Develop BEV Faster. Let’s Release.

In the race for BEVs and SDVs, every delay costs momentum, market share, and your innovation edge. But traditional testing can’t keep up: Fragmented processes, manual prep, and stalled releases slow everything down. As software requirements gain increasing prominence, the process of translating them into executable test cases has emerged as a significant bottleneck. Functional testing has now taken precedence over traditional calibration methods, resulting in the generation of thousands of test cases per release. Yet manual implementation is error-prone and can eat up to 60 % of effort in a typical 18-month project. It’s inefficient and outdated. With seamless software integration across all testbeds, you automate what matters most: Test cases are read, triggered, executed, and reported back to your central lifecycle management tool. No redundancies. No rework. Just full traceability and scalable speed. From firefighting to future-shaping – that’s your path. 

There’s never enough time. With limited calibration slots and tight deadlines, pressure to optimize e-Drive performance intensifies. Teams are expected to calibrate entire maps in days – no room for error. But conventional processes strain resources and jeopardize milestones. Now imagine a calibration strategy that thinks ahead. A patented, Active Design-Of-Experiments (DoE) approach selects only the most valuable data points with the push of a button – distributing variation points smartly, identifying critical regions, adding targeted repetitions, and skipping zones that are not of interest. The AI powered algorithm learns from each measurement point, adapts on the fly, and determines the next best step. The result? Up to 50 % less testbed time, lower manual effort, and efficient optimization of even complex systems. Reliable, reusable results – with fewer cycles, less overhead, and full confidence. When time is tight, your process should empower progress – not block it. 

Testbed inefficiency starts long before: Repeated runs, late anomaly detection, damaged UUTs or equipment. When non-plausible behavior slips through, costs explode and schedules flip. Standard limit monitoring can’t keep up. The smarter way? Self-learning algorithms generate runtime models directly from measurement data – creating a digital twin that turns every test into a predictive feedback loop. Anomalies are identified early, invalid tests are stopped instantly, and downtime is minimized. No expert skills for model creation. No complex setup. Just built-in machine learning, continuously maximizing efficiency. And at lab level? Complexity rises: Stricter regulations, tougher certifications, disconnected systems, missing traceability, and rising admin overhead. Our seamless toolchain restores order: Template processes, automated resource scheduling, and full requirement traceability (Chain of Custody). Overall Equipment Effectiveness (OEE) increases by up to 35 %. Less chaos. More control.