Stochastic state of health estimation for lithium-ion batteries with automated feature fusion using quantum convolutional neural network

This publication has 35 references indexed in Scilit:

• Battery state of health estimation under dynamic operations with physics-driven deep learning
  Applied Energy, 2024
• State of Health (SoH) estimation methods for second life lithium-ion battery—Review and challenges
  Applied Energy, 2024
• Data-driven capacity estimation for lithium-ion batteries with feature matching based transfer learning method
  Applied Energy, 2023
• Configurable sublinear circuits for quantum state preparation
  Quantum Information Processing, 2023
• Data efficient health prognostic for batteries based on sequential information-driven probabilistic neural network
  Applied Energy, 2022
• Implementation for a cloud battery management system based on the CHAIN framework
  Energy and AI, 2021
• Joint Estimation of Inconsistency and State of Health for Series Battery Packs
  Automotive Innovation, 2021
• Joint estimation of battery state-of-charge and state-of-health based on a simplified pseudo-two-dimensional model
  Electrochimica Acta, 2020
• Random forest regression for online capacity estimation of lithium-ion batteries
  Applied Energy, 2018
• A single particle model with chemical/mechanical degradation physics for lithium ion battery State of Health (SOH) estimation
  Applied Energy, 2018