Extending battery life in CubeSats by charging current control utilizing a long short-term memory network for solar power predictions

• Published in: Journal of power sources Vol. 618; p. 235164
• Main Authors: Knap, Vaclav, Bonvang, Gustav A.P., Fagerlund, Frederik Rentzø, Krøyer, Sune, Nguyen, Kim, Thorsager, Mathias, Tan, Zheng-Hua
• Format: Journal Article
• Language: English
• Published: Elsevier B.V 30-10-2024
• Subjects: Charging strategy; CubeSat; Extended satellite life; Lithium-ion battery; Long short-term memory network; Solar power prediction; Long short-term memory network; Lithium-ion battery; Charging strategy; Solar power prediction; Extended satellite life; CubeSat
• ISSN: 0378-7753
• DOI: 10.1016/j.jpowsour.2024.235164

Recently, there has been a surge in small satellites and CubeSats. A crucial factor limiting the duration of their missions is the lifespan of their batteries. Typically, batteries are charged immediately when there is sufficient power generated from the solar panels. However, this practice results in additional charging stress and degradation due to unnecessarily high current amplitudes. In this work, a distributed charging strategy based on solar power prediction is proposed to mitigate charging stress and thereby extend battery life, ensuring sufficient charging without jeopardizing spacecraft operation. The proposed method for power generation prediction relies on a Long Short-Term Memory (LSTM) network, trained on GOMX-4A satellite telemetry data. The proposed LSTM method performed an order of magnitude better, with a root mean square error (RMSE) of 0.2274 W, while a baseline prediction utilizing a Seasonal Auto-Regressive Moving Average has an RMSE of 1.2406 W. Using the predicted power generation from the LSTM method, the current is distributed using a proposed charging multiplier control, resulting in 72.0882% reduction in the median charging current. A direct possible impact on lithium-ion batteries was evaluated by employing an electrochemical model from the literature, confirming that the proposed strategy effectively reduces degradation caused by lithium plating. Moreover, the capacity fade in the example scenario at 25 °C was reduced by 0.0849%. The extent of degradation reduction will vary according to the required mission profile, the operational conditions, the specific chemistry, and the type of battery in use.
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•A Long Short-Term Memory network is proposed for satellite solar power prediction.•CubeSat telemetry is used for training and benchmarking of the proposed solution.•Charging strategy to extend battery life via reducing current amplitude is proposed.•Reduction of a median charging current of 72.0882% was achieved.•Battery degradation due to lithium plating was reduced.