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

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 degrees 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.