Soft measurement model on torque of alternating current electrical dynamometer including copper loss and iron loss

Abstract

Alternating current electrical dynamometer is a common device to measure the torque of engines, such as the gasoline engine. In order to solve the problems such as high cost, high energy consumption and complicated measurement system which exists in the direct measurement on the torque of alternating current electrical dynamometer, copper loss and iron loss are taken as two key factors and a soft-sensing model on the torque of alternating current electrical dynamometer is established using the fuzzy least square support vector machine (FLS-SVM). Then, the FLS-SVM parameters such as penalty factor and kernel parameter are optimized by adaptive genetic algorithm, torque soft-sensing is investigated in the alternating current electrical dynamometer, as well as the energy feedback efficiency and energy consumption during the measurement phase of a gasoline engine loading continual test is obtained. The results show that the minimum soft-sensing error of torque is about 0.0018, and it fluctuates within a range from −0.3 to 0.3 N-m. FLS-SVM soft-sensing method can increase by 1.6% power generation feedback compared with direct measurement, and it can save 500 kJ fuel consumption in the gasoline engine loading continual test. Therefore, the estimation accuracy of the soft measurement model on the torque of alternating current electrical dynamometer including copper loss and iron loss is high and this indirect measurement method can be feasible to reduce production cost of the alternating current electrical dynamometer and energy consumption during the torque measurement phase of a gasoline engine, replacing the direct method of torque measurement.

Keywords

torque / fuzzy theory / least square support vector machine / alternating current electrical dynamometer

References

[1] AkpolatZ H, AsherG M, Arellano-PadillaJ. A test bed for the experimental validation of position control algorithms [J]. Control Engineering Practice, 2004, 12(12): 933-943

[2] ZhuC L, WangY Z, HouL W. Design and simulation of electrical dynamometer using improved DTC induction motor driver [J]. Journal of Central South University, 2017, 24(24): 1360-1368

[3] TopolnickiJ, SkoczylasN. Low cost high sensitivity dynamometer [J]. Measurement, 2011, 44(44): 74-79

[4] EJ Q, LiuG L, ZhangZ Q, HanD D, ChenJ W, WeiK X, GongJ K, YinZ B. Effect analysis on cold starting performance enhancement of a diesel engine fueled with biodiesel fuel based on an improved thermodynamic model [J]. Applied Energy, 2019, 243: 321-335

[5] EJ Q, ZhaoX H, QiuL S, ZhangZ Q, WeiK X, HanD D, DengY W. Experimental investigation on performance and economy characteristics of a diesel engine with variable nozzle turbocharger and its application in urban bus [J]. Energy Conversion and Management, 2019, 193: 149-161

[6] EJ Q, ZhaoX H, XieL F, ZhangB, ChenJ W, ZuoQ S, HanD D, HuW Y, ZhangZ Q. Performance enhancement of microwave assisted regeneration in a wall-flow diesel particulate filter based on field synergy theory [J]. Energy, 2019, 169: 719-729

[7] Environmental Progress & Sustainable Energy, 2019, 38(38

[8] ZhangB, EJ Q, GongJ, YuanW, ZuoW, LiY, FuJ. Multidisciplinary design optimization of the diesel particulate filter in the composite regeneration process [J]. Applied Energy, 2016, 181: 14-28

[9] WuG, LuZ, PanW, GuanY, LiS, JiC. Experimental demonstration of mitigating self-excited combustion oscillations using an electrical heater [J]. Applied Energy, 2019, 239: 331-342

[10] YuanW H, GongJ K, E JQ, YuanF Q. Fuzzy control strategy for CJD alternating current electrodynamometer [J]. Journal of Xiangtan University: Natural Science, 2006, 2828): 103-106

[11] KuljanicE, SortinoM, TotisG. Multisensor approaches for chatter detection in milling [J]. Journal of Sound and Vibration, 2008, 312(45): 672-693

[12] WooL Y, GlassR S, NovakR F, VisserJ H. Diesel engine dynamometer testing of impedancemetric NO* sensors [J]. Sensors and Actuators B: Chemical, 2011, 157(157): 115-121

[13] LiY, TangW, ChenY, LiuJ, LeeC F. Potential of acetone-butanol-ethanol (ABE) as a biofuel [J]. Fuel, 2019, 242: 673-686

[14] WuG, LuZ, XuX, PanW, WuW, LiJ, CiJ. Numerical investigation of aeroacoustics damping performance of a Helmholtz resonator: Effects of geometry, grazing and bias flow [J]. Aerospace Science and Technology, 2019, 86: 191-203

[15] ZhaoX H, E JQ, WuG D Y W, HanD D, ZhangB, ZhangZ Q A. review of studies using graphenes in energy conversion, energy storage and heat transfer development [J]. Energy Conversion and Management, 2019, 184: 581-599

[16] ZuoH Y, LiuG L, E JQ, ZuoW, WeiK X, HuW Y, TanJ Q, ZhongD Q. Catastrophic analysis on the stability of a large dish solar thermal power generation system with wind-induced vibration [J]. Solar Energy, 2019, 183: 40-49

[17] E J Q, LiuG L, LiuT, ZhangZ Q, ZuoH Y, HuW Y, WeiK X. Harmonic response analysis of a large dish solar thermal power generation system with wind-induced vibration [J]. Solar Energy, 2019, 181: 116-129

[18] E J Q, LiY Q, GongJ K. Function chain neural network prediction on heat transfer performance of oscillating heat pipe based on grey relational analysis [J]. Journal of Central South University of Technology, 2011, 18(18): 1733-1737

[19] ZuoH Y, LuoZ Q, GuanJ L, WangY W. Identification on rock and soil parameters for vibration drilling rock in metal mine based on fuzzy least square support vector machine [J]. Journal of Central South University, 2014, 21(21): 1085-1090

[20] WangT S, ZuoH Y. Fuzzy least squares support vector machines soft measurement model based on adaptive mutative scale chaos immune algorithm [J]. Journal of Central South University, 2014, 21(21): 593-599

[21] E J Q, QianC, ZhuH, PengQ G, ZuoW, LiuG L. Parameter-identification investigations on the hysteretic Preisach model improved by the fuzzy least square support vector machine based on adaptive variable chaos immune algorithm [J]. Journal of Low Frequency Noise, Vibration and Active Control, 2017, 36(36): 227-242

[22] HaoZ, ZhaoH L, ZhangC, WangH, JiangY Z, YiZ Y. Estimating winter wheat area based on an SVM and the variable fuzzy set method [J]. Remote Sensing Letters, 2019, 10(10): 343-352

[23] RoyA, LaskarR H. Fuzzy SVM based fuzzy adaptive filter for denoising impulse noise from color images [J]. Multimedia Tools and Applications, 2019, 78(78): 1785-1804

[24] ManoharL, GanesanK. Diagnosis of schizophrenia disorder in MPR brain images using multi-objective BPSO based feature selection with fuzzy SVM [J]. Journal of Medical and Biological Engineering, 2018, 38(38): 917-932

[25] ZhouX L, JiangP Y, WangX X. Recognition of control chart patterns using fuzzy SVM with a hybrid kernel function [J]. Journal of Intelligent Manufacturing, 2018, 29(29): 51-67