Nonlinear Model Predictive Control of a Power-Split Hybrid Electric Vehicle With Consideration of Battery Aging
-
Published:2019-03-25
Issue:8
Volume:141
Page:
-
ISSN:0022-0434
-
Container-title:Journal of Dynamic Systems, Measurement, and Control
-
language:en
-
Short-container-title:
Affiliation:
1. Department of Mechanical Engineering— Engineering Mechanics, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931 e-mail: 2. Mem. ASME Department of Mechanical Engineering— Engineering Mechanics; 3. Department of Electrical and Computer Engineering, Michigan Technological University, 1400 Townsend Drive, Houghton, MI 49931 e-mail:
Abstract
In this paper, the nonlinear model predictive control (NMPC) for the energy management of a power-split hybrid electric vehicle (HEV) has been studied to improve battery aging while maintaining the fuel economy at a reasonable level. A first principle battery model is built with simulation capacity of the battery aging features. The built battery model is integrated with an HEV model from autonomie software to investigate the vehicle and battery performance under control strategies. The NMPC has simplified battery models to predict the state of charge (SOC) change, the fuel consumption of the engine, and the battery aging index over the predicted horizon. The purpose of the NMPC is to find an optimized control sequence over the prediction horizon, which minimizes the designed cost function. The proposed control strategy is compared with that of an NMPC, which does not consider the battery aging. It is found that, with the optimized weighting factor selection, the NMPC with the consideration of battery aging has better battery aging performance and similar fuel economy performance comparing with the NMPC without the consideration of battery aging.
Publisher
ASME International
Subject
Computer Science Applications,Mechanical Engineering,Instrumentation,Information Systems,Control and Systems Engineering
Reference40 articles.
1. Musardo, C., Rizzoni, G., and Staccia, B., 2005, “A-ECMS: An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management,” 44th IEEE Conference on Decision and Control, Seville, Spain, Dec. 12–15, pp. 1816–1823. 2. Dynamic Programming-Informed Equivalent Cost Minimization Control Strategies for Hybrid-Electric Vehicles;ASME J. Dyn. Syst., Meas., Control,2013 3. Paganelli, G., Delprat, S., Guerra, T. M., Rimaux, J., and Santin, J. J., 2002, “Equivalent Consumption Minimization Strategy for Parallel Hybrid Powertrains,” IEEE 55th Vehicular Technology Conference (VTC Spring), Birmingham, AL, May 6–9.10.1109/VTC.2002.1002989 4. Li, Y., and Chen, B., 2016, “Development of Integrated Rule-Based Control and Equivalent Consumption Minimization Strategy for HEV Energy Management,” IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), Auckland, New Zealand, Aug. 29–31.10.1109/MESA.2016.7587127 5. Wang, H., Sacheva, K., Tripp, J., Chen, B., Robinette, D., and Shahbakhti, M., 2018, “Optimal Map-Based Mode Selection and Powertrain Control for a Multi-Mode Plug-In Hybrid Electric Vehicle,” IEEE/ASME International Conference on Mechatronic and Embedded Systems and Applications (MESA), Oulu, Finland, July 2–4.10.1109/MESA.2018.8449157
Cited by
16 articles.
订阅此论文施引文献
订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献
|
|