Generate a Deep Learning SI Engine Model

If you have the Deep Learning Toolbox™ and Statistics and Machine Learning Toolbox™, you can generate a dynamic deep learning spark-ignition (SI) engine model to use for powertrain control, diagnostic, and estimator algorithm design. For example, fit a deep learning model to measured engine-out transient emissions data and use it for aftertreatment control and diagnostic algorithm development. The deep learning SI engine models the dynamic engine behavior from measured laboratory data or a high-fidelity engine model.

To train the deep learning SI engine model, Powertrain Blockset™ uses this SI engine data.

Input Data	Output Data
Engine speed Commanded torque	Brake torque Intake manifold gas pressure Intake manifold gas temperature Fuel flow Intake air mass flow Exhaust gas temperature at exhaust manifold inlet Turbocharger speed Engine out (EO) hydrocarbon (HC) emission mass flow EO carbon monoxide (CO) emission mass flow EO nitric oxide and nitrogen dioxide emissions (NOx) emission mass flow EO carbon dioxide (CO2) emission mass flow

To generate the deep learning engine model, follow these steps.

1. If it is not already opened, open the reference application.

   autoblkSIDynamometerStart 

2. Double-click Generate Deep Learning Engine Model. Generating the model can take several hours.

   By default, to train the deep learning engine model, the reference application generates design of experiment (DoE) response data from the SI Core Engine block. Alternatively, you can use engine data generated by Powertrain Blockset from Gamma Technologies LLC engine models or other high-fidelity engine models.

   • View the training progress window to see the iteration or stop the training.

     

   • As the training runs, Powertrain Blockset logs this data in the base workspace.

     • EngineInputs — m-by-2 array of engine inputs

     • EngineOutputs — m-by-11 array of engine outputs

     Powertrain Blockset uses half the data to train the model and half to test the model.

3. After you generate the deep learning SI model, view the results.

   • For each engine output, a plot displays the SI engine deep learning model (Pred) and the test data (Test). For example, this plot shows the comparison for dynamic engine-out CO emissions mass flow.

     

   • The Simulation Data Inspector displays the SI engine deep learning model speed, torque commands, fuel mass flow rate, and shaft speed.

     

4. You can use the deep learning SI model, SiDLEngine, as an engine plant model variant in the conventional vehicle and hybrid electric vehicle (HEV) reference applications. For example, in the conventional vehicle reference application, on the Modeling tab, in the Design section, open the Variant Manager. Navigate to Passenger Car > Engine. Right-click to set SiDLEngine as the active choice.

   

5. To fit your own deep learning SI engine model or adjust the deep learning training settings, use the FitSiEngineLSTM.m script in the reference application project folder.