An improved particle swarm optimization-adaptive square root Unscented Kalman filter algorithm for accurate state of charge estimation of lithium-ion batteries | Synapse
March 3, 2026
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.