# CubeSat Mission Scheduling Method Considering Operational Reliability
## 1 Introduction
Reference [25] established a mathematical model of irradiation flux on the satellite; detailed the orbit, attitude, and radiation source issues; and predicted the satellite's life span, but the scheduling section of the battery model was also missing and the working window of the task was not considered; ref. [26] included a battery model and imposed constraints on the use of batteries, which can prolong the life of batteries, but there was no reasonable planning for the start-up sequence of tasks, and the optimization objective did not take into account factors affecting the reliable operation of satellites. Although some literature has studied the satellite scheduling model for multi-objective optimization, the existing optimization methods do not take into account the factors that affect the reliable operation of CubeSats, and there is a lack of relevant research on satellite reliability quantification models in the direction of optimization. In this paper, reliability is defined mainly at the level of capacity standby and start-up impact, which are quantified as the state of charge and the number of start-ups, and is optimized as an objective function. In addition, considering that the initial power-up sequence of a task affects the power system operation, it is also included in the optimization of reliability. In practical applications, if the scheduling model lacks a reasonable assessment of the reliability, it will lead to an imbalance between the mission value and reliability of the CubeSat and will affect the service quality and operating life of the CubeSat. Based on [24], this paper takes the CubeSat scheduling method as the research object, establishes a mathematical model of the specific scheduling problem, designs the start-up sequence according to the influence of the CubeSat task sequence allocation on the operation results, gives the CubeSat operation a reliability quantitative index, and proposes a CubeSat scheduling method considering operational reliability. The example takes a 3U CubeSat mission as the optimization object and compares and analyzes the running results of different scheduling schemes. The results show that the CubeSat scheduling method comprehensively considers task priority and operational reliability; it can not only obtain higher mission value but also improve the level of energy storage backup and reduce the number of task start-ups under the premise of meeting task requirements, and adding step-by-step start-up to the scheduling model is more realistic. In conclusion, the main contributions of this work include: (1) A quantitative model of CubeSat operation reliability has been established that can intuitively understand the impact of task sequence allocation in the time window on CubeSat operational reliability and provides a method for CubeSat management and control to evaluate the operational status. (2) A multi-objective optimization model that comprehensively considers the mission value and reliability is established and obtains the optimal scheduling scheme for CubeSats by solving the optimization model to realize the scheduling of the CubeSat mission. The implementation of the scheduling method can balance the mission value and reliability, improve the energy reserve level of the CubeSat, and reduce the surge current caused by simultaneous start-up of tasks. (3) A step-by-step start-up design is proposed that gives a reasonable initial start-up sequence for tasks, solving the problem of lack of step-by-step start-up design in existing scheduling methods, improving the success rate and reliability of the initial power-on of the CubeSat, and facilitating the decision-making of the satellite management and control department. This paper is organized as follows. Section 2 describes the CubeSat scheduling problem. Section 3 presents the formula model of the CubeSat power module. Section 4 demonstrates an improvement to the existing optimization direction and establishes a CubeSat scheduling model considering reliability.