Uncertainty-oriented collaborative learning for on-orbit state-of-health estimation of satellite lithium-ion batteries considering multi-operating conditions

Key Points

• State-of-health estimation shows high accuracy across varying operational conditions and uncertainties.
• Key evidence indicated an improvement in estimation reliability by 25% under multi-operating conditions.
• Analysis of collaborative learning methods enhances state-of-health monitoring for lithium-ion batteries on satellites.
• Supports real-time health assessment and management of satellite batteries in diverse operational settings.