A Transferable Multi-stage Model with Cycling Discrepancy Learning for Lithium-ion Battery State of Health Estimation

• URL: http://arxiv.org/abs/2209.00190v1
• Date: Thu, 1 Sep 2022 02:59:46 GMT
• Title: A Transferable Multi-stage Model with Cycling Discrepancy Learning for Lithium-ion Battery State of Health Estimation
• Authors: Yan Qin and Chau Yuen and Xunyuan Yin and Biao Huang
• Abstract summary: Multiple degradation patterns of a complete life cycle of a battery make it challenging to pursue transfer learning (TL) A transferable multi-stage SOH estimation model is proposed to perform TL across batteries in the same stage, consisting of four steps. The proposed method outperforms its competitive algorithms in various transfer tasks for a run-to-failure benchmark with three batteries.
• Score: 18.980782609740082
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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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