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Model predictive control of hybrid electric vehicles for improved fuel economy. (English) Zbl 1354.93053

Summary: This brief proposes a model predictive control method using preceding vehicle information within Hybrid Electric Vehicles’ (HEVs’) predictive cruise control system to improve car following performance and reduce fuel consumption. This paper adds two original contributions to the related literature. First, a real-time optimization approach using Pontryagin’s minimum principle with analytical methods rather than numerical iteration methods is proposed. Second, to compute the desired battery state of charge trajectory as a function of vehicle position, only the topographic profile of the future road segments must be known. Both the fuel economy and the driving profile are optimized using the proposed approach. Simulation results show that fuel economy using the proposed method is improved significantly.

MSC:

93B40 Computational methods in systems theory (MSC2010)
93C95 Application models in control theory
49N90 Applications of optimal control and differential games
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[1] Sciarretta, Control of hybrid electric vehicles, IEEE Control Syst. Mag. 27 (2) pp 60– (2007) · doi:10.1109/MCS.2007.338280
[2] Zhang, Role of terrain preview in energy management of hybrid electric vehicles, IEEE Trans. Control Syst. Technol. 59 (3) pp 1139– (2010)
[3] Yu, Performance of a nonlinear real-time optimal control system for HEVs/PHEVs during car following, J. Appl. Math. (2014) · doi:10.1155/2014/879232
[4] Serrao , L. A comparative analysis of energy management strategies for hybrid electric vehicles PhD thesis 2009
[5] Liu, Modeling and control of a power-split hybrid vehicle, IEEE Trans Control Syst. Technol. 16 (6) pp 1242– (2008) · doi:10.1109/TCST.2008.919447
[6] Kim, Optimal control of hybrid electric vehicles based on Pontryagin’s minimum principle, IEEE Trans. Control Syst. Technol. 19 (5) pp 1279– (2011) · doi:10.1109/TCST.2010.2061232
[7] Hu, Comparison of three electrochemical energy buffers applied to a hybrid bus powertrain with simultaneous optimal sizing and energy management, IEEE Trans. Intell. Transp. Syst. 15 (3) pp 1193– (2014) · doi:10.1109/TITS.2013.2294675
[8] Kamal, Ecological vehicle control on roads with up-down slopes, IEEE Trans. Intell. Transp. Syst. 12 (3) pp 783– (2011) · doi:10.1109/TITS.2011.2112648
[9] Sun, Dynamic traffic feedback data enabled energy management in plug-in hybrid electric vehicles, IEEE Trans. Control Syst. Technol. 23 (3) pp 1075– (2015) · doi:10.1109/TCST.2014.2361294
[10] Sun, Velocity predictors for predictive energy management in hybrid electric vehicles, IEEE Trans. Control Syst. Technol. 23 (3) pp 1197– (2015) · doi:10.1109/TCST.2014.2359176
[11] Zhang, Route preview in energy management of plug-in hybrid vehicles, IEEE Trans. Control Syst. Technol. 20 (2) pp 546– (2012) · doi:10.1109/TCST.2011.2115242
[12] Zeng , X. J. Wang A stochastic model predictive control approach for hybrid electric vehicle energy management with road grade preview ASME Dyn. Syst. Control Conf. San Antonio, TX, USA 2014
[13] Chen, Energy management for a power-split plug-in hybrid electric vehicle based on dynamic programming and neural networks, IEEE Trans. on Veh. Technol. 63 (4) pp 1567– (2014) · doi:10.1109/TVT.2013.2287102
[14] Hellstrom , E. J. Aslund L. Nielsen Design of a well-behaved algorithm for on-board look-ahead control Proc. IFAC World Congr. 2008
[15] Yu, Model predictive control of a power-split hybrid electric vehicle system, Artificial Life and Robotics 17 (2) pp 221– (2012) · doi:10.1007/s10015-012-0046-0
[16] Yu, A battery management system using nonlinear model predictive control for a hybrid electric vehicle, 7th IFAC Symp. Advances in Automotive Control 7 (1) pp 301– (2013)
[17] Yu, Battery management using model predictive control for a plug-in hybrid electric vehicle, SICE Journal of Control, Measurement, and System Integration 7 (5) pp 304– (2014) · doi:10.9746/jcmsi.7.304
[18] Yu, Performance of an eco-driving model predictive control system for HEVs during car following, Asian J. Control 18 (1) pp 16– (2016) · Zbl 1338.93158 · doi:10.1002/asjc.1155
[19] Yu, Model predictive control for hybrid vehicle ecological driving using traffic signal and road slope information, Control Theory and Technology 13 (1) pp 17– (2015) · doi:10.1007/s11768-015-4058-x
[20] Yu, Model Predictive Control for Hybrid Electric Vehicle Platooning Using Slope Information, IEEE Trans. Intell. Transp. Syst. (2016) · doi:10.1109/TITS.2015.2513766
[21] Yu, Model predictive control for connected hybrid electric vehicles, Math. Probl. Eng. (2015) · Zbl 1394.93246 · doi:10.1155/2015/318025
[22] Yu, Model predictive control for hybrid electric vehicle platooning using route information, Proc. Inst. Mech. Eng., Part D: J. Automob. Eng (2016) · doi:10.1177/0954407015606314
[23] Yu, Model predictive control of a power-split hybrid electric vehicle system with slope preview, Artificial Life and Robotics 20 (4) pp 305– (2015) · doi:10.1007/s10015-015-0230-0
[24] Rotenberg, Ultracapacitor assisted powertrains: modeling, control, sizing, and the impact on fuel economy, IEEE Trans. Control Syst. Technol. 19 (3) pp 576– (2011) · doi:10.1109/TCST.2010.2048431
[25] Ohtsuka, A continuation/GMRES method for fast computation of nonlinear receding horizon control, Automatica 40 (4) pp 563– (2004) · Zbl 1168.93340 · doi:10.1016/j.automatica.2003.11.005
[26] Li, Obser-based fault detection for nonlinear systems with sensor fault and limited communication capacity, IEEE Automatic Control (2016) · Zbl 1359.93065 · doi:10.1109/TAC.2015.2503566
[27] Li, Switched fuzzy output feedback control and its application to mass-spring-damping system, IEEE Fuzzy Systems (2016) · doi:10.1109/TFUZZ.2015.2505332
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