Adaptive optimal control based on driving style recognition for plug-in hybrid electric vehicle

Abstract Vehicle energy economy is affected by different driving styles of individual drivers. To improve energy economy of plug-in hybrid electric vehicles (PHEVs), it is of great importance to develop the driving style adaptive optimal control strategy. In fact, driving styles are often influenced and restricted by different driving cycles. Therefore, to recognize driving style more accurately, this paper decouples driving styles from driving cycles. Based on classification and identification of driving cycles, the accelerator pedal opening and its change rate in different driving cycles are analyzed and the fuzzy-logic recognizer is built to identify driving styles. Afterwards, the driving style adaptive optimal control strategy is realized by combining the recognized driving style with the equivalent consumption minimization strategy (ECMS) and adopting a hybrid particle swarm optimization-genetic algorithm (PSO-GA) to optimize the relationship between the driving style and the equivalence factor (EF). The effectiveness of proposed driving style adaptive control strategy is validated by real vehicle test, which indicates that, compared with the original ECMS, the proposed driving style recognition based adaptive optimal control strategy improves the energy economy by 3.69% in the New European Driving Cycle (NEDC). This adaptive optimal strategy provides guidance for incorporating driving style into PHEV energy management strategy to improve fuel economy.

[1]  Federico Cheli,et al.  Real time energy management strategy for a fast charging electric urban bus powered by hybrid energy storage system , 2016 .

[2]  Thomas H. Bradley,et al.  Design, demonstrations and sustainability impact assessments for plug-in hybrid electric vehicles , 2009 .

[3]  Srdjan M. Lukic,et al.  Review of driving conditions prediction and driving style recognition based control algorithms for hybrid electric vehicles , 2011, 2011 IEEE Vehicle Power and Propulsion Conference.

[4]  Jesús Casanova Kindelán,et al.  Influence of driving style on fuel consumption and Emissions in diesel-powered passenger car , 2010 .

[5]  Eva Ericsson,et al.  Independent driving pattern factors and their influence on fuel-use and exhaust emission factors , 2001 .

[6]  Hao Ying,et al.  Fuzzy Rule-Based Driver Advisory System for Fuel Economy Improvement in a Hybrid Electric Vehicle , 2007, NAFIPS 2007 - 2007 Annual Meeting of the North American Fuzzy Information Processing Society.

[7]  Monica Vladoiu,et al.  Driving Style Analysis Using Data Mining Techniques , 2010, Int. J. Comput. Commun. Control.

[8]  Irene Michelle Berry,et al.  The effects of driving style and vehicle performance on the real-world fuel consumption of U.S. light-duty vehicles , 2010 .

[9]  Reza Langari,et al.  Intelligent energy management agent for a parallel hybrid vehicle-part I: system architecture and design of the driving situation identification process , 2005, IEEE Transactions on Vehicular Technology.

[10]  Karin Brundell-Freij,et al.  Optimizing route choice for lowest fuel consumption - Potential effects of a new driver support tool , 2006 .

[11]  Datong Qin.  Management Strategy of Hybrid Electrical Vehicle Based on Driving Style Recognition , 2016 .

[12]  Thomas H. Bradley,et al.  Total cost of ownership, payback, and consumer preference modeling of plug-in hybrid electric vehicles , 2013 .

[13]  Xiaosong Hu,et al.  An artificial neural network-enhanced energy management strategy for plug-in hybrid electric vehicles , 2018, Energy.

[14]  Eric Wood,et al.  Accounting for the Variation of Driver Aggression in the Simulation of Conventional and Advanced Vehicles , 2013 .

[15]  Yadollah Saboohi,et al.  Model for developing an eco-driving strategy of a passenger vehicle based on the least fuel consumption , 2009 .

[16]  Bo Gao,et al.  Driving Style Recognition for Intelligent Vehicle Control and Advanced Driver Assistance: A Survey , 2018, IEEE Transactions on Intelligent Transportation Systems.

[17]  Zheng Songlin.  Multi-objective Optimization of Energy Management Strategy of Plug-in Hybrid Electric Vehicle , 2011 .

[18]  Jingwei Li,et al.  Optimal energy management strategy for a plug-in hybrid electric commercial vehicle based on velocity prediction , 2018, Energy.

[19]  Stefan Byttner,et al.  Data-Driven Methods for Classification of Driving Styles in Buses , 2012 .

[20]  Hamid Khayyam,et al.  Adaptive intelligent energy management system of plug-in hybrid electric vehicle , 2014 .

[21]  Reza Langari,et al.  Intelligent energy management agent for a parallel hybrid vehicle-part II: torque distribution, charge sustenance strategies, and performance results , 2005, IEEE Transactions on Vehicular Technology.

[22]  A. Ghanbarzadeh,et al.  Application of PSO (particle swarm optimization) and GA (genetic algorithm) techniques on demand est , 2010 .

[23]  Ahmad Aljaafreh,et al.  Driving style recognition using fuzzy logic , 2012, 2012 IEEE International Conference on Vehicular Electronics and Safety (ICVES 2012).

[24]  D. French,et al.  Behavioral correlates of individual differences in road-traffic crash risk: an examination method and findings. , 1993, Psychological bulletin.

[25]  Zhiguo Zhao,et al.  Development of a typical driving cycle for an intra-city hybrid electric bus with a fixed route , 2018 .

[26]  L. Guzzella,et al.  Control of hybrid electric vehicles , 2007, IEEE Control Systems.

[27]  M. Mikulincer,et al.  The multidimensional driving style inventory--scale construct and validation. , 2004, Accident; analysis and prevention.

[28]  Teng Liu,et al.  Predictive vehicle-following power management for plug-in hybrid electric vehicles , 2019, Energy.

[29]  Y. Kishimoto,et al.  A modeling method for predicting driving behavior concerning with driver’s past movements , 2008, 2008 IEEE International Conference on Vehicular Electronics and Safety.

[30]  Eva Ericsson,et al.  Variability in urban driving patterns , 2000 .

[31]  Qingshan Xu,et al.  Research on configuration strategy for regional energy storage system based on three typical filtering methods , 2016 .

[32]  Mohan M. Trivedi,et al.  Driver classification and driving style recognition using inertial sensors , 2013, 2013 IEEE Intelligent Vehicles Symposium (IV).

[33]  Yi-Hsuan Hung,et al.  Optimal energy management of a hybrid electric powertrain system using improved particle swarm optimization , 2015 .

[34]  D. Clodic,et al.  Dynamic modeling of the electro-mechanical configuration of the Toyota Hybrid System series/parallel power train , 2011 .

[35]  Yue Zhao,et al.  Trip-oriented stochastic optimal energy management strategy for plug-in hybrid electric bus , 2016 .

[36]  Frank Gauterin,et al.  Online driving style recognition using fuzzy logic , 2014, 17th International IEEE Conference on Intelligent Transportation Systems (ITSC).

[37]  J. G. Kretzschmar,et al.  Environmental effects of driving behaviour and congestion related to passenger cars , 2000 .

[38]  Joonwoo Son,et al.  Comparative study between Korea and UK: relationship between driving style and real-world fuel consumption , 2016 .

[39]  Jean-Christophe Popieul,et al.  Driving styles and traffic density diagnosis in simulated driving conditions , 2002, Intelligent Vehicle Symposium, 2002. IEEE.

[40]  Alexandre Szklo,et al.  Plug-in hybrid electric vehicles as a way to maximize the integration of variable renewable energy in power systems: The case of wind generation in northeastern Brazil , 2012 .