Stochastic Optimum Energy Management for Advanced Transportation Network

Abstract Smart and optimal energy consumption in electric vehicles has high potential to improve the limited cruising range on a single battery charge. The proposed concept is a semi-autonomous ecological advanced driver assistance system which predictively plans for a safe and energy-efficient cruising velocity profile autonomously for battery electric vehicles. However, high entropy in transportation network leads to a challenging task to derive a computationally efficient and tractable model to predict the traffic flow. Stochastic optimal control has been developed to systematically find an optimal decision with the aim of performance improvement. However, most of the developed methods are not real-time algorithms. Moreover, they are mainly risk-neutral for safety-critical systems. This paper investigates on the real-time risk-sensitive nonlinear optimal control design subject to safety and ecological constraints. This system improves the efficiency of the transportation network at the microscopic level. Obtained results demonstrate the effectiveness of the proposed method in terms of states regulation and constraints satisfaction.

[1]  Mohamed Darouach,et al.  A Fast Model‐Predictive Speed Controller for Minimised Charge Consumption of Electric Vehicles , 2016 .

[2]  James B. Rawlings,et al.  Economic Dynamic Real-Time Optimization and Nonlinear Model-Predictive Control on Infinite Horizons , 2009 .

[3]  Dipti Mishra,et al.  Design and Simulation , 2015 .

[4]  Amir Ahmadi-Javid.  Application of entropic value-at-risk in machine learning with corrupted input data , 2012, 2012 11th International Conference on Information Science, Signal Processing and their Applications (ISSPA).

[5]  G. Ripaccioli,et al.  Stochastic model predictive control with driver behavior learning for improved powertrain control , 2010, 49th IEEE Conference on Decision and Control (CDC).

[6]  A. Mesbah,et al.  Stochastic Model Predictive Control: An Overview and Perspectives for Future Research , 2016, IEEE Control Systems.

[7]  Javad Mohammadpour,et al.  Stochastic model predictive control for LPV systems , 2017, 2017 American Control Conference (ACC).

[8]  Toshiyuki Ohtsuka,et al.  A continuation/GMRES method for fast computation of nonlinear receding horizon control , 2004, Autom..

[9]  Holger Voos,et al.  Risk-averse Stochastic Nonlinear Model Predictive Control for Real-time Safety-critical Systems , 2017 .

[10]  Mohamed Darouach,et al.  Fast stochastic non-linear model predictive control for electric vehicle advanced driver assistance systems , 2017, 2017 IEEE International Conference on Vehicular Electronics and Safety (ICVES).

[11]  Mohamed Darouach,et al.  Nonlinear model predictive extended eco-cruise control for battery electric vehicles , 2016, 2016 24th Mediterranean Conference on Control and Automation (MED).

[12]  Mohamed Darouach,et al.  Design and simulation of a real-time implementable energy-efficient model-predictive cruise controller for electric vehicles , 2015, J. Frankl. Inst..

[13]  Amir Ahmadi-Javid,et al.  Entropic Value-at-Risk: A New Coherent Risk Measure , 2012, J. Optim. Theory Appl..

[14]  Junichi Murata,et al.  Model Predictive Control of Vehicles on Urban Roads for Improved Fuel Economy , 2013, IEEE Transactions on Control Systems Technology.