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B. Beyfuss, P. Hofmann, B. Geringer:
"Efficiency Prediction for Optimal Load Point Determination of Internal Combustion Engines in Hybrid Drives";
Vortrag: 14th International Conference on Engines & Vehicles, Hotel Quisisana Capri | Italy; 15.09.2019 - 19.09.2019; in: "SAE International", (2019), ISSN: 0148-7191; Paper-Nr. 2019-24-0204, 16 S.

The efficiency of a Hybrid Electric Vehicle (HEV) strongly depends on its implemented Energy Management Strategy (EMS) that splits the driver´s torque request onto the Internal Combustion Engine (ICE) and Electric Motor (EM). For calibrating these EMS, usually, steady-state efficiency maps of the power converters are used. These charts are mainly derived from measurements under optimal conditions. However, the efficiency of ICEs fluctuates strongly under different conditions. Among others, these fluctuations can be induced by charge air temperature, engine oil temperature or the fuel´s knock resistance. This paper proposes a new approach for predicting the impact of any external influence onto the ICE efficiency. This is done by computing the actual deviation from the optimal reference ignition timing and adjusting the result by actual oil temperature and target air-to-fuel ratio. For calibration, only a fuel consumption map, measured under random conditions, and some warm-up measurements are required. The efficiency prediction is evaluated by measurements from the engine test bench. Due to the real time capability of this method, the integration into any HEV EMS is possible. By considering this efficiency prediction, the optimal ICE load set-point can be chosen for any condition in HEV. This enables efficient real driving operation including engine warm up, charge air temperature variation, engine knock influences and changes in valve timing or air-to-fuel ratio, for example. For validation, the efficiency prediction is implemented into an adaptive Equivalent Consumption Minimization Strategy (ECMS) and the fuel saving potential is analyzed via simulation for different usecases.

http://dx.doi.org/10.4271/2019-24-0204

http://www.sae-na.it/images/download/ICE2019_low.pdf