Energy-driven scheduling algorithm for nanosatellite energy harvesting maximization

Abstract The number of tasks that a satellite may execute in orbit is strongly related to the amount of energy its Electrical Power System (EPS) is able to harvest and to store. The manner the stored energy is distributed within the satellite has also a great impact on the CubeSat's overall efficiency. Most CubeSat's EPS do not prioritize energy constraints in their formulation. Unlike that, this work proposes an innovative energy-driven scheduling algorithm based on energy harvesting maximization policy. The energy harvesting circuit is mathematically modeled and the solar panel I-V curves are presented for different temperature and irradiance levels. Considering the models and simulations, the scheduling algorithm is designed to keep solar panels working close to their maximum power point by triggering tasks in the appropriate form. Tasks execution affects battery voltage, which is coupled to the solar panels through a protection circuit. A software based Perturb and Observe strategy allows defining the tasks to be triggered. The scheduling algorithm is tested in FloripaSat, which is an 1U CubeSat. A test apparatus is proposed to emulate solar irradiance variation, considering the satellite movement around the Earth. Tests have been conducted to show that the scheduling algorithm improves the CubeSat energy harvesting capability by 4.48% in a three orbit experiment and up to 8.46% in a single orbit cycle in comparison with the CubeSat operating without the scheduling algorithm.

[1]  S. Sengar,et al.  Maximum Power Point Tracking Algorithms for Photovoltaic System : A Review , 2014 .

[2]  Luca Benini,et al.  Real-time scheduling with regenerative energy , 2006, 18th Euromicro Conference on Real-Time Systems (ECRTS'06).

[3]  LiuYang,et al.  Duty-cycle-aware minimum-energy multicasting in wireless sensor networks , 2013 .

[4]  S. H. Durrani,et al.  Efficient scheduling algorithm for demand-assigned TDMA satellite systems , 1989 .

[5]  Antônio Augusto Fröhlich,et al.  Evaluation of Energy-Efficient Heuristics for ACO-based Routing in Mobile Wireless Sensor Networks , 2015, ArXiv.

[6]  Liu Jin,et al.  A Dynamic Scheduling Method of Earth-Observing Satellites by Employing Rolling Horizon Strategy , 2013, TheScientificWorldJournal.

[7]  Yang Liu,et al.  Duty-Cycle-Aware Minimum-Energy Multicasting in Wireless Sensor Networks , 2010, IEEE/ACM Transactions on Networking.

[8]  James W. Layland,et al.  Scheduling Algorithms for Multiprogramming in a Hard-Real-Time Environment , 1989, JACM.

[9]  Hongrae Kim,et al.  Mission scheduling optimization of SAR satellite constellation for minimizing system response time , 2015 .

[10]  Leonardo Reyneri,et al.  Innovative power management, attitude determination and control tile for CubeSat standard NanoSatellites , 2014 .

[11]  Xiaomin Zhu,et al.  Dynamic Scheduling for Emergency Tasks on Distributed Imaging Satellites with Task Merging , 2014, IEEE Transactions on Parallel and Distributed Systems.

[12]  Eberhard Gill,et al.  Swarm satellite mission scheduling & planning using Hybrid Dynamic Mutation Genetic Algorithm , 2017 .

[13]  Chee Khiang Pang,et al.  Nano-satellite swarm for SAR applications: design and robust scheduling , 2015, IEEE Transactions on Aerospace and Electronic Systems.

[14]  Xiaomin Zhu,et al.  Fault-Tolerant Scheduling for Real-Time Tasks on Multiple Earth-Observation Satellites , 2015, IEEE Transactions on Parallel and Distributed Systems.

[15]  Mehdi Gholizadeh,et al.  Estimation of State of Charge, Unknown Nonlinearities, and State of Health of a Lithium-Ion Battery Based on a Comprehensive Unobservable Model , 2014, IEEE Transactions on Industrial Electronics.

[16]  Vivek Agarwal,et al.  Development and Validation of a Battery Model Useful for Discharging and Charging Power Control and Lifetime Estimation , 2010, IEEE Transactions on Energy Conversion.

[17]  Symeon Chatzinotas,et al.  Multicast Multigroup Precoding and User Scheduling for Frame-Based Satellite Communications , 2014, IEEE Transactions on Wireless Communications.

[18]  Al Globus,et al.  A Comparison of Techniques for Scheduling Earth Observing Satellites , 2004, AAAI.

[19]  Xiaojun Shen,et al.  Optimal energy efficient packet scheduling with arbitrary individual deadline guarantee , 2014, Comput. Networks.

[20]  James Cutler,et al.  Maximizing photovoltaic power generation of a space-dart configured satellite , 2015 .

[21]  C. Ahara,et al.  The Scheduling Problem in Satellite Communications Systems , 1967 .

[22]  Dirk Uwe Sauer,et al.  From accelerated aging tests to a lifetime prediction model: Analyzing lithium-ion batteries , 2013, 2013 World Electric Vehicle Symposium and Exhibition (EVS27).

[23]  Kang G. Shin,et al.  Design and Management of Satellite Power Systems , 2013, 2013 IEEE 34th Real-Time Systems Symposium.

[24]  Shigeru Shimamoto,et al.  Dynamic Scheduling for High Throughput Satellites Employing Priority Code Scheme , 2015, IEEE Access.

[25]  Yonghong Tan,et al.  A dynamic scheduling algorithm for energy harvesting embedded systems , 2016, EURASIP J. Wirel. Commun. Netw..