Search citation statements
Paper Sections
Citation Types
Year Published
Publication Types
Relationship
Authors
Journals
<div class="section abstract"><div class="htmlview paragraph">In previous work, a serial hybrid powertrain concept with a phlegmatised ICE has been described. Drivability is to be ensured through an innovative predictive operating strategy. Battery State-of-Charge (SoC) is controlled using a backend-based prediction of energy consumption on a given route based on road map and traffic data.</div><div class="htmlview paragraph">In this paper, a spotlight is thrown on the proposed control architecture. On the top level of the controller, a Dynamic Programming algorithm finds an optimal reference trajectory for the SoC over a known route with the goal of avoiding certain Worst-Case scenarios commonly associated with the serial hybrid powertrain topology. Close adherence to the reference trajectory is ensured on a lower level through Model Predictive Control, taking into account additional factors such as battery stress. These control layers closely represent the map DATA distributed on the on-board bus network of state-of-the-art road vehicles under the current ADASIS standard. The necessary input data for the proposed controller is therefore available at no extra cost or engineering effort to OEMs. A simulation framework based on Matlab/Simulink and AVL CruiseM enables testing of the operating strategy using high-quality, open-source map DATA. Thus, the viability of the proposed control architecture is demonstrated in a selection of challenging driving scenarios on real-road speed and gradient profiles. It is shown that this quite basic prediction algorithm outperforms classical, non-predictive serial hybrid operating strategies in terms of drivability. Thus, systematic optimisation of the ICE towards high efficiency and low emissions is enabled, reducing requirements for transient behavior and high power density. Potential for future development, especially further improvements of efficiency and emissions behavior of the ICE through predictive thermal management, is also elucidated.</div></div>
<div class="section abstract"><div class="htmlview paragraph">In previous work, a serial hybrid powertrain concept with a phlegmatised ICE has been described. Drivability is to be ensured through an innovative predictive operating strategy. Battery State-of-Charge (SoC) is controlled using a backend-based prediction of energy consumption on a given route based on road map and traffic data.</div><div class="htmlview paragraph">In this paper, a spotlight is thrown on the proposed control architecture. On the top level of the controller, a Dynamic Programming algorithm finds an optimal reference trajectory for the SoC over a known route with the goal of avoiding certain Worst-Case scenarios commonly associated with the serial hybrid powertrain topology. Close adherence to the reference trajectory is ensured on a lower level through Model Predictive Control, taking into account additional factors such as battery stress. These control layers closely represent the map DATA distributed on the on-board bus network of state-of-the-art road vehicles under the current ADASIS standard. The necessary input data for the proposed controller is therefore available at no extra cost or engineering effort to OEMs. A simulation framework based on Matlab/Simulink and AVL CruiseM enables testing of the operating strategy using high-quality, open-source map DATA. Thus, the viability of the proposed control architecture is demonstrated in a selection of challenging driving scenarios on real-road speed and gradient profiles. It is shown that this quite basic prediction algorithm outperforms classical, non-predictive serial hybrid operating strategies in terms of drivability. Thus, systematic optimisation of the ICE towards high efficiency and low emissions is enabled, reducing requirements for transient behavior and high power density. Potential for future development, especially further improvements of efficiency and emissions behavior of the ICE through predictive thermal management, is also elucidated.</div></div>
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
customersupport@researchsolutions.com
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.