An improved particle swarm optimization-adaptive square root Unscented Kalman filter algorithm for accurate state of charge estimation of lithium-ion batteries

Key Points

• Accurate state of charge estimation is crucial for optimizing lithium-ion battery performance.
• The algorithm improves estimation accuracy by combining particle swarm optimization with adaptive square root unscented Kalman filtering.
• Assessment using performance metrics shows significant enhancements in estimation compared to traditional methods.
• This algorithm supports effective management of battery systems, leading to better energy efficiency and longevity.