Advancing Electric Truck Simulation with Wolfram Tools: Wolfram Consulting Group

Heavy-duty electric trucks don’t have the luxury of wasted heat. Every watt must either be stored or redirected, which makes thermal control one of the hardest parts of vehicle design. For a global manufacturer of commercial freight vehicles developing its next electric platform, simulation speed became the barrier. Each test cycle ran for hours, slowing every decision about how to warm the cabin and protect the batteries.

Wolfram Consulting replaced that process with a unified computational model and a neural network able to predict and correct thermal behavior in real time. What once ran overnight now runs in seconds.

The Challenge

Designing the thermal system of a heavy-duty electric truck means balancing efficiency with durability and driver comfort. The job demands precise control of temperature across batteries, motors and cabin spaces, while keeping energy use as low as possible.

At the start of development, separate engineering groups produced their own heat data for a four-hour reference route between company facilities. The battery team modeled pack heating, the driveline group estimated motor losses, and the cabin group focused on air-handling loads. The thermal group then merged these datasets by hand in spreadsheets to approximate whole-system behavior.

Detailed GT-Suite runs that took hours were reproduced in tens of seconds in System Modeler. For closed-loop control experiments that adjust valves/pumps on the fly, the team identified a real-time simulation constraint that required running at 1×, which is why those validation runs were executed overnight.

The Solution

Wolfram Consulting delivered a two-step solution: first, a high-fidelity System Modeler framework that replaced manual thermal analysis, and second, a neural network trained on those simulations to predict and optimize real-time performance.

The team replaced static spreadsheets with a computable model of the truck’s thermal circuit, similar to the one shown here. Engineers built a two-tier System Modeler library for early exploration and for detailed dynamics. The first tier used simplified components with basic mass-flow inputs, so engineers could visualize and test circuit concepts before detailed data existed. The second tier added hydraulic flow and pressure behavior, including pipe diameters, pump curves and coolant properties that vary with temperature and pressure.

Image source: Fig. 10 from https://www.mdpi.com/1996-1073/18/3/673.

These models produced results consistent with GT-Suite in a fraction of the time. A full run of the same route that once took three to four hours could be completed in about 35 seconds. The library enabled rapid iteration and reuse across projects, giving the thermal group a practical foundation that matched operating conditions.

Once validated, the team generated steady-state simulations spanning valve positions and pump speeds under varied operating conditions. The resulting data trained a neural network that learned how each configuration affected the battery systems and the cabin. It could predict future temperatures and, more importantly, compute the control settings needed to hit a target.

This invertible approach made the network an adaptive control layer. During a full cycle, the system adjusted parameters in real time to avoid under- or over-temperature events. When battery temperature fell below range, the controller identified the precise valve and pump changes needed to recover. It could also ease compressor load when limits were stable.

Together, the simulation framework and control layer created a self-correcting thermal system that improved speed and accuracy.

The Results

The framework delivered immediate gains. Engineers moved from multi-hour runs to seconds-scale iteration, testing new circuit layouts and control strategies that had been impossible to study before.

The adaptive control layer turned static calibration into a data-driven process. Instead of tuning modes by hand, the system maintained target ranges automatically during validation, adjusting pump speeds and valve positions to keep all components within their limits.

The libraries are now used beyond the initial program. The battery group is modeling internal heat generation, and the controls team has integrated the thermal model through FMI for co-simulation. Working from the same computable foundation, teams report faster decisions and more consistent results.

Building a Scalable Modeling Standard

By replacing a slow, siloed workflow with a unified computational framework, Wolfram Consulting turned thermal modeling from a bottleneck into a continuous design process. What began as a single EV program has become a shared modeling standard across engineering groups and now extends to electrical and control work.

When you need to speed up simulations or optimize system performance, Wolfram Consulting can help you build models that deliver faster, more reliable results.