State of charge estimation for lithium-ion batteries connected in series using two sigma point Kalman filters

This paper proposes a method to estimate state of charge (SoC) for Lithium-ion battery pack (LIB) with 𝑁 series-connected cells. The cell’s model is represented by a second-order equivalent circuit model taking into account the measurement disturbances and the current sensor bias. By using two sigma point Kalman filters (SPKF), the SoC of cells in the pack is calculated by the sum of the pack’s average SoC estimated by the first SPKF and SoC differences estimated by the second SPKF. The advantage of this method is the SoC estimation algorithm performed only two times instead of 𝑁 times in each sampling time interval, so the computational burden is reduced. The test of the proposed SoC estimation algorithm for 7 samsung ICR18650 Lithium-ion battery cells connected in series is implemented in the continuous charge and discharge scenario in one hour time. The estimated SoCs of the cells in the pack are quite accurate, the 3-sigma criterion of estimated SoC error distributions is 0.5%.

Research on an Obstacle Avoidance Method for UAV

With the continuous development of UAV technology, UAV has been widely used in various industries. In the flight process of UAV, UAV often changes the given path because of obstacles (including static nonliving body and moving living body). According to the properties of obstacles and the characteristics of UAV, standard Kalman filter is used for nonmaneuvering targets, and sigma point Kalman filter is used for maneuvering targets. In the aspect of obstacle avoidance, the minimum search method is used to get the initial population of local programming. Then, the improved genetic algorithm is run. Combined with the predicted obstacle features, the local planning path can be obtained. Finally, the local planning path and global planning path are combined to generate the planning path with new obstacles. At the end of the paper, the obstacle avoidance strategies of static and moving obstacles are simulated. The simulation results show that this method has fast convergence speed and good feasibility and can flexibly deal with the obstacle avoidance and local path planning of various new obstacles.