Maite Etxandi-Santolaya

(English) The adoption of Electric Vehicles (EVs) requires careful management of their resource-intensive batteries to make sure that the transition does not come at a high environmental cost. Technological advancements and cost reductions have led to increased battery capacities and substantial expected residual value in retired batteries, highlighting the importance of circular economy practices, such as sharing and repurposing. Effective decision-making in the battery lifecycle requires precise estimation of End of Life (EoL). Current methods use a universal criterion of 70-80% State of Health (SoH) for defining the EoL, but this overlooks the individualities of each driver and their EV battery capacity. The initial part of the thesis assesses the adequacy of the universal EoL criteria. It begins by analysing the driving performance of EVs to identify new criteria for determining the functional EoL based on specific driving needs. Subsequently, the functional EoL thresholds for various realistic use cases are obtained using driving profiles derived from a proposed synthetic driving cycle model. The findings indicate that underperformance is influenced by factors such as battery capacity, charging frequency, and driving times, highlighting the shortcomings of current EoL estimates. Recognizing the limitations of the fixed EoL criteria, the second part of the thesis introduces a novel approach for EoL estimation using the State of Function (SoF). This approach considers historical driving requirements to define individualize EoL thresholds and employs a data-driven algorithm to predict the SoH under real-world operating conditions (i.e. partial charges). Both aspects, EoL requirements and battery degradation, are included in the proposed definition of the SoF, measuring how far the battery is from underperforming. The thesis results advocate shifting from the 70-80% SoH EoL criterion to the SoF measure to improve EoL estimations. In addition, understanding the functional EoL allows exploration of alternatives like Vehicle to Grid (V2G) and second-life applications to maximize battery usage, reduce residual value when reaching the recycling stage, and minimize environmental impact. (Català) L'aposta pel Vehicle Elèctric (VE) requereix una gestió acurada de les seves bateries, que són intensives en recursos, per assegurar que la transició no tingui un elevat impacte ambiental. Els avanços tecnològics i les reduccions de costos han donat lloc a un augment de les capacitats de les bateries i a un valor residual substancial esperat en bateries retirades, el que destaca la importància de pràctiques d’economia circular com compartir i reutilitzar. La presa de decisions al llarg del cicle de vida de les bateries requereix una estimació precisa del final de la seva vida útil (EoL). Els mètodes actuals utilitzen un criteri universal del 70-80% d'Estat de Salut (SoH) per definir l’'EoL, però això ignora les individualitats de cada conductor i la capacitat de la bateria del seu vehicle. La primera part de la tesi avalua la validesa del criteri universal d'EoL. Primer es valora el rendiment dels vehicles elèctrics durant la conducció per identificar nous criteris per determinar l'EoL funcional basat en necessitats específiques de conducció. Posteriorment, s’obtenen els llindars d'EoL per a diversos casos d’ús realistes utilitzant perfils de conducció derivats d’un model proposat de cicle de conducció sintètic. Els resultats indiquen que les limitacions de rendiment estan influenciades per factors com la capacitat de la bateria, la freqüència de càrrega i els temps de conducció, ressaltant les deficiències de les estimacions actuals d'EoL. Tenint en compte aquestes limitacions, la segona part de la tesi presenta un nou enfocament per a l’estimació de l'EoL utilitzant l'Estat de Funció (SoF). Aquest enfocament considera els requisits històrics de conducció per definir llindars d'EoL individualitzats i un algorisme basat en dades per predir el SoH en condicions d’ús realistes (càrregues parcials). Ambdós aspectes, els requisits d'EoL i la degradació, s’inclouen en la definició proposada de SoF, mesurant fins a quin punt la bateria està prop de presentar un baix rendiment. Els resultats de la tesi defensen substituir el criteri del 70-80% de SoH pel SoF per millorar les estimacions d'EoL. A més, entendre l'EoL funcional permet explorar alternatives com el Vehicle to Grid (V2G) i la segona vida per maximitzar l’ús de la bateria, reduir el valor residual al arribar al reciclatge i minimitzar l’impacte ambiental. (Español) La apuesta por el Vehículo Eléctrico (VE) requiere una gestión cuidadosa de sus baterías, que son intensivas en recursos, para asegurarse de que la transición no tenga un elevado impacto ambiental. Los avances tecnológicos y las reducciones de costes han llevado a un aumento en las capacidades de las baterías y un valor residual sustancial esperado en baterías retiradas, lo que resalta la importancia de prácticas de economía circular, como compartir y reutilizar. La toma de decisiones a lo largo del ciclo de vida de las baterías requiere una estimación precisa del final de su vida útil (EoL). Los métodos actuales utilizan un criterio universal de 70-80% de Estado de Salud (SoH) para definir el EoL, pero esto pasa por alto las individualidades de cada conductor y la capacidad de la batería de su vehículo. La primera parte de la tesis evalúa la validez del criterio universal de EoL. Primero se evalúa el rendimiento de los vehículos eléctricos durante la conducción para identificar nuevos criterios para determinar el EoL...

Effective Electric Vehicle (EV) operation relies on robust State of Health (SoH) estimation algorithms, key for informed battery management. Various algorithms have been proposed to estimate degradation, often depending on full charge segmentation or on conditions that deviate from real-world EV operation. Addressing this gap, this study introduces a cell-level SoH estimation algorithm based on partial charges. The proposed approach employs Health Indicators (HIs) derived from realistic laboratory testing, which contains a variety of voltage ranges during charge to replicate the complexity of real data. The study compares two commonly employed data-driven algorithms, Support Vector Regression (SVR) and Neural Networks (NN) and two estimation voltage ranges, which encompass the second and third Incremental Capacity (IC) peak. Along with the SoH, the battery functionality is estimated through the State of Function (SoF), leveraging degradation data and performance requirements for each tested cell. This enables the definition of an indicator quantifying the proximity of the battery to underperformance in specific applications. In general, the second IC peak shows higher correlation to the SoH. However, the NN SoH algorithm, when trained with high number of observations in the third IC peak, shows the lowest error with an average Root Mean Square Error (RMSE) of 0.00330. Moreover, the translation from SoH to SoF highlights the different performance requirements for each case and supports a functional definition of End of Life (EoL) beyond the fixed threshold.

As the Electric Vehicle market grows, understanding the implications of battery degradation on the driving experience is key to fostering trust among users and improving End of Life estimations. This study analyses various road types, charging behaviours and Electric Vehicle models to evaluate the impact of degradation on the performance. Key indicators related to the speed, acceleration, driving times and regenerative capabilities are obtained for different degradation levels to quantify the performance decay. Results show that the impact is highly dependent on the road type and nominal battery capacity. Vehicles with long and medium ranges show a robust performance for common driving conditions. Short-range vehicles perform adequately in urban and rural road conditions, but on highways, speed and acceleration reductions of up to 6.7 km/h and 3.96 (km/h)/s have been observed. The results of this study suggest that degradation should not be a concern for standard driving conditions and mid- and long-range vehicles currently dominate the market. In addition, the results are used to define a functional End of Life criterion based on performance loss, beyond the oversimplified 70–80% State-of-Health threshold, which does not consider individual requirements.

Electric vehicle battery second-life applications are gaining attention as a way to minimize the environmental impact and increase economic profits. However, the demand for stationary energy storage is expected to be saturated in the near future with these second-life batteries. This fact, in addition to the several technical and economic challenges of second-life batteries, promotes exploring other alternatives. This work analyses and compares these possible approaches in terms of battery degradation and economic profit. The results show that for large batteries, intensive Vehicle to Grid does not cause an early retirement of the battery and allows reducing the underuse of the battery. In addition, for the same battery size, Vehicle to Grid provides more economic profit than second-life applications. Nevertheless, only in a few cases does this appear to be more profitable than simply utilizing the battery for driving. Importantly, this study has shown how the assessment of the second-life tends to be too optimistic as a consequence of assuming a fixed End of Life threshold for the batteries.

Second-life businesses from Electric Vehicle (EV) batteries are gaining attention considering that these batteries are deemed as inappropriate for transport purposes once they reach 80 or 70% of State of Health (SoH). However, the limited number of retired batteries and the trend in battery capacity increase hinder a realistic evaluation of second-life applications. To analyze battery reuse, a closer look at the End of Life (EoL) conditions of these batteries must be taken. This study presents a battery ageing model to estimate the SoH of EV batteries according to their age and mileage. The model is applied to the current retirement characteristics of combustion vehicles to statistically determine the expected SoH at the vehicle EoL. Results indicate that most EVs will reach EoL for reasons other than under-performance. Once retired, most EV batteries will have a SoH higher than 75% within the next 20 years, opening an interesting market for second-life businesses. However, battery reuse is an option that, considering the growing EV market, will rapidly saturate the stationary energy storage demand. Before 2040, most EV batteries will follow streams towards the circular economy, although at some point, they will have to be sent directly to recycling after the vehicular use.

Using batteries after their first life in an Electric Vehicle (EV) represents an opportunity to reduce the environmental impact and increase the economic benefits before recycling the battery. Many different second life applications have been proposed, each with multiple criteria that have to be taken into consideration when deciding the most suitable course of action. In this article, a battery assessment procedure is proposed that consolidates and expands upon the approaches in the literature, and facilitates the decision-making process for a battery after it has reached the end of its first life. The procedure is composed of three stages, including an evaluation of the state of the battery, an evaluation of the technical viability and an economic evaluation. Options for battery configurations are explored (pack direct use, stack of battery packs, module direct use, pack refurbish with modules, pack refurbish with cells). By comparing these configurations with the technical requirements for second life applications, a reader can rapidly understand the tradeoffs and practical strategies for how best to implement second life batteries for their specific application. Lastly, an economic evaluation process is developed to determine the cost of implementing various second life battery configurations and the revenue for different end use applications. An example of the battery assessment procedure is included to demonstrate how it could be carried out.