A neurofuzzy-controlled power management strategy for a series hybrid electric vehicle

This paper focuses on the design of the power management strategy as the key factor in improving the performance in terms of the efficiency, the range and the fuel consumption for a small-scale series hybrid electric vehicle. A complex hybrid vehicle system is considered, and a practically realisable and traceable neurofuzzy strategy for improving the vehicle efficiency is introduced. The method results in extending the vehicle’s range while deciding when to switch the internal-combustion engine on or off as a function of the state of charge of the battery and the electrical power produced from the generator. Consequently, the speed of the internal-combustion engine (i.e. the current produced) is determined as a function of the driving conditions. Suitable tests were performed in order to verify the effectiveness of the proposed strategy; the verification tests were carried out using a consolidated model which also includes real-world experimental vehicle data. The results show that, by using the proposed power management strategy, a good compromise between the efficiency, the range and the fuel consumption can be obtained in many practically useful driving conditions.

[1]  Masatoshi Sakawa,et al.  Operation planning of district heating and cooling plants using genetic algorithms for mixed integer programming , 2001, Appl. Soft Comput..

[2]  Dirk Uwe Sauer,et al.  Influence of plug-in hybrid electric vehicle charging strategies on charging and battery degradation costs , 2012 .

[3]  Ebrahim Mamdani,et al.  Applications of fuzzy algorithms for control of a simple dynamic plant , 1974 .

[4]  Olle Sundström,et al.  Torque-Assist Hybrid Electric Powertrain Sizing: From Optimal Control Towards a Sizing Law , 2010, IEEE Transactions on Control Systems Technology.

[5]  P. J. MacVicar-Whelan Fuzzy sets for man-machine interaction , 1976 .

[6]  Marco Sorrentino,et al.  Analysis of a rule-based control strategy for on-board energy management of series hybrid vehicles , 2011 .

[7]  Huei Peng,et al.  Optimal Control of Hybrid Electric Vehicles Based on Pontryagin's Minimum Principle , 2011, IEEE Transactions on Control Systems Technology.

[8]  Michio Sugeno,et al.  Fuzzy identification of systems and its applications to modeling and control , 1985, IEEE Transactions on Systems, Man, and Cybernetics.

[9]  Giorgio Rizzoni,et al.  A-ECMS: An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management , 2005, Proceedings of the 44th IEEE Conference on Decision and Control.

[10]  Kazuo Tanaka,et al.  Stability analysis and design of fuzzy control systems , 1992 .

[11]  Frank L. Lewis,et al.  Control of a nonholonomic mobile robot using neural networks , 1998, IEEE Trans. Neural Networks.

[12]  C. M. Shepherd Design of Primary and Secondary Cells II . An Equation Describing Battery Discharge , 1965 .

[13]  K. T. Chau,et al.  Overview of power management in hybrid electric vehicles , 2002 .

[14]  Mutasim A. Salman,et al.  Emissions and fuel economy trade-off for hybrid vehicles using fuzzy logic , 2004, Math. Comput. Simul..

[15]  Marco Gadola,et al.  Simulation tool for optimization and performance prediction of a generic hybrid electric series powertrain , 2014 .

[16]  Bo Gu,et al.  An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management Based on Driving Pattern Recognition , 2006 .

[17]  Houssem Jerbi,et al.  An advanced fuzzy logic gain scheduling trajectory control for nonlinear systems , 2010 .

[18]  Mutasim A. Salman,et al.  Energy management strategies for parallel hybrid vehicles using fuzzy logic , 2000 .

[19]  Guillermo R. Bossio,et al.  Optimization of power management in an hybrid electric vehicle using dynamic programming , 2006, Math. Comput. Simul..

[20]  Tomaž Katrašnik,et al.  Energy conversion efficiency of hybrid electric heavy-duty vehicles operating according to diverse drive cycles , 2009 .

[21]  Seung-Ki Sul,et al.  Fuzzy-logic-based torque control strategy for parallel-type hybrid electric vehicle , 1998, IEEE Trans. Ind. Electron..

[22]  Ozan Erdinc,et al.  Energy management of an FC/UC hybrid vehicular power system using a combined neural network-wavelet transform based strategy , 2010 .

[23]  Morteza Montazeri-Gh,et al.  Application of genetic algorithm for optimization of control strategy in parallel hybrid electric vehicles , 2006, J. Frankl. Inst..

[24]  F. R. Salmasi,et al.  Control Strategies for Hybrid Electric Vehicles: Evolution, Classification, Comparison, and Future Trends , 2007, IEEE Transactions on Vehicular Technology.

[25]  L.-A. Dessaint,et al.  A Generic Battery Model for the Dynamic Simulation of Hybrid Electric Vehicles , 2007, 2007 IEEE Vehicle Power and Propulsion Conference.

[26]  Xiaosong Hu,et al.  Energy efficiency analysis of a series plug-in hybrid electric bus with different energy management strategies and battery sizes , 2013 .

[27]  Enrique Romero-Cadaval,et al.  Electric vehicle battery charger for smart grids , 2012 .