Eco-Driving Strategy Implementation for Ultra-Efficient Lightweight Electric Vehicles in Realistic Driving Scenarios

Pietro Stabile, Federico Ballo, Giorgio Previati, Giampiero Mastinu and Massimiliano Gobbi ()
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Pietro Stabile: Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy
Federico Ballo: Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy
Giorgio Previati: Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy
Giampiero Mastinu: Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy
Massimiliano Gobbi: Department of Mechanical Engineering, Politecnico di Milano, 20156 Milan, Italy

Energies, 2023, vol. 16, issue 3, 1-19

Abstract: This paper aims to provide a quantitative assessment of the effect of driver action and road traffic conditions in the real implementation of eco-driving strategies. The study specifically refers to an ultra-efficient battery-powered electric vehicle designed for energy-efficiency competitions. The method is based on the definition of digital twins of vehicle and driving scenario. The models are used in a driving simulator to accurately evaluate the power demand. The vehicle digital twin is built in a co-simulation environment between VI-CarRealTime and Simulink. A digital twin of the Brooklands Circuit (UK) is created leveraging the software RoadRunner. After validation with actual telemetry acquisitions, the model is employed offline to find the optimal driving strategy, namely, the optimal input throttle profile, which minimizes the energy consumption over an entire lap. The obtained reference driving strategy is used during real-time driving sessions at the dynamic driving simulator installed at Politecnico di Milano (DriSMi) to include the effects of human driver and road traffic conditions. Results assess that, in a realistic driving scenario, the energy demand could increase more than 20% with respect to the theoretical value. Such a reduction in performance can be mitigated by adopting eco-driving assistance systems.

Keywords: eco-driving; electric vehicle; digital twin; dynamic driving simulator (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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