zbMATH — the first resource for mathematics

A fast model-predictive speed controller for minimised charge consumption of electric vehicles. (English) Zbl 1338.93156
Summary: This paper presents the design of a real-time implementable energy-efficient model-predictive cruise controller for electric vehicles including the driving speed reference generation. The controller is designed to meet the properties of a series-production electric vehicle whose characteristics are identified and validated by measurements. The predictive eco-cruise controller aims at finding the best compromise between speed-reference tracking and energy consumption of the vehicle using an underlying dynamic model of the vehicle motion and charge consumption. The originally non-linear motion model is transformed into a linear model mainly by using a coordinate transform. To obtain a piecewise linear approximation of the charge consumption map, the measured characteristics are approximated by a convex piecewise linear function represented as the maximum of a set of linear constraint functions. The reformulations finally lead to a model-predictive control approach with quadratic cost function, linear prediction model and linear constraints that corresponds to a piecewise linear system behaviour and allows a fast real-time implementation with guaranteed convergence. Simulation results of the closed-loop operation finally illustrate the effectiveness of the approach.

93B40 Computational methods in systems theory (MSC2010)
93B15 Realizations from input-output data
93C95 Application models in control theory
Full Text: DOI
[1] Barkenbus, Eco-driving: An overlooked climate change initiative, Energy Policy 38 (2) pp 762– (2010) · doi:10.1016/j.enpol.2009.10.021
[2] Rommerskirchen C. M. Helmbrecht K. Bengler Increasing complexity of driving situations and its impact on an adas for anticipatory assistance for the reduction of fuel consumption 2013 IEEE Intelligent Vehicles Symposium (iv) Gold Coast City, Australia 573 578 2013
[3] Schwarzkopf, Control of highway vehicles for minimum fuel consumption over varying terrain, Trans. Res. 11 (4) pp 279– (1977) · doi:10.1016/0041-1647(77)90093-4
[4] Saerens B. Optimal control based eco-driving - theoretical approach and practical applications Ph.D. Thesis 2012
[5] Lattemann F. K. Neiss S. Terwen T. Connolly The predictive cruise control U a system to reduce fuel consumption of heavy duty trucks Technical Report 2004-01-2616 2004
[6] 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
[7] Kamal, Model predictive control of vehicles on urban roads for improved fuel economy, IEEE Trans. Control Syst. Technol. 21 (3) pp 831– (2013) · doi:10.1109/TCST.2012.2198478
[8] Asadi, Predictive cruise control: Utilizing upcoming traffic signal information for improving fuel economy and reducing trip time, IEEE Trans. Control Syst. Technol. 19 (3) pp 707– (2011) · doi:10.1109/TCST.2010.2047860
[9] Lin X D. Gorges S. Liu Eco-driving assistance system for electric vehicles based on speed profile optimization 2014 IEEE Conference on Control Applications (CCA) Antibes, France 629 634 2014
[10] Li X. Y. Chen J. Wang In-wheel motor electric ground vehicle energy management strategy for maximizing the travel distance 20102 American Control Conference (ACC) Montreal, Canada 4993 4998 2012
[11] Kuriyama M. S. Yamamoto M. Miyatake Theoretical study on eco-driving technique for an electric vehicle with dynamic programming 2010 International Conference on Electrical Machines and Systems (ICEMS) Incheon, South Korea 2026 2030 2010
[12] Dib W. L. Serrao A. Sciarretta Optimal control to minimize trip time and energy consumption in electric vehicles 2011 IEEE Vehicle Power and Propulsion Conference (VPPC) Chicago, Illinois, USA 1 8 2011
[13] Kirches C. Fast numerical methods for mixed-integer nonlinear model-predicitve control Ph.D. Thesis 2010
[14] Schwickart T. H. Voos J. R. Hadji-Minaglou M. Darouach A novel model-predictive cruise controller for electric vehicles and energy-efficient driving AIM 2014. 11th IEEE International Conference on Advanced Intelligent Mechatronics, 2014 Besancon, France 1067 1072 2014 · Zbl 1307.93154
[15] Schwickart, Design and simulation of a real-time implementable energy-efficient model-predictive cruise controller for electric vehicles, J. Frankl. Inst.-Eng. Appl. Math. 352 (2) pp 603– (2014) · Zbl 1307.93154 · doi:10.1016/j.jfranklin.2014.07.001
[16] Guzzella, Vehicle Propulsion Systems - Introduction to Modeling and Optimization (2013) · doi:10.1007/978-3-642-35913-2
[17] Zomotor, Fahrwerktechnik, Fahrverhalten (1991)
[18] Smart Specifications 2013 http://www.smart.de/
[19] Hellstroem E. Look-ahead control of heavy vehicles Ph.D. Thesis 2010
[20] Kohut N. K. Hedrick F. Borrelli Integrating traffic data and model predictive control to improve fuel economy 12th IFAC Symposium on Control in Transportation Systems Redondo Beach, California, USA 155 160 2009
[21] Saerens, A methodology for assessing eco-cruise control for passenger vehicles, Transp. Res. Part D- Transp. Environ. 19 (0) pp 20– (2013) · doi:10.1016/j.trd.2012.12.001
[22] Stefanov, Separable Programming - Theory and Methods (2001) · doi:10.1007/978-1-4757-3417-1
[23] Boyd, Convex Optimization (2004) · doi:10.1017/CBO9780511804441
[24] Magnani, Convex piecewise-linear fitting, Optim. Eng. 10 (1) pp 1– (2009) · Zbl 1273.65086 · doi:10.1007/s11081-008-9045-3
[25] Mayne, Constrained model predictive control: Stability and optimality, Automatica 36 (6) pp 789– (2000) · Zbl 0949.93003 · doi:10.1016/S0005-1098(99)00214-9
[26] Maciejowski, Predictive Control with Constraints (2001) · Zbl 0978.93002
[27] Mattingley, Cvxgen: a code generator for embedded convex optimization, Optim.Eng. 13 (1) pp 1– (2012) · Zbl 1293.65095 · doi:10.1007/s11081-011-9176-9
This reference list is based on information provided by the publisher or from digital mathematics libraries. Its items are heuristically matched to zbMATH identifiers and may contain data conversion errors. It attempts to reflect the references listed in the original paper as accurately as possible without claiming the completeness or perfect precision of the matching.