python电池soc预测

使用Python实现电池SOC预测的方法及模型
基于Basisformer的时间序列锂离子电池SOC预测
为了实现基于Basisformer的时间序列锂离子电池SOC预测，可以采用深度学习框架PyTorch。此方法旨在利用Basisformer模型对锂离子电池的SOC进行准确预测，从而提高电池使用效率并优化能源管理系统[^2]。
以下是具体的实现过程：

准备环境

安装必要的库：
pip install torch torchvision torchaudio pandas numpy matplotlib scikit-learn
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加载所需模块：
import torch
from torch import nn, optim
import torch.nn.functional as F
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
from sklearn.preprocessing import MinMaxScaler
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数据预处理

读取和清理数据集，并对其进行标准化处理以便更好地训练神经网络：
def load_and_preprocess_data(file_path):
df = pd.read_csv(file_path)

scaler = MinMaxScaler(feature_range=(-1, 1))
scaled_features = scaler.fit_transform(df[['voltage', 'current', 'temperature']])
df_scaled = pd.DataFrame(scaled_features, columns=['voltage', 'current', 'temperature'])

return df_scaled.values, scaler

data, scaler = load_and_preprocess_data('battery_data.csv')
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构建Basisformer模型结构

定义自定义层MultiHeadAttentionLayer以及完整的Transformer编码器架构：
class MultiHeadAttentionLayer(nn.Module):
def __init__(self, hid_dim, n_heads, dropout, device):
super().__init__()

self.hid_dim = hid_dim
self.n_heads = n_heads

assert hid_dim % n_heads == 0

self.w_q = nn.Linear(hid_dim, hid_dim)
self.w_k = nn.Linear(hid_dim, hid_dim)
self.w_v = nn.Linear(hid_dim, hid_dim)

self.fc_o = nn.Linear(hid_dim, hid_dim)

self.dropout = nn.Dropout(dropout)

def forward(self, query, key, value, mask=None):
batch_size = query.shape[0]

Q = self.w_q(query)
K = self.w_k(key)
V = self.w_v(value)

# ... (省略部分代码) ...

class TransformerEncoder(nn.Module):
def __init__(self, input_dim, hid_dim, n_layers, n_heads, pf_dim, dropout, device, max_length=100):
super().__init__()

self.device = device

self.tok_embedding = nn.Embedding(input_dim, hid_dim)
self.pos_embedding = nn.Embedding(max_length, hid_dim)

self.layers = nn.ModuleList([EncoderLayer(hid_dim,
n_heads,
pf_dim,
dropout,
device)
for _ in range(n_layers)])

self.dropout = nn.Dropout(dropout)

self.scale = torch.sqrt(torch.FloatTensor([hid_dim])).to(device)

def forward(self, src, src_mask):
# ... (省略部分代码) ...

model = TransformerEncoder(
input_dim=data.shape[-1],
hid_dim=512,
n_layers=6,
n_heads=8,
pf_dim=2048,
dropout=0.1,
device=device
).to(device)
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训练与评估

编写函数用于分割训练/测试集、创建批次迭代器、定义损失函数和优化器，并执行实际的训练循环：
train_ratio = 0.7
split_index = int(len(data)*train_ratio)

X_train, X_test = data[:split_index], data[split_index:]
y_train, y_test = data[:split_index,-1:], data[split_index:,-1:]

batch_size = 64
dataset = TensorDataset(X_train, y_train)
dataloader = DataLoader(dataset, batch_size=batch_size, shuffle=True)

criterion = nn.MSELoss()
optimizer = optim.Adam(model.parameters(), lr=0.001)

for epoch in range(epochs):
model.train()

running_loss = []
for i, (inputs, labels) in enumerate(dataloader):

inputs = inputs.to(device)
labels = labels.to(device)

optimizer.zero_grad()

outputs = model(inputs.unsqueeze(-1)).squeeze(-1)
loss = criterion(outputs, labels.float())

loss.backward()
optimizer.step()

running_loss.append(loss.item())

avg_loss = sum(running_loss)/len(running_loss)
print(f'Epoch {epoch}, Loss: {avg_loss}')
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结果可视化

绘制预测值对比真实值图表以直观展示模型性能：
with torch.no_grad():
predictions = []

test_inputs = X_test.clone().detach().float().unsqueeze(-1).to(device)
predicted_output = model(test_inputs).cpu().numpy()

plt.figure(figsize=(10,6))
plt.plot(y_test.flatten(), label='Actual SOC')
plt.plot(predicted_output.flatten(), label='Predicted SOC')
plt.legend(loc="upper left")
plt.show()
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