Operational Optimization of the Lithium-Ion Batteries of TerraSAR-X/TanDEM-X

Abstract

After a decade of successful TanDEM-X mission operations, the degradation of the satellite's battery capacity due to ageing has defined a new challenge for DLR's mission operations team. In response, a novel machine-learning strategy has been gradually deployed in the Mission Planning System in order to optimize the battery utilization. The objective of this strategy is twofold, (i) protecting the operational state of the battery, while (ii) maximizing the executed SAR acquisitions under newly introduced planning restrictions. The limits, resulting in the battery utilization optimization, have been communicated to the customers in a user-friendly way in order to assist their future planning, minimizing the number of notexecuted requests due to the new energy and power constraints imposed by the joint TerraSAR-X/TanDEM-X Mission Planning System. In this paper, we (i) detail the quantitative approach to model the satellites' battery behavior in comparison to the previously used physical models, (ii) outline the process of the new machine learning model as implemented in the Mission Planning System, (iii) present the operational results of the model in comparison to satellite telemetry and (iv) discuss the evolution of the machine learning model towards higher accuracy telemetry estimations.

A Novel Modular, Reconfigurable Battery Energy Storage System: Design, Control, and Experimentation

“…Convective L ITHIUM-ION battery energy storage systems (BESSs) have proven themselves as an enabling technology for various applications, including electric cars, electric aircraft, smart grid, and space systems [1][2][3][4]. Despite their high energy density and long cycle life, lithium-ion batteries suffer from safety risks, which trace to the high reactivity of lithium and flammability of the commonly used electrolyte solutions and are exacerbated by side reactions, aging, and degradation [5].…”