Energy-management system for a hybrid electric vehicle, using ultracapacitors and neural networks

A very efficient energy-management system for hybrid electric vehicles (HEVs), using neural networks (NNs), was developed and tested. The system minimizes the energy requirement of the vehicle and can work with different primary power sources like fuel cells, microturbines, zinc-air batteries, or other power supplies with a poor ability to recover energy from a regenerative braking, or with a scarce power capacity for a fast acceleration. The experimental HEV uses lead-acid batteries, an ultracapacitor (UCAP) bank, and a brushless dc motor with nominal power of 32 kW, and a peak power of 53 kW. The digital signal processor (DSP) control system measures and stores the following parameters: primary-source voltage, car speed, instantaneous currents in both terminals (primary source and UCAP), and actual voltage of the UCAP. When UCAPs were installed on the vehicle, the increase in range was around 5.3% in city tests. However, when optimal control with NN was used, this figure increased to 8.9%. The car used for this experiment is a Chevrolet light utility vehicle (LUV) truck, similar in shape and size to Chevrolet S-10, which was converted to an electric vehicle (EV) at the Universidad Catolica de Chile. Numerous experimental tests under different conditions are compared and discussed.

[1]  Thierry-Marie Guerra,et al.  Optimal control of a parallel powertrain: from global optimization to real time control strategy , 2002, Vehicular Technology Conference. IEEE 55th Vehicular Technology Conference. VTC Spring 2002 (Cat. No.02CH37367).

[2]  Bimal K. Bose,et al.  Artificial neural network applications in power electronics , 2001, IECON'01. 27th Annual Conference of the IEEE Industrial Electronics Society (Cat. No.37243).

[3]  Juan W. Dixon,et al.  Regenerative Braking for an Electric Vehicle Using Ultracapacitors and a Buck-Boost Converter , 2002 .

[4]  Giorgio Rizzoni,et al.  OPTIMAL CONTROL THEORY APPLIED TO HYBRID FUEL CELL POWERED VEHICLE , 2002 .

[5]  F. A. Wyczalek.  Hybrid electric vehicles: year 2000 status , 2001 .

[6]  Giorgio Rizzoni,et al.  Supervisory control of fuel cell vehicles and its link to overall system efficiency and low-level control requirements , 2003, Proceedings of the 2003 American Control Conference, 2003..

[7]  J. Nazuno.  Haykin, Simon. Neural networks: A comprehensive foundation, Prentice Hall, Inc. Segunda Edición, 1999 , 2000 .

[8]  A. Burke.  Ultracapacitors: why, how, and where is the technology , 2000 .

[9]  Niels Kjølstad Poulsen,et al.  Neural Networks for Modelling and Control of Dynamic Systems: A Practitioner’s Handbook , 2000 .

[10]  C. J. Harris,et al.  Neural Networks for Modelling and Control , 1994 .

[11]  Robert F. Stengel,et al.  Optimal Control and Estimation , 1994 .

[12]  S. Hyakin,et al.  Neural Networks: A Comprehensive Foundation , 1994 .