Ursúa Rubio, Alfredo
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Ursúa Rubio
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Alfredo
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Ingeniería Eléctrica, Electrónica y de Comunicación
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ISC. Institute of Smart Cities
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Publication Open Access Health indicator selection for state of health estimation of second-life lithium-ion batteries under extended ageing(Elsevier, 2022) Braco Sola, Elisa; San Martín Biurrun, Idoia; Sanchis Gúrpide, Pablo; Ursúa Rubio, Alfredo; Stroe, Daniel-Ioan; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Institute of Smart Cities - ISC; Ingeniería Eléctrica, Electrónica y de Comunicación; Gobierno de Navarra / Nafarroako Gobernua; Universidad Pública de Navarra / Nafarroako Unibertsitate PublikoaNowadays, the economic viability of second-life (SL) Li-ion batteries from electric vehicles is still uncertain. Degradation assessment optimization is key to reduce costs in SL market not only at the repurposing stage, but also during SL lifetime. As an indicator of the ageing condition of the batteries, state of health (SOH) is currently a major research topic, and its estimation has emerged as an alternative to traditional characterization tests. In an initial stage, all SOH estimation methods require the extraction of health indicators (HIs), which influence algorithm complexity and on-board implementation. Nevertheless, a literature gap has been identified in the assessment of HIs for reused Li-ion batteries. This contribution targets this issue by analysing 58 HIs obtained from incremental capacity analysis, partial charging, constant current and constant voltage stage, and internal resistance. Six Nissan Leaf SL modules were aged under extended cycling testing, covering a SOH range from 71.2 % to 24.4 %. Results show that the best HI at the repurposing stage was obtained through incremental capacity analysis, with 0.2 % of RMSE. During all SL use, partial charge is found to be the best method, with less than 2.0 % of RMSE. SOH is also estimated using the best HI and different algorithms. Linear regression is found to overcome more complex options with similar estimation accuracy and significantly lower computation times. Hence, the importance of analysing and selecting a good SL HI is highlighted, given that this made it possible to obtain accurate SOH estimation results with a simple algorithm.Publication Open Access State of health estimation of second-life lithium-ion batteries under real profile operation(Elsevier, 2022) Braco Sola, Elisa; San Martín Biurrun, Idoia; Sanchis Gúrpide, Pablo; Ursúa Rubio, Alfredo; Stroe, Daniel-Ioan; Ingeniaritza Elektrikoa, Elektronikoaren eta Telekomunikazio Ingeniaritzaren; Institute of Smart Cities - ISC; Ingeniería Eléctrica, Electrónica y de Comunicación; Gobierno de Navarra / Nafarroako Gobernua; Universidad Pública de Navarra / Nafarroako Unibertsitate PublikoaThe economic viability of second-life (SL) Li-ion batteries from electric vehicles (EVs) is still uncertain nowadays. Assessing the internal state of reused cells is key not only at the repurposing stage but also during their SL operation. As an alternative of the traditional capacity tests used to this end, the estimation of State of Health (SOH) allows to reduce the testing time and the need of equipment, thereby reinforcing the economic success of SL batteries. However, the estimation of SOH in real SL operation has been rarely analysed in literature. This contribution aims thus to cover this gap, by focusing on the experimental assessment of SOH estimation in reused modules from Nissan Leaf EVs under two SL scenarios: a residential household with self-consumption and a fast charge station for EVs. By means of partial charge and experimental data from cycling and calendar ageing tests, accuracy and robustness of health indicators is firstly assessed. Then, SOH estimation is carried out using real profiles, covering a SOH range from 91.3 to 31%. Offline assessment led to RMSE values of 0.6% in the residential profile and 0.8% in the fast charge station, with a reduction in testing times of 85% compared to a full capacity test. In order to avoid the interruption of battery operation, online assessment in profiles was also analysed, obtaining RMSE values below 1.3% and 3.6% in the residential and charging station scenarios, respectively. Therefore, the feasibility of SOH estimation in SL profiles is highlighted, as it allows to get accurate results reducing testing times or even without interrupting normal operation.