2026-07-09T03:09:49Zhttps://ebiltegia.mondragon.edu/oai/request

oai:ebiltegia.mondragon.edu:20.500.11984/145722026-06-18T06:15:43Zcom_20.500.11984_473com_20.500.11984_14090col_20.500.11984_478

Dorronsoro, Xabier de Castro, Ricardo Varela Barreras, Jorge GARAYALDE, ERIK IRAOLA, UNAI 2026-06-17T13:05:38Z 2026-06-17T13:05:38Z 2025 0306-2619 https://hdl.handle.net/20.500.11984/14572 This paper presents an aging-rate aware nonlinear model predictive control (MPC) strategy for battery energy storage systems, integrating a semi-empirical, experimentally validated electro-thermal and degradation model to account for both calendar and cycle aging factors, often neglected in conventional energy management approaches. A key contribution is the introduction of a adaptive weighting method that dynamically adjusts the weights of the MPC cost function according to the battery's aging state, primarily driven by time-dependent degradation factors. This adaptive mechanism improves control decisions across varying prediction horizons, leading to reductions in both battery degradation and total operating costs by up to 262.7 % and 44.51 %, respectively, when compared to a standard MPC. eng © 2025 Elsevier Cost Derating Lifetime extension Lithium-ion battery Model predictive control Optimization Battery aging-aware adaptive model predictive control based on coupled semi-empirical electro-thermal and aging models