网站手机适配跳转,手机网页游戏排行榜2021前十名,曲阜住房城乡建设局网站,泉州网站建设怎么收费电池的故障预测和健康管理PHM是为了保障设备或系统的稳定运行#xff0c;提供参考的电池健康管理信息#xff0c;从而提醒决策者及时更换电源设备。不难发现#xff0c;PHM的核心问题就是确定电池的健康状态#xff0c;并预测电池剩余使用寿命。但是锂电池的退化过程影响因…电池的故障预测和健康管理PHM是为了保障设备或系统的稳定运行提供参考的电池健康管理信息从而提醒决策者及时更换电源设备。不难发现PHM的核心问题就是确定电池的健康状态并预测电池剩余使用寿命。但是锂电池的退化过程影响因素众多不仅受其本身工作模式的影响外部环境的压力、温度等都会影响锂电池的退化。这些影响因素之间的相互耦合导致锂电池的退化表现出很强的非线性及不确定性这给SOH估计和RUL预测带来了很大的困难。
该项目代码较简单主要包括Capacity predictRUL predictTrends predic以Capacity predict为例首先加载模块。
import torchimport torch.nn as nnimport torch.optim as optimimport torch.nn.functional as F from torch.utils.data import DataLoader, TensorDataset
import pandas as pdimport numpy as npimport osimport matplotlib.pyplot as pltimport warningswarnings.filterwarnings(ignore)device torch.device(cuda:0 if torch.cuda.is_available() else cpu)Debug False
模型定义
# 定义LSTM模型class LSTMModel(nn.Module): def __init__(self, conv_input, input_size, hidden_size, num_layers, output_size): super(LSTMModel, self).__init__() self.convnn.Conv1d(conv_input,conv_input,1) self.lstm nn.LSTM(input_size, hidden_size, num_layers, batch_firstTrue, bidirectionalTrue).to(device) #self.fc1 nn.Linear(hidden_size*2, hidden_size*2) self.fc nn.Linear(hidden_size, output_size) self.num_layers num_layers self.hidden_dim hidden_size self.dropout nn.Dropout(p0.3)def forward(self, x): xself.conv(x) h0 torch.randn((self.num_layers, x.shape[0], self.hidden_dim)).to(device) # 初始化隐藏状态 c0 torch.randn((self.num_layers, x.shape[0], self.hidden_dim)).to(device) # 初始化细胞状态 output, _ self.lstm(x,(h0,c0)) output self.dropout(output) output self.fc(output[:, -1, :]) return output
导入数据
# 创建一个空列表来存储读取的 DataFramesdataframes_Cap []dataframes_EIS []
# 使用循环读取文件并分配名称for i in range(1, 9): # 构建文件名 file_name_cap fCapacity_data/Data_Capacity_25C{i:02}.txt file_name_EIS fEIS_data/EIS_state_V_25C{i:02}.txt # 使用状态V if not os.path.isfile(file_name_cap): print(fCap文件 {file_name_cap} 不存在跳过...) continue elif not os.path.isfile(file_name_EIS): print(fEIS文件 {file_name_EIS} 不存在跳过...) continue# 读取文件并添加到列表 df_cap pd.read_csv(file_name_cap, sep\t) df_EIS pd.read_csv(file_name_EIS, sep\t) #print(df_cap.columns) if i 1 or i5: cap_number 3 else: cap_number 5 #剔除表现不佳的电池 if i 4 or i 8: continue cycle [] cap [] eis [] cycle_max df_cap[df_cap.columns[1]].max() cycle_max2 df_EIS[df_EIS.columns[1]].max() cycle_number min(cycle_max,cycle_max2) max_scale df_cap[df_cap[df_cap.columns[1]]0][df_cap.columns[cap_number]][:].max() for i in range(1,int(cycle_number)1): temp df_cap[df_cap[df_cap.columns[1]]i][df_cap.columns[cap_number]][-1:].max() temp_EIS_Re np.array(df_EIS[df_EIS[df_EIS.columns[1]]i][df_EIS.columns[3]][:]) temp_EIS_Im np.array(df_EIS[df_EIS[df_EIS.columns[1]]i][df_EIS.columns[4]][:]) cycle.append(i) cap.append(temp) eis.append(np.concatenate((temp_EIS_Re, temp_EIS_Im), axis0)) dataframes_Cap.append(cap) dataframes_EIS.append(eis)
#将35数据读入
# 使用循环读取文件并分配名称for i in range(1, 3): # 构建文件名 file_name_cap fCapacity_data/Data_Capacity_35C{i:02}.txt file_name_EIS fEIS_data/EIS_state_V_35C{i:02}.txt # 使用状态V if not os.path.isfile(file_name_cap): print(fCap文件 {file_name_cap} 不存在跳过...) continue elif not os.path.isfile(file_name_EIS): print(fEIS文件 {file_name_EIS} 不存在跳过...) continue# 读取文件并添加到列表 df_cap pd.read_csv(file_name_cap, sep\t) df_EIS pd.read_csv(file_name_EIS, sep\t) cap_number 3 cycle [] cap [] eis [] cycle_max df_cap[df_cap.columns[1]].max() cycle_max2 df_EIS[df_EIS.columns[1]].max() cycle_number min(cycle_max,cycle_max2) #max_scale df_cap[df_cap[df_cap.columns[1]]1][df_cap.columns[cap_number]][-1:].max() for i in range(1,int(cycle_number)1): temp df_cap[df_cap[df_cap.columns[1]]i][df_cap.columns[cap_number]][-1:].max() temp_EIS_Re np.array(df_EIS[df_EIS[df_EIS.columns[1]]i][df_EIS.columns[3]][:]) temp_EIS_Im np.array(df_EIS[df_EIS[df_EIS.columns[1]]i][df_EIS.columns[4]][:]) #temp temp/max_scale cycle.append(i) cap.append(temp) eis.append(np.concatenate((temp_EIS_Re, temp_EIS_Im), axis0))dataframes_Cap.append(cap) dataframes_EIS.append(eis)
#将45数据读入for i in range(1, 3): # 构建文件名 file_name_cap fCapacity_data/Data_Capacity_45C{i:02}.txt file_name_EIS fEIS_data/EIS_state_V_45C{i:02}.txt # 使用状态V if not os.path.isfile(file_name_cap): print(fCap文件 {file_name_cap} 不存在跳过...) continue elif not os.path.isfile(file_name_EIS): print(fEIS文件 {file_name_EIS} 不存在跳过...) continue# 读取文件并添加到列表 df_cap pd.read_csv(file_name_cap, sep\t) df_EIS pd.read_csv(file_name_EIS, sep\t) #print(df_cap.columns) cap_number 3 cycle [] cap [] eis [] cycle_max df_cap[df_cap.columns[1]].max() cycle_max2 df_EIS[df_EIS.columns[1]].max() cycle_number min(cycle_max,cycle_max2) #max_scale df_cap[df_cap[df_cap.columns[1]]1][df_cap.columns[cap_number]][-1:].max() for i in range(1,int(cycle_number)1): temp df_cap[df_cap[df_cap.columns[1]]i][df_cap.columns[cap_number]][-1:].max() temp_EIS_Re np.array(df_EIS[df_EIS[df_EIS.columns[1]]i][df_EIS.columns[3]][:]) temp_EIS_Im np.array(df_EIS[df_EIS[df_EIS.columns[1]]i][df_EIS.columns[4]][:]) #temp temp/max_scale cycle.append(i) cap.append(temp) eis.append(np.concatenate((temp_EIS_Re, temp_EIS_Im), axis0)) dataframes_Cap.append(cap) dataframes_EIS.append(eis)
X []y []for i in range(0,len(dataframes_Cap)): for j in range(len(dataframes_Cap[i])): X.append(dataframes_EIS[i][j]) y.append(dataframes_Cap[i][j])X np.array(X)y np.array(y)print(X.shape,y.shape)
# 将EIS的每个实部和每个虚部分别各自归一化remax []immax []data{}from sklearn.preprocessing import MinMaxScalerscaler MinMaxScaler()y y.reshape(-1,1)# 对标签也进行归一化y scaler.fit_transform(y)# 将每份电池单独制作便于交叉训练和验证, start 0for i in range(len(dataframes_Cap)): feature_name fEIS{i1:02} target_name fCap{i1:02} n len(dataframes_Cap[i]) X_r X[start:startn,:60].copy()#将实部整体进行归一化 X_r_flat X_r.flatten() #取第一个EIS的最大最小值进行归一 X_r_min X_r_flat[:60].min() X_r_max X_r_flat[:60].max() remax.append(X_r_flat[:].max()/X_r_max) normalized_Xr_flat ((X_r_flat.reshape(-1, 1))-X_r_min)/(X_r_max-X_r_min) normalized_Xr_data normalized_Xr_flat.reshape(X[start:startn,:60].shape) #将虚部进行归一化 X_i X[start:startn,60:] X_i_flat X_i.flatten() X_i_min X_i_flat[:60].min() X_i_max X_i_flat[:60].max()immax.append(X_i_flat[:].max()/X_i_max) normalized_Xi_flat ((X_i_flat.reshape(-1, 1))-X_i_min)/(X_i_max-X_i_min) normalized_Xi_data normalized_Xi_flat.reshape(X[start:startn,60:].shape) data[feature_name] np.concatenate((normalized_Xr_data, normalized_Xi_data), axis1) data[feature_name] data[feature_name].reshape(-1,2, 60)#将数据形式转换为batch602实部和虚部作为一个整体特征 data[feature_name] data[feature_name].transpose(0, 2, 1) data[target_name] y[start:startn].reshape(-1,1) start n
if Debug: # 检查数据效果 for i in range(15,20): x_plot data[EIS01][i][:60] y_plot data[EIS01][i][60:] plt.plot(x_plot, y_plot) plt.show()
if Debug: # 检查数据效果 for i in range(15,20): x_plot data[Cap02][:] plt.plot(x_plot) plt.show()
start 0for i in range(1,11): if i 1: trainning_data data[fEIS{i:02}][start:].copy() trainning_target data[fCap{i:02}][start:].copy() #剔除测试集 elif i!4 and i! 8 and i! 10: #else: trainning_data np.vstack((trainning_data,data[fEIS{i:02}][start:])) trainning_target np.vstack((trainning_target,data[fCap{i:02}][start:]))
trainning_data torch.tensor(trainning_data, dtypetorch.float32)trainning_target torch.tensor(trainning_target, dtypetorch.float32)
开始训练
# 初始化模型、损失函数和优化器input_size 2 # 特征数量hidden_size 128num_layers 5output_size 1conv_input 60batch_size 128epochs 1500n_splits 5 from sklearn.model_selection import KFold import gcdef gc_collect(): gc.collect() torch.cuda.empty_cache()gc_collect()
kf KFold(n_splitsn_splits, shuffleTrue)
model_number 0for train_idx, val_idx in kf.split(trainning_data): train_X, val_X trainning_data[train_idx], trainning_data[val_idx] train_y, val_y trainning_target[train_idx], trainning_target[val_idx] train_dataset TensorDataset(train_X, train_y) val_dataset TensorDataset(val_X, val_y) train_loader DataLoader(train_dataset, batch_sizebatch_size, shuffleTrue) val_loader DataLoader(val_dataset, batch_sizebatch_size, shuffleFalse) model LSTMModel(conv_input, input_size, hidden_size, num_layers, output_size) model model.to(device) criterion nn.MSELoss() optimizer optim.Adam(model.parameters(), lr0.0001,betas(0.5,0.999)) for epoch in range(epochs): model.train() for i, (inputs, labels) in enumerate(train_loader): inputs inputs.to(device) labels labels.to(device) optimizer.zero_grad() outputs model(inputs) loss criterion(outputs, labels) loss.backward() optimizer.step() model.eval() with torch.no_grad(): for inputs, labels in val_loader: inputs inputs.to(device) labels labels.to(device) outputs model(inputs) val_loss criterion(outputs, labels) if epoch%100 0: print(fEpoch [{epoch1}/{epochs}], Loss: {loss.item()}, Validation Loss: {val_loss.item()}) torch.save(model.state_dict(), fmodel_weights/CNNBiLSTM/test{model_number}.pth) model_number 1
from sklearn.metrics import mean_squared_errorfrom sklearn.metrics import r2_scoreimport math
# 创建画布 fig, axs plt.subplots(nrows3, ncols4, figsize(15, 8)) ID 1title_name 1start 0mean_RMSE_train 0mean_RMSE_test 0mean_R2_train 0mean_R2_test 0model LSTMModel(conv_input, input_size, hidden_size, num_layers, output_size) model model.to(device)
# 在每个小区域中绘制图像 for i in range(3): for j in range(4): result [] x torch.tensor(data[fEIS{ID:02}], dtypetorch.float32) for k in range(n_splits): model.load_state_dict(torch.load(fmodel_weights/CNNBiLSTM/test{k}.pth, map_locationtorch.device(device))) out model(x.to(device)) out out.cpu() out out.detach().numpy() out scaler.inverse_transform(out) result.append(out) result np.array(result) out np.mean(result, axis0) out_upper np.max(result, axis0) out_upper np.squeeze(out_upper) out_lower np.min(result, axis0) out_lower np.squeeze(out_lower) true data[fCap{ID:02}] true scaler.inverse_transform(true) MSE mean_squared_error(out[start:], true[start:]) R2_result r2_score(true[start:], out[start:]) RMSE_result math.sqrt(MSE) mean_RMSE_train RMSE_result mean_R2_train R2_result RMSE_str {:.4f}.format(RMSE_result) R2_str {:.4f}.format(R2_result) x np.linspace(0,x.shape[0],x.shape[0]) axs[i, j].plot(x[start:], true[start:]) axs[i, j].plot(x[start:], out[start:]) axs[i, j].fill_between(x[start:], out_upper[start:], out_lower[start:], colororange, alpha0.5) axs[i, j].set_title(f25Cap{title_name:02}) axs[i, j].text(0.95, 0.95, RMSE: RMSE_str, haright, vatop, fontsize12, transformaxs[i, j].transAxes) axs[i, j].text(0.95, 0.85, R2: R2_str, haright, vatop, fontsize12, transformaxs[i, j].transAxes) # 使用循环将数组中的每个元素写入文件 with open(fdata/Nature_Cap_train{title_name:02}, w) as file: for item in range(out[start:].shape[0]): out_number round(float(out[start:][item].flatten()), 4) #file.write(str(out_number) \tstr(out_upper[start:][item]) \tstr(out_lower[start:][item]) \n) file.write(str(out_number)\n) # 关闭文件 file.close() ID 1 title_name 1 if ID 11: break# 调整子图之间的距离 plt.tight_layout()plt.savefig(figure_results/cap_alltempalldata_test_5_10_12.png)print(train RMSE: , mean_RMSE_train/8)print(train R2: , mean_R2_train/8) # 显示图像 plt.show() RUL预测结果 Trends预测结果 担任《Mechanical System and Signal Processing》《中国电机工程学报》《控制与决策》等期刊审稿专家擅长领域现代信号处理机器学习深度学习数字孪生时间序列分析设备缺陷检测、设备异常检测、设备智能故障诊断与健康管理PHM等。
