Improving Efficiency Through Adaptive Internal Model Control of Hydrogen-Based Genset Used as a Range Extender for Electric Vehicles

This paper addresses a hydrogen-based generator (genset) adaptive control used as a range extender of a battery electric vehicle. Based on a commercially available gasoline genset, this hydrogen-based generator can use a mixture of gasoline and hydrogen in which the proportion of gasoline varies between 0% and 100%. This hybrid energy system is controlled by an onboard energy management system that splits the electric power demand between the battery and the genset. Given the genset power profile, a maximum efficiency tracking module was designed to provide optimal operating conditions (engine speed and electric power) to a real-time controller. To tackle the genset nonlinearities, an adaptive controller based on the internal model control approach is designed and successfully validated. In addition, a comparative study with an industrial-based control method indicates that the proposed approach is effective and can achieve significant improvement in genset efficiency.

[1]  K. Agbossou,et al.  PEMFC low temperature startup for electric vehicle , 2012, IECON 2012 - 38th Annual Conference on IEEE Industrial Electronics Society.

[2]  Intan Z. Mat Darus,et al.  System Identification for Internal Combustion Engine Model , 2012, 2012 Sixth Asia Modelling Symposium.

[3]  Lino Guzzella,et al.  Adaptive internal model control with application to fueling control , 2010 .

[4]  Lei Rao,et al.  SmartCar: Smart charging and driving control for electric vehicles in the smart grid , 2014, 2014 IEEE Global Communications Conference.

[5]  P. M. Diéguez,et al.  Conversion of a commercial gasoline vehicle to run bi-fuel (hydrogen-gasoline) , 2012 .

[6]  C. C. Chan,et al.  The State of the Art of Electric, Hybrid, and Fuel Cell Vehicles , 2007, Proceedings of the IEEE.

[7]  Carlos E. Garcia,et al.  Internal model control. 2. Design procedure for multivariable systems , 1985 .

[8]  Sebastian Verhelst,et al.  Onderzoek naar de verbranding in waterstofverbrandingsmotoren - A Study of the Combustion in Hydrogen-Fuelled Internal Combustion Engines , 2005 .

[9]  Sousso Kelouwani,et al.  Tracking maximum efficiency of hydrogen genset used as electric vehicle range extender , 2014 .

[10]  P. M. Diéguez,et al.  Conversion of a gasoline engine-generator set to a bi-fuel (hydrogen/gasoline) electronic fuel-injec , 2011 .

[11]  Muhammad Shafiq Internal model control structure using adaptive inverse control strategy. , 2005 .

[12]  Aniruddha Datta,et al.  Adaptive internal model control: Design and stability analysis , 1996, Autom..

[13]  A. Isidori,et al.  Semiglobal nonlinear output regulation with adaptive internal model , 2000, Proceedings of the 39th IEEE Conference on Decision and Control (Cat. No.00CH37187).

[14]  L. Ljung,et al.  Subspace-based multivariable system identification from frequency response data , 1996, IEEE Trans. Autom. Control..

[15]  Hyunchul Ju,et al.  Evaluating cold-start behaviors of end and intermediate cells in a polymer electrolyte fuel cell (PEFC) stack , 2012 .

[16]  Michel Verhaegen,et al.  Closed-loop subspace identification methods: an overview , 2013 .

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

[18]  Michael Buchholz,et al.  Recursive subspace identification of linear parameter-varying systems , 2012, 2012 American Control Conference (ACC).

[19]  Gregory N. Washington,et al.  Mechatronic design and control of hybrid electric vehicles , 2000 .

[20]  Chang,et al.  Energy Management Strategies for a Hybrid Electric Vehicle , 2005 .

[21]  Ning Zhou,et al.  Robust RLS Methods for Online Estimation of Power System Electromechanical Modes , 2007, IEEE Transactions on Power Systems.

[22]  S. Verhelst,et al.  Hydrogen-fueled internal combustion engines , 2014 .

[23]  D. Naidu,et al.  Optimal Control Systems , 2018 .

[24]  Simona Onori,et al.  Analysis of energy management strategies in plug-in hybrid electric vehicles: Application to the GM Chevrolet Volt , 2013, 2013 American Control Conference.

[25]  Ramon Vilanova,et al.  Generalized Internal Model Control for Balancing Input/Output Disturbance Response , 2011 .

[26]  Lorenzo Marconi,et al.  Semi-global nonlinear output regulation with adaptive internal model , 2001, IEEE Trans. Autom. Control..

[27]  Pierluigi Pisu,et al.  A Comparative Study Of Supervisory Control Strategies for Hybrid Electric Vehicles , 2007, IEEE Transactions on Control Systems Technology.

[28]  Tomomichi Hagiwara,et al.  Sequential tuning methods of LQ/LQI controllers for multivariable systems and their application to hot strip mills , 2000 .

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

[30]  Shantanu Das,et al.  LQR based improved discrete PID controller design via optimum selection of weighting matrices using fractional order integral performance index , 2013, 1301.0931.

[31]  Han-Fu Chen,et al.  New Approach to Recursive Identification for ARMAX Systems , 2010, IEEE Transactions on Automatic Control.

[32]  Ajith Abraham,et al.  Modified Line Search Method for Global Optimization , 2007, First Asia International Conference on Modelling & Simulation (AMS'07).

[33]  Thomas Wallner,et al.  Electricity Powering Combustion: Hydrogen Engines , 2012, Proceedings of the IEEE.

[34]  P. P. J. van den Bosch,et al.  Online Energy Management for Hybrid Electric Vehicles , 2008, IEEE Transactions on Vehicular Technology.

[35]  K. Agbossou,et al.  Online System Identification and Adaptive Control for PEM Fuel Cell Maximum Efficiency Tracking , 2012, IEEE Transactions on Energy Conversion.

[36]  Jae Young Lee,et al.  On integral generalized policy iteration for continuous-time linear quadratic regulations , 2014, Autom..

[37]  C. S. George Lee,et al.  Adaptive perturbation control with feedforward compensation for robot manipulators , 1985 .

[38]  M. Morari,et al.  Internal model control: PID controller design , 1986 .

[39]  J. Suykens,et al.  Subspace identification of Hammerstein systems using least squares support vector machines , 2005 .