Lithium-ion battery state of health estimation method based on variational quantum algorithm optimized stacking strategy

This publication has 48 references indexed in Scilit:

• Prediction of transportation energy demand in Türkiye using stacking ensemble models: Methodology and comparative analysis
  Applied Energy, 2023
• Power system transient stability preventive control optimization method driven by Stacking Ensemble Learning
  Energy Reports, 2023
• Distributed quantum multiagent deep meta reinforcement learning for area autonomy energy management of a multiarea microgrid
  Applied Energy, 2023
• A review of machine learning state-of-charge and state-of-health estimation algorithms for lithium-ion batteries
  Energy Reports, 2023
• HFCM-LSTM: A novel hybrid framework for state-of-health estimation of lithium-ion battery
  Energy Reports, 2023
• Quantum computation in power systems: An overview of recent advances
  Energy Reports, 2022
• Lithium-ion battery charging optimization based on electrical, thermal and aging mechanism models
  Energy Reports, 2022
• Online state of health estimation for lithium-ion batteries based on a dual self-attention multivariate time series prediction network
  Energy Reports, 2022
• A review on online state of charge and state of health estimation for lithium-ion batteries in electric vehicles
  Energy Reports, 2021
• A Neural Network Based State-of-Health Estimation of Lithium-ion Battery in Electric Vehicles
  Energy Procedia, 2017