State-of-charge estimation of lithium-ion battery for a satellite power management system
Date of Issue2016-05-04
School of Electrical and Electronic Engineering
Satellite Engineering Centre
The lithium-ion battery has become an important energy source for many applications due to its numerous advantages such as high energy density, lack of memory effect, low self-discharging rate, and long cycle life. This also makes it a very promising candidate for pico- and nanosatellites that have space and weight constraint. For high performance and reliability, it is necessary to have accurate knowledge of the present condition of the battery and the remaining battery life. Among the many ways to monitor the battery conditions, estimation of battery’s state-of-charge (SOC) is one of the critical tasks in battery management of the satellite power subsystem. Since the satellite operates at different temperatures throughout the orbit, its operating temperature, which is the most significant factor that affects the SOC estimation, must be taken into consideration. In this thesis, four different SOC estimation methods for a satellite have been progressively proposed and developed. Under different orbital periods, the satellite operates with different subsystems. Taking advantage of different operational scenarios of satellites in different orbits, a SOC estimation method using ampere-hour counting with impulse response reset is proposed and developed. The impulse response is obtained from turning on/off of satellite subsystems and payloads that occur by the satellite operations rather than using an artificial injected pulse. The charging and discharging dependency on current have been considered and taken into account as the charge and discharge rate factors in the method. Moreover, integral resets can be conducted at all SOC levels instead of the traditional fully charged/discharged state. It eliminates the risk of increasing the number of fully charged and discharged cycles thus extending the battery life span. For this approach, it requires a lookup table to store the various impulse responses which vary at different operating temperatures. To further improve the performance, a SOC estimation method using the square root unscented Kalman filter (Sqrt-UKFST) with unit-sphere spherical transform has been developed. The Sqrt-UKFST takes advantage of Jacobian-free linearization, which is one of the major drawbacks of extended Kalman filter (EKF) based methods used in SOC estimation. It has a higher error order (second order) than EKF (first order). When it is compared with the unscented transform, fewer sigma points are needed for estimation of sample mean and covariance by using the spherical unscented transform. This results in 32% lower computational requirements. The satellite’s operating temperature varies across the orbit and it is one of the critical factors that affect the battery parameters. To include the temperature factor, the SOC estimation with a dual square root unscented Kalman filter (DUKFST) has been developed. In this approach, a dual unscented Kalman filter is used to update battery parameters at different temperature through adaptive covariance matching. The temperature effect on open-circuit voltage (OCV) has been modelled in the DUFKST. All Kalman filter (KF) based methods require the knowledge of measurement and process noise. If not determined properly, it can lead to poor performance in terms of filter divergence and instability. A SOC estimation based on particle swarm optimization (PSO) with inverse barrier constraint is proposed to overcome these drawbacks. This method neither needs to linearize the model nor requires the information on measurement and process noise.