State-of-Charge Estimation Using Particle Swarm Optimization with Inverse Barrier Constraint in a Nanosatellite

Lithium ion batteries have become the preferred choice for many applications due to their high energy density and low self-discharge rate. State-of-charge (SOC) information is required in many applications to optimize and safeguard the performance of the batteries. Different methods of SOC estimation such as Ampere counting and model-based estimation have been used in SOC estimation. Among the model based estimation, Kalman filter based method is one of the most commonly used method. However, it requires linearization, an accurate battery model and information on measurement and process noise. In this paper, a SOC estimation based on particle swarm optimization (PSO) with inverse barrier constraint is proposed. This method overcomes the need to linearize the model and does not require the information on measurement and process noise. The proposed method has been verified experimentally. From the experimental results, the root mean square error (RMSE) is 1.03% and absolute maximum error is 3.35%.