Enhanced adaptive LSTM framework for robust early-life prediction of lithium-ion battery degradation

Results in Engineering 2026 Open access

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Authors: Meilia Safitri, Teguh Bharata Adji, Adha Imam Cahyadi
DOI: 10.1016/j.rineng.2026.109664
Cited by: 1
References: 49

Abstract

• A multicycle feature pipeline captures early-cycle degradation behavior. • EA-LSTM uses adaptive gating and attention to enhance prognostic sensitivity. • KO, KP, and EoL are predicted jointly using only the first 100 cycles. • Strong cross-domain robustness with

Fields of Study & Subjects

Advanced Battery Technologies Research Advancements in Battery Materials Advanced Battery Materials and Technologies

Publication details

Journal / source
Results in Engineering
Publisher
Elsevier BV
Volume
29
Pages
109664–109664
Publication date
20 February 2026
Language
EN