The fuel economy of Hybrid Electric Vehicles (HEVs) can be further improved by exploiting the increased connectivity level of last-generation vehicles. Minimizing the fuel consumption of an HEV is a global problem and its optimal solution inevitably entails the complete knowledge of the driving conditions. Hence, optimality can only be reached on a limited number of a priori known mission profiles, and never on real driving test cases. Thus, the capabilities of conventional Energy Management Systems (EMS) can be strongly enhanced by integrating the prediction of future vehicle speed into the powertrain control strategy. Vehicle-to-Everything (V2X) technology adoption paves the way for reliable future driving conditions forecasting. As a result, in this paper, the possible exploitation of information derived from V2X connectivity was explored to develop an innovative Adaptation algorithm for an Equivalent Consumption Minimization Strategy (A-V2X- ECMS). Driving pattern identification was employed to predict future driving conditions and, in turn, to adapt the equivalence factor of the ECMS. Hence, an Artificial Neural Network (ANN) was trained to continuously optimize the equivalence factor thus ensuring an enhanced fuel economy while guaranteeing charge sustainability. The potential of this innovative Adaptive ECMS (A-V2X-ECMS) was assessed, by means of numerical simulation, on a P2 diesel Plug-in Hybrid Electric Vehicle (PHEV) available on the European market. An experimental campaign was carried out on the real vehicle, by testing it both on an All-Wheel Drive (AWD) chassis dynamometer, and in Real Driving Emissions (RDE) scenarios. Then, a virtual test rig of the investigated vehicle was built in the GT-SUITE software environment and validated against experimental results. The simulation results proved that the proposed approach can significantly improve the strategy adaptability and its fuel economy potential if compared with the conventional EMS taken as reference. Fuel consumption reductions up to 4% were demonstrated, depending on the vehicle mission profile. Finally, a significant reduction of the amplitude of the State of Charge (SoC) swings in charge sustaining conditions was proved

Energy Management System Optimization Based on V2X Connectivity / Millo, Federico; Rolando, Luciano; Pulvirenti, Luca. - ELETTRONICO. - 1:(2021). ((Intervento presentato al convegno FISITA World Congress 2021 tenutosi a PRAGUE (CZ) nel 14–16 Settembre 2021 [10.46720/F2020-ADM-087].

Energy Management System Optimization Based on V2X Connectivity

Millo, Federico;Rolando, Luciano;Pulvirenti, Luca
2021

Abstract

The fuel economy of Hybrid Electric Vehicles (HEVs) can be further improved by exploiting the increased connectivity level of last-generation vehicles. Minimizing the fuel consumption of an HEV is a global problem and its optimal solution inevitably entails the complete knowledge of the driving conditions. Hence, optimality can only be reached on a limited number of a priori known mission profiles, and never on real driving test cases. Thus, the capabilities of conventional Energy Management Systems (EMS) can be strongly enhanced by integrating the prediction of future vehicle speed into the powertrain control strategy. Vehicle-to-Everything (V2X) technology adoption paves the way for reliable future driving conditions forecasting. As a result, in this paper, the possible exploitation of information derived from V2X connectivity was explored to develop an innovative Adaptation algorithm for an Equivalent Consumption Minimization Strategy (A-V2X- ECMS). Driving pattern identification was employed to predict future driving conditions and, in turn, to adapt the equivalence factor of the ECMS. Hence, an Artificial Neural Network (ANN) was trained to continuously optimize the equivalence factor thus ensuring an enhanced fuel economy while guaranteeing charge sustainability. The potential of this innovative Adaptive ECMS (A-V2X-ECMS) was assessed, by means of numerical simulation, on a P2 diesel Plug-in Hybrid Electric Vehicle (PHEV) available on the European market. An experimental campaign was carried out on the real vehicle, by testing it both on an All-Wheel Drive (AWD) chassis dynamometer, and in Real Driving Emissions (RDE) scenarios. Then, a virtual test rig of the investigated vehicle was built in the GT-SUITE software environment and validated against experimental results. The simulation results proved that the proposed approach can significantly improve the strategy adaptability and its fuel economy potential if compared with the conventional EMS taken as reference. Fuel consumption reductions up to 4% were demonstrated, depending on the vehicle mission profile. Finally, a significant reduction of the amplitude of the State of Charge (SoC) swings in charge sustaining conditions was proved
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11583/2972335