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Two substantially different methods have become popular in building fast computing catalyst models: physico-chemical approaches focusing on dimensionality reduction and machine learning approaches. Data driven models are known to be very fast computing and to achieve high accuracy but they can lack of extrapolation capability. Physico-chemical models are usually slower and less accurate but superior regarding robustness. The robustness can even be reinforced by implementing an extended Kalman filter, which enables the model to adapt its states based on actual sensor values, even if the sensors are drifting. The present study proposes a combination of both approaches into one hybrid model, keeping the robustness of the physico-chemical model in edge cases while also achieving the accuracy of the data based model in well-known regimes. The output of the hybrid model is controlled by an autoencoder, utilizing methods well known from the field of anomaly detection.

Because of the direct connection between the compound power split hybrid transmission (CPSHT) and the engine in hybrid electric vehicle (HEV), engine ripple torque (ERT) can lead to obvious jerks when the mode transition from electric driving mode to hybrid driving mode occurs. In order to enhance the riding comfort, two additional wet clutches are mounted in this novel CPSHT and the relevant coordinated control strategy is developed. Firstly, after the description of the mechanical and hydraulic parts of the novel CPSHT, the dynamic plant model including driveline model, engine ripple torque and clutch torque is deduced. Secondly, the mode transition process in the original and current designs are compared and analyzed with the equivalent level diagram and the encountered problems are stated.

With the continuous growth in the emission requirements and higher riding comfort demand, the shift quality becomes more and more an important evaluation index of the automated transmission control algorithms. Traditionally, the shift quality is assessed subjectively by the driver's feeling and then adjusted based on the calibration engineer experience. This classical calibration has disadvantages, such as low reproducibility of the shifting event and a high dependence on the driver's driving habit, so here a model-based multi-objective optimization method is proposed, and the optimization of the clutch control parameters during the vehicle starting is used as an example. Firstly, a Modelica® based vehicle model is introduced. A second-order sliding mode control is applied to track the clutch position trajectory. The clutch engagement point and the clutch engagement speed in the slipping stage are taken as the optimization objects.