Browsing by Author "Lux, G."
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Item Comparison of different Hybrid Electric Vehicles concepts in terms of consumption and efficiency(2012) Torres, O.; Bader, B.; Romeral, J. L.; Lux, G.; Ortega, J. A.; FACTORYThe objective of this paper is to compare the most common HEV power train stractures. As a first step, forward and backward models of these vehicle concepts are implemented using Modelica/Dymola in order to evaluate and compare the energy consumption. Taking into account fuel/electrical consumption and the losses in the powertrain components, a comparison of two different alternatives of Hybrid Electric Vehicle models (parallel structure and Range Extender) are presented in this publication. To simulate these models using different driving cycles, a rule-based operating strategy is implemented. As a second step, a Dynamic Programming (DP) based algorithm is applied to these models. This algorithm is used to determine the optimal fuel consumption for given driving cycles. A comparison of the DP results and rule-based results is carried out to evaluate the potential improvement that is possible to achieve optimizing the energy management strategy and the size of the powertrain components.Item Driving cycle and road grade on-board predictions for the optimal energy management in EV-PHEVs(Institute of Electrical and Electronics Engineers Inc., 2014-10-01) Valera, J. J.; Heriz, B.; Lux, G.; Caus, J.; Bader, B.; Tecnalia Research & InnovationThe prediction of the driving cycle (vehicle speed profile versus time) and the road grade cycle (road grade profile versus time) can improve a variety of vehicle functions, especially the energy management of HEVs and PHEVs. The variability of the driving conditions (environment) together with the nonlinear and variable driver behaviour (driving style) makes the driving cycle 'on-board & real-time' prediction a highly complex task. This paper proposes an intelligent technique for the real time prediction of the vehicle speed and road grade profiles for the (selected) time horizon whilst the vehicle is in route. The proposed method uses an Artificial Neural Network which processes both the vehicle speed measurement (current and previous data samples) and some information related to the driving conditions present in the route, which could be obtained in advance from the new generation of vehicle navigation systems. The driving cycle and road grade on-board predictions allow the energy management system of HEV/PHEVs to achieve further reductions of fuel consumptions.