【2011.05.19】【工况识别】神经网络工况识别的溷合动力电动汽车模煳控制策略.pdf
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1、28 3 2011 M3e Control Theory 2V UNmin Tmax Vemin H, OSOCmin Vemin, NminTmax SOCmaxB T;5V UVe Vemin, Treq :0;(xa)=(ba);(cx)=(cb);0:x a;a 6 x 6 b;b x 6 c;c x:(1)m5 f mFig. 5 Sketch of membership functionK1 e SOC f V U:lf low1000, P1,P1 +P2,lf highP4 P3, P4, 1000. f VU:lf slow10000, P5, P5+P6,lffastP8
2、P7, P8, 10000. K1 f V U:lf small1000, P9, P9 + P10,lf usuaP12P11; P12; P12+P13,lf large366 e 28 P15 P14, P15, 1000. e?5 V1 U.V1 K1 e?5Table 1 Fuzzy control rule of K1SOC low highslow small usua fast usua largeK2 e SOC f V U:lf low1000, P16,P16+P17,lf highP19 P18, P19, 1000. ?Treq f VU:lf NB1000;P20;
3、P20 +P21,lfPBP23 P22;P23;10000. K2 f V U:lf small1000, P24, P24 + P25,lf normalP27P26, P27, P27+P28,lf largeP30 P29;P30;10000. e?5 V2 U.V2 K2 e?5Table 2 Fuzzy control rule of K2SOC low highNB small normalTreq PB normal large4LLL.(Genetic optimize)4.1 “ “ “SSSfff (Objective function) 6 b “S, “Sf ( a
4、Nf ) T(2) U:f (x) =w1 FCFCk+w2 HCHCk+w3 COCOk+w4 NOxNOxk+w5 PMPMk+w6SOCeSOCsSOCk: (2): FC B H h FC(t) 9;HC, CO, NOxsY? b _aBa 9; PM b;kV U -9 H h b ;SOCeV U_ SOC; SOCsV U_ 7 S HSOC; SOCkV U SOC o.Q1 ,SOCoAl0.01,yNlSOCk0.01;w1 = 0:5, w2 = w3 = w4 = w5 = 0:25; w6 = 1.4.2LLL. EEE (Model of genetic algo
5、rithm) | e P1; ;P30,T8y, L. EsK -A I . I n MS1 p,%16= yI VUBM ./30 FBH 8,V5B:x = f(P1;P30): (3)M M uW, V3 U.V3 uWTable 3 Area of parameters |S |SP1 0.10.5 P16 0.10.5P2 0.30.7 P17 0.30.7P3 0.40.7 P18 0.40.7P4 0.50.9 P19 0.50.9P5 5001200 P20 80150P6 9001450 P21 200220P7 9001450 P22 180250P8 14501700 P
6、23 280350P9 0.650.85 P24 0.650.85P10 0.010.09 P25 0.010.09P11 0.010.09 P26 0.010.09P12 0.670.87 P27 0.670.87P13 0.010.09 P28 0.010.09P14 0.010.09 P29 0.010.09P15 0.780.94 P30 0.780.9448 qJx;inPi=1Jx;i: Jx;i8 a, B8 a1 .L. E m m6 U. CRUISEy _ v T 3 MATLAB, B 8,BQ_ ,i h # b “S.L. E ! V4 U.V4L. E Table
7、4 Parameters of genetic algorithmL. | S810Kv 50 q0.7Ms q0.053 Bj: * MY e 367m6L. mFig. 6 Sketch of genetic algorithm4.3TTTsss(Analysis of optimized results)2 R /, -K1#K2 e V5 U.V5 R e TTable 5 Optimized results of the fuzzy controllerparameters based on Guangzhou urbandriving cycle - - | | | |P1 0.
8、2 0.29 P16 0. 3 0.33P2 0. 6 0.32 P17 0. 4 0.43P3 0.5 0.64 P18 0.5 0.50P4 0. 7 0.78 P19 0. 7 0.81P5 635 609 P20 110 128P6 1025 1323 P21 215 214P7 1000 1257 P22 242 240P8 1685 1500 P23 315 314P9 0. 7 0.81 P24 0. 65 0.85P10 0. 02 0.04 P25 0. 05 0.03P11 0. 06 0.03 P26 0. 05 0.05P12 0. 8 0.85 P27 0. 7 0.
9、87P13 0. 04 0.04 P28 0. 05 0.05P14 0. 02 0.05 P29 0. 05 0.06P15 0. 85 0.89 P30 0. 8 0.91 b V6 U.T:h 2.7%, NOxh 2.3%, COh 1.2%, HC b M. 6, SOC1 - 0.027327, SOCoSOCe SOCsh98%. 2 R /,L. E, V SOC0.7, SOCMSl, z,7 O b .V6 R HEVTTable 6 Optimized results of HEV based onGuangzhou urban driving cycles “S - /
10、%NOx 8. 71 8. 51 2. 3CO 0. 84 0. 83 1. 2 b/(gkm1)HC 0. 34 0. 34 0PM 0. 028 0. 028 0/(1100 km1) FC 24. 52 23. 87 2. 7SOCe SOCs 0. 027877 0. 0022 982 Z R /, -K1#K2 e V7 U.V7 Z R e TTable 7 Optimized results of the fuzzy controllerparameters based on Shanghai urbandriving cycle - - | | | |P1 0.2 0.35 P
11、16 0. 3 0.25P2 0.6 0.39 P17 0. 4 0.49P3 0.5 0.52 P18 0.5 0.44P4 0.7 0.77 P19 0. 7 0.71P5 635 523 P20 110 139P6 1025 1382 P21 215 218P7 1000 1348 P22 242 211P8 1685 1575 P23 315 313P9 0.7 0.83 P24 0. 65 0.76P10 0.02 0.05 P25 0. 05 0.05P11 0.06 0.04 P26 0. 05 0.04P12 0.8 0.83 P27 0. 7 0.84P13 0.04 0.0
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- 2011.05 19 工况 识别 神经网络 动力 电动汽车 控制 策略