The unofficial reproduction of A transferable lithium-ion battery remaining useful life prediction method from cycle-consistency of degradation trend
torch==1.13.1
- Feature Extraction
python preprocessing.py
- Model Training
python main.py
class Cycle_Consistency_Loss(nn.Module):
def __init__(self, penalty=0.01):
super(Cycle_Consistency_Loss, self).__init__()
self.penalty = penalty
def forward(self, seq, src_len, combinations):
loss_i, loss_j = 0, 0
src_len = src_len//4
for c in combinations: # the combinations in a batch
seq1, seq2 = seq[c[0], :src_len[c[0]]], seq[c[1], :src_len[c[1]]]
for i, p in enumerate(seq1):
d1 = torch.sum(torch.square(seq2-p.repeat(len(seq2), 1)), dim=1)
alpha = F.softmin(d1, dim=0).reshape(-1, 1)
snn = torch.sum(alpha.repeat(1, 32)*seq2, dim=0) # soft nearest neighbor
d2 = torch.sum(torch.square(seq1-snn.repeat(len(seq1), 1)), dim=1)
beta = F.softmin(d2, dim=0)
u_id = torch.dot(beta, torch.arange(len(seq1)).cuda().float())
std_id = torch.dot(beta, torch.square(torch.arange(len(seq1)).cuda().float()-u_id))
loss_i+=(torch.square(i-u_id)/std_id)+self.penalty*torch.log(torch.sqrt(std_id))
for j, q in enumerate(seq2):
d1 = torch.sum(torch.square(seq1-q.repeat(len(seq1), 1)), dim=1)
alpha = F.softmin(d1, dim=0).reshape(-1, 1)
snn = torch.sum(alpha.repeat(1, 32)*seq1, dim=0) # soft nearest neighbor
d2 = torch.sum(torch.square(seq2-snn.repeat(len(seq2), 1)), dim=1)
beta = F.softmin(d2, dim=0)
u_id = torch.dot(beta, torch.arange(len(seq2)).cuda().float())
std_id = torch.dot(beta, torch.square(torch.arange(len(seq2)).cuda().float()-u_id))
loss_j+=(torch.square(j-u_id)/std_id)+self.penalty*torch.log(torch.sqrt(std_id))
return (loss_i+loss_j)/len(combinations)
- the code above equals to:
-
one-to-one connections by epochs
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one-to-one connections in training & testing set (in testing set, testing curves are the below sequences)
