of building successful products and businesses.

Upcoming Webinars

Understanding Thermal Runaway and Preventing Battery Accidents 

  • 3rd January 2023,
  • 11 am CEST (2:30 pm IST) and 10 am PDT
Understanding thermal runway propagation and introducing tweaks in pack design can help ensure a thermal runaway event doesn’t lead to an explosion at the pack level. The biggest challenge with TR prediction is that it is a highly complex and fast physical process which is very difficult and computationally intensive to capture using traditional physics based approaches. Join this 20 min. webinar to understand design best practices which can help minimize the risk of explosions and how oorja’s hybrid approach can accelerate safe design of batteries.
Register Now

Battery Pack Design and Integration Best Practices

  • 4th January 2023,
  • 11 am CEST (2:30 pm IST) and 10 am PDT
Pack casing material and thickness, cell spacing, thermal pads, cooling strategy and pack placement in the vehicle. These are some of the many design choices which can have a significant impact on the temperature of the cells in a pack, which in turn affect safety, performance and life of the vehicle. At the beginning of the design process, there is no real life data available. Traditional physics based approaches require battery parameters which are difficult to obtain. Join this 20 min.webinar to understand how hybrid analysis can give you fast and accurate predictions to optimize pack design.
Register Now

Degradation Prediction in Li Ion
Batteries

  • 5th January 2023, 
  • 11 am CEST (2:30 pm IST) and 10 am PDT
Degradation prediction in Li ion batteries is a challenging and highly nonlinear problem. Traditional physics based approaches and tools need several design, electrochemical and degradation parameters and long computation times which make this approach expensive. Pure machine learning requires huge amounts of data and using real life drive cycles is still not possible. Join this 20 min webinar to learn how a hybrid approach can be used to perform accurate capacity fade modeling at a fraction of the time and cost of traditional methods.
Register Now

Understanding thermal runway propagation and introducing tweaks in pack design can help ensure a thermal runaway event doesn't lead to an explosion at the pack level. The biggest challenge with TR prediction is that it is a highly complex and fast physical process which is very difficult and computationally intensive to capture using traditional physics based approaches. Join this 20 min. webinar to understand design best practices which can help minimize the risk of explosions and how oorja's hybrid approach can accelerate safe design of batteries.

Register Now

Pack casing material and thickness, cell spacing, thermal pads, cooling strategy and pack placement in the vehicle. These are some of the many design choices which can have a significant impact on the temperature of the cells in a pack, which in turn affect safety, performance and life of the vehicle. At the beginning of the design process, there is no real life data available. Traditional physics based approaches require battery parameters which are difficult to obtain. Join this 20 min.webinar to understand how hybrid analysis can give you fast and accurate predictions to optimize pack design.

Register Now

Degradation prediction in Li ion batteries is a challenging and highly nonlinear problem. Traditional physics based approaches and tools need several design, electrochemical and degradation parameters and long computation times which make this approach expensive. Pure machine learning requires huge amounts of data and using real life drive cycles is still not possible. Join this 20 min webinar to learn how a hybrid approach can be used to perform accurate capacity fade modeling at a fraction of the time and cost of traditional methods.

Register Now

Upcoming Webinars

Understand and mitigate thermal runaway

  • Date 31st Jan 2023
  • Time 3 PM IST
  • Duration is 30 mins
Request Recording

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. Join this 30 min Webinar to check out best practices that can help minimize the risk of explosions and accelerate the design of safer battery packs.

Predict battery degradation for real world conditions

  • Date 8th Feb 2023
  • Time 10 PM IST
  • Duration is 45 mins
Request Recording

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. Jointhis45 min webinar to learn how to accurately predict capacity fade at a fraction of the time and cost of traditional methods.

Understanding Thermal Runaway and Preventing Battery Accidents 

  • 3rd January, 2023
  • 11.00AM CEST | 2:30PM IST | 10.00AM PDT
Register Now
Understanding thermal runway propagation and introducing tweaks in pack design can help ensure a thermal runaway event doesn’t lead to an explosion at the pack level. The biggest challenge with TR prediction is that it is a highly complex and fast physical process which is very difficult and computationally intensive to capture using traditional physics based approaches. Join this 20 min. webinar to understand design best practices which can help minimize the risk of explosions and how oorja’s hybrid approach can accelerate safe design of batteries.

Battery Pack Design and Integration
Best Practices

  • 4th January 2023
  • 11.00AM CEST | 2:30PM IST | 10.00AM PDT
Pack casing material and thickness, cell spacing, thermal pads, cooling strategy and pack placement in the vehicle. These are some of the many design choices which can have a significant impact on the temperature of the cells in a pack, which in turn affect safety, performance and life of the vehicle. At the beginning of the design process, there is no real life data available. Traditional physics based approaches require battery parameters which are difficult to obtain. Join this 20 min.webinar to understand how hybrid analysis can give you fast and accurate predictions to optimize pack design.

Degradation Prediction in Li Ion
Batteries

  • 5th January 2023,
  • 11.00AM CEST | 2:30PM IST | 10.00AM PDT
Degradation prediction in Li ion batteries is a challenging and highly nonlinear problem. Traditional physics based approaches and tools need several design, electrochemical and degradation parameters and long computation times which make this approach expensive. Pure machine learning requires huge amounts of data and using real life drive cycles is still not possible. Join this 20 min webinar to learn how a hybrid approach can be used to perform accurate capacity fade modeling at a fraction of the time and cost of traditional methods.

Understanding thermal runway propagation and introducing tweaks in pack design can help ensure a thermal runaway event doesn't lead to an explosion at the pack level. The biggest challenge with TR prediction is that it is a highly complex and fast physical process which is very difficult and computationally intensive to capture using traditional physics based approaches. Join this 20 min. webinar to understand design best practices which can help minimize the risk of explosions and how oorja's hybrid approach can accelerate safe design of batteries.

Register Now

Pack casing material and thickness, cell spacing, thermal pads, cooling strategy and pack placement in the vehicle. These are some of the many design choices which can have a significant impact on the temperature of the cells in a pack, which in turn affect safety, performance and life of the vehicle. At the beginning of the design process, there is no real life data available. Traditional physics based approaches require battery parameters which are difficult to obtain. Join this 20 min.webinar to understand how hybrid analysis can give you fast and accurate predictions to optimize pack design.

Register Now

Degradation prediction in Li ion batteries is a challenging and highly nonlinear problem. Traditional physics based approaches and tools need several design, electrochemical and degradation parameters and long computation times which make this approach expensive. Pure machine learning requires huge amounts of data and using real life drive cycles is still not possible. Join this 20 min webinar to learn how a hybrid approach can be used to perform accurate capacity fade modeling at a fraction of the time and cost of traditional methods.

Register Now

Our Speakers

Founder and CEO

Vineet Dravid

Co-Founder and COO

Prajakta Sabnis

Co-Founder and CTO

Prashant Srivastava

Register Now

Understanding thermal runway propagation and introducing tweaks in pack design can help ensure a thermal runaway event doesn't lead to an explosion at the pack level. The biggest challenge with TR prediction is that it is a highly complex and fast physical process which is very difficult and computationally intensive to capture using traditional physics based approaches. Join this 20 min. webinar to understand design best practices which can help minimize the risk of explosions and how oorja's hybrid approach can accelerate safe design of batteries.

Pack casing material and thickness, cell spacing, thermal pads, cooling strategy and pack placement in the vehicle. These are some of the many design choices which can have a significant impact on the temperature of the cells in a pack, which in turn affect safety, performance and life of the vehicle. At the beginning of the design process, there is no real life data available. Traditional physics based approaches require battery parameters which are difficult to obtain. Join this 20 min.webinar to understand how hybrid analysis can give you fast and accurate predictions to optimize pack design.

Degradation prediction in Li ion batteries is a challenging and highly nonlinear problem. Traditional physics based approaches and tools need several design, electrochemical and degradation parameters and long computation times which make this approach expensive. Pure machine learning requires huge amounts of data and using real life drive cycles is still not possible. Join this 20 min webinar to learn how a hybrid approach can be used to perform accurate capacity fade modeling at a fraction of the time and cost of traditional methods.