State-of-health estimation for lithium-ion battery via an evolutionary Stacking ensemble learning paradigm of random vector functional link and active-state-tracking long–short-term memory neural network

Accurate estimation of State of Health (SOH) is crucial to ensure optimal performance and safe operation of lithium-ion battery. This paper proposes a Stacking ensemble learning paradigm for SOH estimation. The Stacking ensemble learning increases adaptability to different features by using base lea...

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Veröffentlicht in:	Applied energy 2024-02, Vol.356, p.122417, Article 122417
Hauptverfasser:	Zhang, Yue, Wang, Yeqin, Zhang, Chu, Qiao, Xiujie, Ge, Yida, Li, Xi, Peng, Tian, Nazir, Muhammad Shahzad
Format:	Artikel
Sprache:	eng
Schlagworte:	algorithms ASTLSTM energy Heap-based optimizer lithium batteries neural networks risk RVFL Stacking ensemble learning State of health estimation temperature
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