锂离子电池Simulink模型.zip

close all clear echo on clc % NEWFF——生成一个新的前向神经网络 % TRAIN——对 BP 神经网络进行训练 % SIM——对 BP 神经网络进行仿真 pause % 敲任意键开始 clc % 定义训练样本 % P 为输入矢量 P=[1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00;1.00,0.99,0.98,0.95,0.90,0.86,0.80,0.78,0.71,0.69,0.66,0.45,0.32,0.13,0.00 ;1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00]; % T 为目标矢量 T=[1.00,0.91,0.84,0.76,0.68,0.60,0.51,0.46,0.38,0.30,0.21,0.12,0.08,0.05,0.00]; pause; clc % 创建一个新的前向神经网络 net=newff(minmax(P),[9,1],{'tansig','purelin'},'traingdm') % 当前输入层权值和阈值 inputWeights=net.IW{1,1} inputbias=net.b{1} % 当前网络层权值和阈值 layerWeights=net.LW{2,1} layerbias=net.b{2} pause clc % 设置训练参数 net.trainParam.show = 2; %显示训练迭代过程 net.trainParam.lr = 0.05; %学习速率 net.trainParam.mc = 0.9; %网络中设置动量因子为0.9 net.trainParam.epochs = 2000; %最大训练次数 net.trainParam.goal = 0.001; %训练精度 pause clc % 调用 TRAINGDM 算法训练 BP 网络 [net,tr]=train(net,P,T); pause clc % 对 BP 网络进行仿真 A = sim(net,P) % 计算仿真误差 E = T - A MSE=mse(E) pause clc echo off h1=plot(U,A,'-r'); xlabel('电压(V)'),ylabel('SOC'); title('BP网络建模测试曲线'); hold on ; h2=plot(U,T,'-b'); legend([h1 h2],'预测值','实测值'); close all clear echo on clc % NEWFF——生成一个新的前向神经网络 % TRAIN——对 BP 神经网络进行训练 % SIM——对 BP 神经网络进行仿真 pause % 敲任意键开始 clc % 定义训练样本 % P 为输入矢量 P=[1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00,1.00;1.00,0.61,0.46,0.43,0.32,0.32,0.19,0.20,0.14,0.00 ;0.00,1.00,1.00,0.30,0.30,0.30,0.30,0.30,0.30,0.30]; % T 为目标矢量 T=[1.00,0.90,0.81,0.65,0.48,0.30,0.26,0.16,0.10,0.00]; pause; clc % 创建一个新的前向神经网络 net=newff(minmax(P),[3,1],{'tansig','purelin'},'traingdm') % 当前输入层权值和阈值 inputWeights=net.IW{1,1} inputbias=net.b{1} % 当前网络层权值和阈值 layerWeights=net.LW{2,1} layerbias=net.b{2} pause clc % 设置训练参数 net.trainParam.show = 50; %显示训练迭代过程 net.trainParam.lr = 0.05; %学习速率 net.trainParam.mc = 0.9; %网络中设置动量因子为0.9 net.trainParam.epochs = 1000; %最大训练次数 net.trainParam.goal = 1e-3; %训练精度 pause clc % 调用 TRAINGDM 算法训练 BP 网络 [net,tr]=train(net,P,T); pause clc % 对 BP 网络进行仿真 A = sim(net,P) % 计算仿真误差 E = T - A MSE=mse(E) pause clc echo off