Rimac Technology is at the forefront of designing and building advanced electric vehicle components for global OEMs, including battery systems, e-axles, control electronics, and software. In this talk, Ivan Krajinović will delve into how an integrated modelling approach, from initial requirements to comprehensive feedback loops from testing, with models in the middle which drives the development of next-generation battery packs at Rimac Technology.
The presentation will highlight the team's position as a vertically integrated, cross-functional department of Rimac Technology, encompassing both modelling and testing capabilities under one roof. Additionally, it will explore the close integration with other development aspects, such as Verification and Validation (V&V) and the manufacturing of high-volume products. While the primary focus will be on next-generation battery packs, the discussion will also touch upon the development of powertrain components.
AI has been a constant presence in the headlines for several years now, with daily articles detailing how many people will lose their jobs and which professions are at risk from AI development. What exactly are AI methods, what opportunities do they offer, and how can they support the work of engineers and scientists?
This lecture will attempt to answer these questions, discussing it from the theoretical perspective to the practical guidance. AI methods developed over 70 years are present in many tools used daily, but the widespread availability of LLM-based models (ChatGPT, DeepSeek, LLAMA, Gemini) has both fascinated and frightened humanity. The limitations of AI methods and the ideas for their application in engineering practice will be presented during lecture. The role of scientists and engineers, in collaboration with AI methods, creates new areas that simplify the design and computational processes. It might seem that the use of AI methods would reduce the demand for engineers, while it actually allows for the creation of better products from an companies perspective. It requires engineers to familiarize themselves with new methods, ways of using and verifying AI-based results, and even the employment of AI specialists.
In the presentation we introduce tools related to global optimization methods, machine learning, neural networks, large language models, and data-driven modeling. Then we discuss recent papers on the application of AI in the design and monitoring of structures and materials. Using AI will enable a similar shift in tools as replacing a slide rule (every engineer in 70s last century used one) with a calculator. The most important aspect of this shift is the fundamental engineering knowledge that allows for the assessment of the correctness of results regardless of the tool used. The ability to spot AI hallucinations and errors is an engineer most valuable skill nowadays.
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