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polestar007
ÖÁ×ðľ³æ (Ö°Òµ×÷¼Ò)
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fwlcq(½ð±Ò+10):·Ç³£¸Ðл£¡ 2010-10-12 09:48:44
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FN ISI Export Format VR 1.0 PT J AU Piao, CH Fu, WL Lei, GH Cho, CD AF Piao, Chang-hao Fu, Wen-li Lei, Gai-hui Cho, Chong-du TI Online Parameter Estimation of the Ni-MH Batteries Based on Statistical Methods SO ENERGIES AB Based on the application of the power batteries, this paper uses a statistical method to estimate the internal resistance and open-circuit voltage of Ni-MH battery. Battery status is monitored and simulated by battery pack test bench. Through using ideal battery model and fitting the data of measured voltage and current, the battery internal resistance and open-circuit voltage are estimated. The average relative error between battery statistic internal resistance and pulse internal resistance is less than 15% in different state of charge. Relative error is influenced by dispersion and symmetry of charge or discharge current. Average of absolute error in open-circuit voltage is about 5% respectively. The results show that it is feasible and accurate to estimate the parameters of Ni-MH battery by using statistical method. SN 1996-1073 PD FEB PY 2010 VL 3 IS 2 BP 206 EP 215 DI 10.3390/en3020206 UT ISI:000276705200004 ER -------------------------------------------------------------------------------- EF |

2Â¥2010-10-12 09:39:43
polestar007
ÖÁ×ðľ³æ (Ö°Òµ×÷¼Ò)
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fwlcq(½ð±Ò+10):·Ç³£¸Ðл£¡Äã¿ÉÒÔ°ÑÕâ¸ö²éѯµ½µÄÍøÒ³´«¸øÎÒÂð£¿Ð»Ð»£¡ÎÒµÄQQ£º806922418 2010-10-12 09:49:17
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FN ISI Export Format VR 1.0 PT B AU Piao, CH Fu, WL Wang, J Huang, ZY Cho, CD AF Piao, Chang-Hao Fu, Wen-Li Wang, Jin Huang, Zhi-Yu Cho, Chongdu GP IEEE TI Estimation of the state of charge of Ni-MH battery pack based on artificial neural network SO INTELEC 09 - 31ST INTERNATIONAL TELECOMMUNICATIONS ENERGY CONFERENCE LA English DT Proceedings Paper CT 31st International Telecommunications Energy Conference (INTELEC 09) CY OCT 18-22, 2009 CL Incheon, SOUTH KOREA ID LEAD-ACID-BATTERIES; ELECTRIC VEHICLES; OF-CHARGE AB To track the state of charge (SOC) of Ni-MH battery pack at the hybrid electric vehicle, an artificial neural network (ANN) is designed. Current, voltage and the previous SOC are used to inputs of ANN, and output is SOC. The result show that, this artificial neural network can track the state of charge(SOC) of the batteries accurately, in the average tracking error less than 5%; the ANN is in low dependence on the initial SOC, and the output can be achieved target value only in 90 seconds. C1 [Piao, Chang-Hao; Fu, Wen-Li; Huang, Zhi-Yu] Chongqing Univ Posts & Commun, Minist Educ, Key Lab Network Control & Intelligent Instrument, Chongqing 400065, Peoples R China. RP Piao, CH, Chongqing Univ Posts & Commun, Minist Educ, Key Lab Network Control & Intelligent Instrument, Chongqing 400065, Peoples R China. EM changhaopark@hotmail.com NR 11 TC 0 PU IEEE PI NEW YORK PA 345 E 47TH ST, NEW YORK, NY 10017 USA BN 978-1-4244-2490-0 PY 2009 BP 785 EP 788 PG 4 SC Energy & Fuels; Engineering, Electrical & Electronic; Telecommunications GA BPG08 UT ISI:000278798400148 ER -------------------------------------------------------------------------------- EF |

3Â¥2010-10-12 09:40:45
polestar007
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4Â¥2010-10-12 11:01:07
polestar007
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5Â¥2010-10-12 11:01:28
fwlcq
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6Â¥2010-10-12 16:44:31











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