AI with Model-Based Design: Virtual Sensor Modeling

Virtual Sensors Reshaping Automotive

The automotive industry faces mounting pressure from regulatory deadlines, such as the European Union's 2035 ban on new internal combustion engine sales, which accelerates the shift to electric vehicles. Virtual sensor modeling, combining AI with model-based design, emerges as a key enabler for efficient battery management systems. These software-based estimators use machine learning to infer unmeasurable states like battery state of charge from available data, avoiding the need for additional physical hardware that adds weight, cost, and failure points.

In real-world applications, companies like ZF have deployed AI-driven temperature prediction models that enhance electric powertrain efficiency by unlocking 6% more peak power. This impacts manufacturers, suppliers, and consumers: automakers cut development cycles, suppliers streamline sensor integration, and drivers benefit from extended range and reliability. However, the stakes are high— inaccurate state estimation can lead to overcharging, thermal runaway, or sudden power loss, as seen in the 2024 Chevrolet Bolt recalls affecting 140,000 vehicles and costing General Motors $1.8 billion.

Non-obvious tensions arise in the trade-offs between AI and traditional methods like Kalman filters. AI models offer faster inference and adaptability to nonlinear systems but suffer from data-hungry training requirements and opacity, making fault diagnosis harder in embedded environments. In aerospace and manufacturing, similar integrations reduce prototyping risks, yet supply chain disruptions from 2025 chip shortages have amplified the appeal of virtual alternatives, potentially saving firms 20-30% in hardware expenses. Overlooking these could exacerbate competitive gaps, with leaders like Tesla leveraging digital twins for predictive maintenance while laggards face higher warranty claims.