Kavli Affiliate: Biao Huang
| First 5 Authors: Yan Qin, Chau Yuen, Xunyuan Yin, Biao Huang,
| Summary:
As a significant ingredient regarding health status, data-driven
state-of-health (SOH) estimation has become dominant for lithium-ion batteries
(LiBs). To handle data discrepancy across batteries, current SOH estimation
models engage in transfer learning (TL), which reserves apriori knowledge
gained through reusing partial structures of the offline trained model.
However, multiple degradation patterns of a complete life cycle of a battery
make it challenging to pursue TL. The concept of the stage is introduced to
describe the collection of continuous cycles that present a similar degradation
pattern. A transferable multi-stage SOH estimation model is proposed to perform
TL across batteries in the same stage, consisting of four steps. First, with
identified stage information, raw cycling data from the source battery are
reconstructed into the phase space with high dimensions, exploring hidden
dynamics with limited sensors. Next, domain invariant representation across
cycles in each stage is proposed through cycling discrepancy subspace with
reconstructed data. Third, considering the unbalanced discharge cycles among
different stages, a switching estimation strategy composed of a lightweight
model with the long short-term memory network and a powerful model with the
proposed temporal capsule network is proposed to boost estimation accuracy.
Lastly, an updating scheme compensates for estimation errors when the cycling
consistency of target batteries drifts. The proposed method outperforms its
competitive algorithms in various transfer tasks for a run-to-failure benchmark
with three batteries.
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