SIMBA 25.06 Preview is here! We're excited to announce the preview release of SIMBA 25.06, packed with major updates like JMAG Direct Coupling, new GUI features, enhanced SPICE support, and more. The final version will be released in a couple of weeks.
Electric motors and inverters are central to modern electrification, making their optimization crucial for efficiency, sustainability, and performance. Accurately evaluating losses in both components is a key challenge in motor drive design. This webinar demonstrates how the combined use of SIMBA and JMAG provides fast and accurate solutions to these challenges.
Our team will be in Detroit on June 4–5 for the JMAG Annual Conference. We'll be showcasing the new direct coupling between SIMBA and JMAG that is perfect for anyone looking to perform detailed simulations combining finite element analysis of motors and magnetics with advanced power electronics and control. If you're attending, please stop by and say hi!
Our partner Reliamotive Labs will showcase SIMBA at the ECCE Asia 2025 conference (11–14 May 2025, Booth A2).
We demonstrated the new features of SIMBA 25.03 , including accelerated Dual-Stage Electro-Thermal Simulation, in a webinar.
SIMBA 25.03 Release! One of the major features of SIMBA 25.03 is the new Dual-Stage Electro- Thermal Analysis, which speeds up electrothermal simulation by up to 20 times!
We are thrilled to announce that Reliamotive Labs is now our official distributor for SIMBA in India! Reliamotive Labs has a strong background in power electronics and a deep understanding of the industry's challenges and needs. With their expertise, we are confident that Indian engineers and researchers will benefit from top-notch local support and a seamless experience with SIMBA.
We released a new application example exploring the Dual Active Bridge (DAB) Converter: Loss Evaluation in a Dual Active Bridge (DAB) Converter
A comprehensive modeling guide for fuel cells has been published on the SIMBA resources platform: Fuel Cell Modeling: implementations and parameter extraction from experimental data.
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