Neural network sliding mode control based on on-line identification for electric vehicle with ultracapacitor-battery hybrid power

International Journal of Control, Automation and Systems

Volumn 7, Issue 3, 2009, Pages 409-418

  Cao, Jian Bo ^a   Cao, Bing Gang ^b  

^a ZHEJIANG NORMAL UNIVERSITY   (China)

^b XI'AN JIAOTONG UNIVERSITY   (China)

Author keywords

Electric vehicle; Hybrid power; Neural network; Regenerative braking; Sliding mode control; Ultracapacitor

Indexed keywords

BACK-PROPAGATION NEURAL NETWORKS; BIDIRECTIONAL DC/DC CONVERTERS; DRIVING RANGE; HYBRID POWER; HYBRID POWER SYSTEMS; LONG LIFE; NOVEL NEURAL NETWORK; ON-LINE IDENTIFICATION; PARAMETER PREDICTION; PID CONTROLLERS; POWER SOURCES; RADIAL BASIS FUNCTION NEURAL NETWORKS; REGENERATIVE BRAKING; RESPONSE SPEED; SLIDING MODE CONTROLLER; SPECIFIC POWER; START-UP; STEADY STATE TRACKING; SWITCHING GAIN; SYSTEM IDENTIFICATIONS; ULTRACAPACITOR;

BRAKING; CAPACITORS; ELECTRIC AUTOMOBILES; ELECTRIC CONTROL EQUIPMENT; ELECTRIC POWER MEASUREMENT; ENGINEERING EXHIBITIONS; IDENTIFICATION (CONTROL SYSTEMS); LAND VEHICLE PROPULSION; MATHEMATICAL MODELS; NEURAL NETWORKS; PROPORTIONAL CONTROL SYSTEMS; RADIAL BASIS FUNCTION NETWORKS; SLIDING MODE CONTROL; THREE TERM CONTROL SYSTEMS; TWO TERM CONTROL SYSTEMS; VEHICLES;

CONTROLLERS;

EID: 67949095962     PISSN: 15986446     EISSN: None     Source Type: Journal    
DOI: 10.1007/s12555-009-0310-9     Document Type: Article

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