Electric vehicles (EVs) are pushing engineering boundaries, and battery performance is at the heart of this revolution. In our latest webinar, Ajitkumar Ananthu Jeyakumar, Senior Application Engineer at SimScale, and Dr. Ceyhun Sahin, Product Owner at Noesis Solutions, presented innovative solutions to optimize EV batteries’ power and thermal management. Here are the top five highlights from this insightful session.
On-Demand Webinar
If these highlights caught your interest, there are many more to see. Watch the on-demand Engineering Leaders Series webinar from SimScale on Preparing Your Teams for AI-Powered Computational Design by clicking the link below.
1. Building an Optimized Battery Thermal Management Workflow
Ajit presented an efficient workflow combining SimScale’s cloud-native simulation capabilities with Noesis’ Envision data analysis tool. This integration provides engineers with rapid predictions on temperature and heat transfer, which is crucial for EV battery packs, using high-fidelity simulations paired with AI-driven insights. The workflow allows engineers to simulate scenarios and predict battery thermal behavior in seconds, enabling fast design iterations.
2. Leveraging Cloud-Native Simulation for Scalability and Accessibility
SimScale’s cloud-based platform eliminates the need for high-cost local hardware, allowing simulations to run in a browser with real-time collaboration. This enables design engineers to conduct complex thermal and flow simulations from anywhere. Ajit emphasized how the platform’s scalability supports multiple design iterations simultaneously, reducing the time from hours to minutes while ensuring consistent results.
3. High-Fidelity Data for Accurate Predictions
Noesis’ Envision model provides an intelligent prediction loop based on SimScale’s simulation data. By utilizing a rich data set—such as temperature readings and heat transfer coefficients from charging cycles—engineers can make real-time predictions for battery cooling requirements. Dr. Sahin highlighted how integrating these data points in a feedback loop allows control systems to maintain optimal battery temperatures, increasing battery life and safety.
4. Optimized Data Management and AI Prediction
Ajit discussed how SimScale’s platform automatically organizes simulation data, creating a structured database for AI-powered predictions. This data-driven approach reduces the time needed for engineers to manually collect and prepare simulation data, allowing more time to innovate. Envision further strengthens this workflow by delivering predictions that enhance decision-making during both early-stage design and final validation.
5. Enabling Real-Time Control with Surrogate Models
Dr. Sahin demonstrated how Envision’s surrogate model could predict EV battery pack temperatures and heat transfer coefficients in real time. Unlike traditional analytical approaches, Envision’s model feeds instantaneous data directly into control systems, allowing real-time adjustments that enhance battery performance and energy efficiency. This predictive capability, he explained, is ideal for managing variables like state of charge and ambient conditions during an EV’s drive cycle.
Accelerating Innovation in EV Battery Design
The SimScale and Noesis workflow is transforming how engineers tackle thermal challenges in EV batteries. By integrating high-fidelity simulations and real-time AI predictions, EV manufacturers can now achieve better battery efficiency, extended lifespan, and faster design cycles. If you missed the live session, you can watch the on-demand recording and explore how cloud-native tools like SimScale, combined with powerful predictive models from Noesis, can drive innovation in your workflows.
