Optimal Estimation of the Hybrid Vehicles Battery Charge State by Setting the Parameters of the Kalman Filter Based on the Smart Cuckoo Algorithm

Estimation of battery charge state is important in two aspects: First, to increase the economic life of the battery and therefore, decreasing the annual costs of the device maintenance, and second, to display the remaining charge to the consumer of the operator for the purpose of deciding whether to recharge the battery or to connect or disconnect the hybrid automobile to the power grid. One of the battery SOC estimation methods is using the Kalman filter which is a profitable regression filter. This filter estimates the state of a dynamic system through some measuring along with errors. It solves the control problems with a linear first-order regulator. This paper focuses on estimating the battery charge state by precisely setting the parameters of this filter using the Cuckoo Search Algorithm (CSA). Comparing the results of CSA and GA algorithms suggests significant usefulness of the CSA algorithm.