The most efficient engines today, gasoline or diesel, are complex machines, with variable valve timing and lift, direct injection, turbocharging, and various flavors of exhaust gas recirculation (EGR). Engines need to be complex to balance the competing demands of fuel economy with emissions control.
Controlling the complex array of actuators requires precise, fast sensors that measure everything from mass airflow into the manifold to the engine-out level of NOx. With these data, the engine control unit (ECU) can advance or retard spark, actuate valves, and adjust turbocharger flow to get the engine humming at its optimum performance while keeping emissions within limits.
The challenges are complexity and cost. Not only do engineers have to manipulate a dozen or more independent parameters for individual actuators, but also adding more equipment is expensive. The cost of an individual sensor that measures NOx or turbocharger speed can run into a hundred dollars or more (per engine), according to Chris Greentree, General Manager Automotive Software for Honeywell.
Virtual sensors provide an alternative. They replace costly hardware sensors with software models that can cost very little per engine.
Virtual sensors and physics
As Greentree explains, for example, to create a virtual NOx sensor, they first calibrate a model using data from production and non-production sensors. These include in-cylinder pressure and temperature, air/fuel ratio, mass airflow through the cylinder, humidity, and backpressure sensors. Honeywell then simplifies the model to use only production sensors.
“All of these influence how much NOx will be produced, so if we model it correctly, we can accurately predict engine-out NOx and eliminate that sensor,” said Greentree.
The idea of virtual sensors is one that seems to be gaining traction. One way they are implemented is to use statistical response surfaces created from data measured in a lab or dyno, usually collected using Design of Experiments methods. These statistical models predict sensor responses without really knowing the physics. They are approximations only. Their utility is limited to how much data is collected.
Honeywell offers an alternative approach in its OnRAMP development tool. The tool allows engineers to embed a first-principles model in the ECU, using that to create virtual sensors.
“It is a physics-based model of the engine system,” explained Greentree, similar in function to familiar 0D or 1D system models engineers use to design engines. “The OnRAMP model is simpler than traditional system models and runs significantly faster than real time,” he explained. “We call it a ‘medium fidelity control oriented model,' allowing you to run your model native in the ECU.”
He went onto explain that, while it boasts a small computational footprint, it still incorporates basic physics such as the Ideal Gas Law, Bernoulli’s Equation, and Conservation of Mass & Energy. As he describes it, users still collect data using a DoE to calibrate the model, but the advantages of OnRAMP and its physics based control strategy is both a better control model and specific virtual sensors. Before loading it into the ECU, the OnRAMP development environment optimizes the model to create a smaller, faster running but still accurate model.
Honeywell’s OnRAMP enables five specific virtual sensors: engine-out NOx; selective catalytic reduction mid-bed ammonia; EGR flow rate; turbocharger speed; and fresh airflow rate. Another advantage besides very low per engine cost is that these virtual sensors respond faster and offer greater availability than real ones, according to Greentree.
“[For example], a physical NOx sensor takes some time after engine startup to reach a temperature sufficient for the sensor to function. Even after it has reached temperature, the operating principle of the sensor means that the signal will be slightly delayed, making it challenging to use in feedback control applications,” he explained.
In contrast, the OnRAMP model predicts the formation of NOx while it is leaving the engine. "This non-delayed information can be used on its own, or in conjunction with a physical sensor, to improve control," said Greentree.
“There are also less things to fail if you have fewer sensors, reducing possible warranty costs,” he said.