A pipeline combining PSO-optimized variational mode decomposition with a CNN-LSTM network estimates battery state of health with reported MAPE as low as 0.26% on one NASA battery, but the evaluation uses non-causal decomposition and lacks a trivial baseline.
Sagpcn: Self-attention graph pooling convolutional network for battery state of health estimation,
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Optimal Signal Decomposition-based Multi-Stage Learning for Battery Health Estimation
A pipeline combining PSO-optimized variational mode decomposition with a CNN-LSTM network estimates battery state of health with reported MAPE as low as 0.26% on one NASA battery, but the evaluation uses non-causal decomposition and lacks a trivial baseline.