Analysis of optimal battery state-of-charge trajectory for blended regime of plug-in hybrid electric vehicle

被引:0
作者
Škugor B. [1 ]
Soldo J. [1 ]
Deur J. [1 ]
机构
[1] University of Zagreb, Ivana Lučića 5, Zagreb
来源
World Electric Vehicle Journal | 2019年 / 10卷 / 04期
关键词
Battery state-of-charge trajectory; Dynamic programming; Efficiency; Optimization; Plug-in hybrid electric vehicle; Power management;
D O I
10.3390/wevj10040075
中图分类号
学科分类号
摘要
Plug-in hybrid electric vehicles (PHEV) typically combine several power sources, which call for the use of optimal control strategy design techniques. The PHEV powertrain efficiency can be improved if the battery is gradually discharged by blending fully electric and hybrid driving modes during the whole trip. Here, the battery state-of-charge (SoC) trajectory profile is of particular importance to achieving near-optimal powertrain operation. In order to reveal optimal patterns of SoC trajectory profiles, numerical optimizations of PHEV control variables based on the dynamic programing (DP) algorithm are conducted in the paper. The obtained optimal SoC trajectories are found to form linear-like profiles of minimum length when expressed with respect to travelled distance. Detailed analyses of the DP results point out that the SoC trajectory length is minimized in order to minimize electric losses, which is then reflected in reduced total fuel consumption. This finding is further justified by analyzing the problem of optimal discharging for the simplified battery-only system and for the powertrain as a whole. The impact of engine specific fuel consumption characteristic on the optimal SoC trajectory profile under simplified driving conditions is analyzed, as well. © 2019 by the authors.
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  • [1] Miller M.A., Holmes A.G., Conlon B.M., Savagian P.J., The GM "Voltec" 4ET50 Multi-Mode Electric Transaxle, SAE Int. J. Engines, 4, pp. 1102-1114, (2011)
  • [2] Skugor B., Cipek M., Deur J., Control Variables Optimization and Feedback Control Strategy Design for the Blended Operating Mode of an Extended Range Electric Vehicle, SAE Int. J Altern. Powertrains, 3, pp. 152-162, (2014)
  • [3] Yu H., Kuang M., McGee R., Trip-Oriented Energy Management Control Strategy for Plug-In Hybrid Electric Vehicles, IEEE Trans. Control Syst. Technol, 22, pp. 1323-1336, (2014)
  • [4] Onori S., Tribioli L., Adaptive Pontryagin's Minimum Principle supervisory controller design for the plug-in hybrid GM Chevrolet Volt, Appl. Energy, 147, pp. 224-234, (2015)
  • [5] Soldo J., Skugor B., Deur J., Optimal Energy Management Control of a Parallel Plug-in Hybrid Electric Vehicle in the Presence of Low Emission Zones, Proceedings of the WCX SAE World Congress Experience, (2019)
  • [6] Martinez C.M., Hu X., Cao D., Velenis E., Gao B., Wellers M., Energy Management in Plug-in Hybrid Electric Vehicles: Recent Progress and a Connected Vehicles Perspective, IEEE Trans. Veh. Technol, 66, pp. 4534-4549, (2017)
  • [7] Ambuhl D., Guzzella L., Predictive reference signal generator for hybrid electric vehicles, IEEE Trans. Veh. Technol, 58, pp. 4730-4740, (2009)
  • [8] Liu Y., Li J., Qin D., Lei Z., Energy management of plug-in hybrid electric vehicles using road grade preview, In Proceedings of the IET International Conference on Intelligent and Connected Vehicles (ICV 2016), (2016)
  • [9] Bouwman K.R., Pham T.H., Wilkins S., Hofman T., Predictive Energy Management Strategy Including Traffic Flow Data for Hybrid Electric Vehicles, IFAC-PapersOnLine, 50, pp. 10046-10051, (2017)
  • [10] Gaikwad T.D., Asher Z.D., Liu K., Huang M., Kolmanovsky I., Vehicle Velocity Prediction and Energy Management Strategy Part 2: Integration of Machine Learning Vehicle Velocity Prediction with Optimal Energy Management to Improve Fuel Economy, In Proceedings of the WCX SAE World Congress Experience, (2019)