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Validated degradation models are needed to ensure optimized operation of battery systems. This paper presents a simplified electric vehicle battery degradation model, which estimates the degradation based on vehicle usage daily values. The model quantifies calendar and cycle degradation based on battery temperature, State-of-Charge and odometer. The model results are compared against two datasets of 2.5 years obtained with a LEAF e-plus: on-board State-of-Health readings retrieved from the battery management system and capacity estimations assessed while monitoring battery full recharges. The results show that, after 2.5 years, the model is well-aligned with the capacity estimations, while the on-board readings estimate the State-of-Health 1.3% lower. The obtained State-of-Health from the model after 2.5 years is equal to 95.6%, with calendar degradation being the major driver for the degradation cumulated so far.

Background: The phenomenon of calendar ageing continues to have an impact on battery systems worldwide by causing them to have undesirable operation life and performance. Predicting the degradation in the capacity can identify whether this phenomenon is occurring for a cell and pave the way for placing mechanisms that can circumvent this behaviour. Methods: In this study, the machine learning algorithms, Extreme Gradient Boosting (XGBoost) and artificial neural network (ANN) have been used to predict the calendar ageing data belonging to six types of cell chemistries namely, Lithium Cobalt Oxide, Lithium Iron Phosphate, Lithium Manganese Oxide, Lithium Titanium Oxide, Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide. Results: Prediction results with overall Mean Absolute Percentage Error of 0.0126 have been obtained for XGBoost algorithm. Among these results, Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide type cell chemistries stand out with their mean absolute percentage errors of 0.0035 and 0.0057 respectively. Also, algorithm fitting performance is relatively better for these chemistries at 100% state of charge and 60°C temperature compared to ANN results. ANN algorithm predicts with mean absolute error of approximately 0.0472 overall and 0.0238 and 0.03825 for Nickle Cobalt Aluminum Oxide and Nickle Manganese Cobalt Oxide. The fitting performance of ANN for Nickle Manganese Cobalt Oxide at 100% state of charge and 60°C temperature is especially poor compared to XGBoost. Conclusions: For an electric vehicle battery calendar ageing prediction application, XGBoost can establish itself as the primary choice more easily compared to ANN. The reason is XGBoost’s error rates and fitting performance are more usable for such application especially for Nickel Cobalt Aluminum Oxide and Nickel Manganese Cobalt Oxide chemistries, which are amongst the most demanded cell chemistries for electric vehicle battery packs.

Målet med projektet var att undersöka samt implementera en fysikbaserad DFN modell för att simulera kalender samt cyklingåldrande av litiumbatterier som används i elbilar. Den fysikbaserade modellen var konstruerad baserad på ett Python biblioteket vid namn PyBaMM, vilket till skillnad från datadrivna modeller ger essentiell information om de kemiska processerna inuti batteriet. Den första delen av projektet täcker konceptet av kalenderåldring, vilket inkluderar en jämförelse mellan tre olika tre olika hastighetsbegränsande SEI modeller. Parametrar som påverkar det erhållna resultatet från modellen är identifierade, estimerade, och till slut validerade för att säkerhetsställa att modellen och parametrarna är identifierbara gentemot experimentella data. Resultatet av jämförelsen gav att SEI tillväxt begränsad av litium interstitiell diffusion är den mest optimala modellen att applicera när kalenderåldring för litiumbatterier ska modelleras. Resultaten visade också att endast en parameter, inre SEI litium interstitiell diffusivitet ska justeras för att erhålla optimal anpassning mot experimentella data. Andra delen av projektet använde resultatet från den första delen och litium plätering implementerades som en andraåldringsmekanism som undersöktes under tre olika laddningsprotokoll. Modellen var optimerad och anpassad gentemot experimentella data, där parametervärdet för kinetisk hasighetskonstanten för plätering var estimerad. Den optimerade modellen användes därefter för att erhålla mer information om elektrokemiska variabler för att kunna analysera samt beskriva åldringsprocessen utan att behöva genomföra praktiska laborationer. Resultaten visade att mängden pläterat litium på den negativa elektroden ökade för celler som var exponerade till högre ström under laddningsprocessen, samt när cellerna var laddade vid höga SoC nivåer. Sammanfattningsvis, visade modellen hög potential att representera och evaluera experimentella data, samt tillhandahålla en inblick i elektrokemiska processer och kapacitetsförluster länkade till SEI tillväxt och litium plätering. Däremot, för att erhålla en högre grad noggrannhet av elektrokemiska åldringsmekanismer i litiumbatterier, fler ytterligare mekanismer måste implementeras såsom mekanisk stress av både negativ och positiv elektrod. The aim of this study was to investigate and apply a physics-based DFN model to simulate the calendar and cycling ageing of lithium-ion batteries manufactured for EV applications. The physics-based cell ageing model was constructed based on the open-source software Python library PyBaMM, which in comparison to data-driven models provides more essential information about the chemical process within the battery cell. The first part of the project covers the concept of calendar ageing which includes comparisons between three different rate-limiting SEI growth models. Parameters that affect the output from the physics-based model are isolated, estimated with numerical methods, and lastly validated to ensure that the model and the parameters rep- resent the physics behind the experimental data. It was found that the SEI growth limited by lithium interstitial diffusion is the most optimal model to apply for a physics-based model when modeling calendar ageing. It was also found that the only parameter that should be tuned against experimental data is the inner SEI lithium interstitial diffusivity. The second part of the project utilizes the results from the first part and introduces lithium plating as a second cell ageing mechanism under three different charging protocols. The model was optimized and fitted against experimental data by sweeping the lithium plating kinetic rate constant parameter. The optimized model was thereafter used to generate outputs that more thoroughly can explain the degradation effects of the cell without constructing real-world experiments. Where increased rate of plated lithium could be observed for the cell subjected to higher charging C-rate, and when the cells were charged at high SoC levels. To summarize, the model showed great potential in representing and evaluating the experimental data and providing the project with insight into the electrochemical processes and cell capacity losses of SEI growth and lithium plating. However, in order to achieve a higher accuracy of cell ageing model in relation to the lithium-ion cells used in customer vehicles, several additional cell degradation mechanisms have to be introduced, such as mechanical degradation of the two electrodes.

Transport electrification and energy storage are considered part of the solution to decrease CO2 emissions from the energy and transport sectors. In this context, batteries can be a promising technology, since advances in the last few years have ensured a larger lifetime and better performance. Depending on actual use of the batteries, calendar ageing can be considered as the main origin of degradation in both transport electrification and energy storage since electric vehicles are parked 96 % of the time and battery energy storage stations (BESSs) can remain at a high State of Charge (SoC) for a long time along their lifetime. Therefore, a lifetime model or a degradation model of batteries is necessary to optimally develop an application of these in every sector. In this sense, this paper presents a calendar ageing model of a nickel-manganese-cobalt (NMC) battery, which is used in commercialised electric vehicles. The degradation model presented here is based on the Hermite Cubic Interpolation Polynomial (PCHIP) over an experimental results data set in combination with a power law for modeling the influence of the storing time. In this context, four fitting equations have been compared in search of the most appropriate time depending rate, and the accuracy of the most commonly used model was improved. The storing temperature and SoC have been found to be the most harmful factors in the degradation of these batteries by calendar ageing. The degradation model developed yields of an average root-mean-square error (RMSE) of 0.8 % in capacity fade (CF), while in power fade (PF), the average RMSE has been 2.3 %. The authors thank the Basque Government (project PIBA_2019_1_0098, KK-2022/00100 and GISEL research group IT1522-22) and the University of the Basque Country UPV/EHU (project COLAB19 and PES16/31) for their support.

This paper presents a fully embedded state of health (SoH) estimator for widely used C/LiFePO4 batteries. The SoH estimation study was intended for applications in electric vehicles (EV). C/LiFePO4 cells were aged using pure electric vehicle cycles and were monitored with an automotive battery management system (BMS). An online capacity estimator based on incremental capacity analysis (ICA) is developed. The proposed estimator is robust to depth of discharge (DoD), charging current and temperature variations to satisfy real vehicle requirements. Finally, the SoH estimator tuned on C/LiFePO4 cells from one manufacturer was tested on C/LiFePO4 cells from another LFP (lithium iron phosphate) manufacturer.

29th European Symposium on Reliability of Electron Devices, Failure Physics and Analysis, ESREF, AALBORG, DANEMARK, 01-/10/2018 - 05/10/2018; International audience; The battery is the most sensitive part in the powertrain of full electric vehicles because of its cost and weight. The full electric vehicle range and prize are highly determined by the battery performances. During its lifespan, the battery performances are degraded because of ageing mechanisms. Typically, there are two types of ageing: calendar and cycling ageing. In the electric vehicle application, both types of ageing coexist and interact. In this paper, we report the results of accelerated ageing tests and present a methodology to separate calendar from cycling ageing. With this analysis method, we demonstrate the interaction between calendar and cycling ageing when battery is cycled following representative current profiles of the electric vehicle application.

Lithium-ion batteries are regarded as the key energy storage technology for both e-mobility and stationary renewable energy storage applications. Nevertheless, Lithium-ion batteries are complex energy storage devices, which are characterized by a complex degradation behavior, which affects both their capacity and internal resistance. This paper investigates, based on extended laboratory calendar ageing tests, the degradation of the internal resistance of a lithium-ion battery. The dependence of the internal resistance increase on the temperature and state-of-charge level has been extensive studied and quantified. Based on the obtained laboratory results, an accurate semiempirical lifetime model, which is able to predict with high accuracy the internal resistance increase of the lithium-ion battery over a wide temperature range and for all state-of-charge levels, was proposed and validated.

L'étude du vieillissement des batteries est nécessaire car la dégradation de leurs caractéristiques détermine en grande partie le coût, les performances et l'impact environnemental des véhicules électrifiés, notamment des véhicules 100 % électriques. La méthodologie choisie pour cette thèse consiste en deux étapes bien différenciées, à savoir la caractérisation et la modélisation. Pour la première étape, on s'appuie sur des essais de vieillissement accéléré d'éléments de batterie. Malgré leur caractère accéléré, les campagnes d'essais de vieillissement sont très coûteuses en moyens humains comme matériels : une connaissance à priori des facteurs de vieillissement est nécessaire, soit par le moyen d'études bibliographiques, soit par la réalisation de pré campagnes d'essais. Ces études préalables conduisent à la conception d'un plan d'expériences composé d'un certain nombre d'essais dont les résultats permettront de révéler comment les conditions d'utilisation influencent la dégradation des batteries. Dans la seconde étape, grâce à la connaissance apportée par l'étape de caractérisation, on procède à la modélisation du vieillissement. Celle-ci permet de mettre en évidence des lois de vieillissement qui sont généralisées pour prédire l'évolution des performances d'une batterie soumise à de conditions d'utilisation variables dans le temps. Le modèle de vieillissement qui en résulte peut être utilisé pour concevoir et utiliser d'une manière optimale les batteries des véhicules pour minimiser à la fois la consommation d'énergie et de ressources naturelles. En sachant que la dégradation d'une batterie se produit différemment selon si elle est au repos ou si elle est parcourue par un courant, une difficulté majeure est celle de déterminer comment se combinent les effets du vieillissement calendaire et de cyclage pour un véhicule électrique. Dans les applications "véhicules électriques", les batteries passent une part importante de leur temps au repos et les niveaux de courant pendant leur utilisation sont relativement faibles. Les résultats des essais de vieillissement accéléré réalisés dans cette thèse confirment le caractère non-linéaire de la combinaison des vieillissements calendaire et en cyclage lorsque les batteries suivent des profils d'utilisation similaires à l'application considérée. Le modèle de vieillissement qui est proposé dans le dernier chapitre se veut simple mais efficace. Ainsi il repose sur un faible nombre d'équations (2) et de paramètres (6) et il permet de simuler l'évolution de la capacité d'une cellule soumise à un vieillissement qui combine des périodes de cyclage et de repos. Les exemples d'application de ce modèle démontrent son utilité pour l'établissement de stratégies d'utilisation de batteries dans le but de prolonger leur durée de vie. Studying the ageing of batteries is necessary because the degradation of their features largely determines the cost, the performances and the environmental impact of electric vehicles, particularly of full electric vehicles. The chosen method in this thesis is divided in two distinct phases, namely characterisation and modelling. The first phase is based on accelerated ageing testing of battery cells. Despite being accelerated, ageing test campaigns are expensive in terms of workforce and equipments: an a priori knowledge of ageing factors is necessary, either by the means of bibliographic studies or by performing preliminary test campaigns. These initial studies lead to an experimental design setup including a certain number of ageing tests. The obtained results may reveal the influence of use conditions on the degradation of batteries. In the second phase, the battery ageing is modelled applying the knowledge acquired in the first phase. Here, the ageing laws are generalised to predict the performance degradation of a battery subjected to variable use conditions. The resulting ageing model can be used to optimally design and use the battery in a vehicle by minimising both energy and natural resources consumption. Given that battery degradation occurs in a different way if the battery is in rest condition or if a current flows through, a major challenge is to determine how calendar and cycling ageing effects combine together. In electric vehicle applications, batteries are not used (in rest condition) most of the time and current levels are relatively low when they are used. The results from accelerated ageing tests which have been carried out during this thesis confirm the non-linearity of the combination of calendar and cycling ageing when usage profiles are applied to the batteries. The usage profiles are similar to the considered application: the electric vehicle. In the last chapter of this manuscript a simple but effective ageing model is proposed. It lies in a low number of equations (2) and parameters (6) and enables to simulate the capacity fade of a battery cell subjected to ageing conditions combining cycling and rest periods. The application examples prove the usefulness of this model for the development of battery use strategies for the purpose of extending their lifespan.

Lithium-ion battery second life usage might be an interesting solution for certain applications and it can bring not only environmental benefits but also e.g. lower electric vehicles or hybrid electric vehicles total cost of ownership. However, the electrical performance and performance degradation of the battery systems in the second life application are still unknown what brings significant economic risk and hinders second life battery usage. This work investigates the capacity and power degradation of the 2.5Ah nanophosphate LiFePO4/C cells under different the second life calendar ageing regimes.

2017 Twelfth International Conference on Ecological Vehicles and Renewable Energies - EVER, MONACO, MONACO, 11-/04/2017 - 13/04/2017; International audience; This paper focuses on energy efficiency degradation of two lithium-ion technologies, NMC (NickelManganese Cobalt) and LFP (Lithium Iron Phosphate), under accelerated calendar ageing tests. Results reveal the importance of considering battery ageing in the design phase of electric vehicles, not only for capacity but also for energy efficiency reasons.

International audience; Great research effort is currently devoted towards enhancing performance and operating time of electrical vehicle (EV). Classic storage element in EV consists of accumulators, which allow a relatively high autonomy. However, the power capability of these devices can be considered as insufficient. Supercapacitors (SC) are a new promising storage system used in EV to minimize this lack thanks to their high power capability. One of the ageing cause for SCs for this application is the day/night cycling, that is why we study the impact of thermal cycling on SC performances in this paper. In this article, and in order to have a descriptive sample, impacts of three thermal cycling ageing test conditions are compared with obtained results coming from simple calendar ageing tests at constant temperature and voltage. The comparison is based on physics modelling parameters evolution in the Constant Phase Element (CPE) and Multi-Pore (MP) models. These parameters are calculated based on experimental measurements during calendar ageing tests up to 14,000 h and 6000 h, 10,000 h for thermal cycling tests. Finally, the study of modelling parameters highlights changes in the internal physical structure for both ageing tests.

Lithium-ion batteries are regarded as the key energy storage technology for both e-mobility and stationary renewable energy storage applications. Nevertheless, the Lithium-ion batteries are complex energy storage devices, which are characterized by a complex degradation behavior, which affects both their capacity and internal resistance. This paper investigates, based on extended laboratory calendar ageing tests, the degradation of the internal resistance of a Lithium-ion battery. The dependence of the internal resistance increase on the temperature and state-of-charge level have been extensive studied and quantified. Based on the obtained laboratory results, an accurate semi-empirical lifetime model, which is able to predict with high accuracy the internal resistance increase of the Lithium-ion battery over a wide temperature range and for all state-of-charge levels was proposed and validated.

EVER 2015 - Tenth International Conference on Ecological Vehicles and Renewable Energies, MONACO, MONACO, 31-/03/2015 - 02/04/2015; International audience; This paper presents a methodology for modelling the capacity loss of Lithium ion cells in accelerated ageing tests. We have considered the state-of-charge drift during calendar ageing tests due to irreversible capacity losses.

The study of the behavior of supercapacitors during ageing is required in orderto integrate them in transportation applications. The aim of this thesis is tounderstand and model ageing phenomena observed on supercapacitors. For thispurpose, electrical characterization methodologies and original experimentalprotocols combining various constraints of ageing have been implemented.Measurements of leakage current and combined ageing tests were conducted. Theobtained results were used for developing models allowing the monitoring of theevolution of supercapacitors performance during ageing., Dans le but de l’intégration des supercondensateurs dans des applicationstransport, la connaissance et l’étude de leur comportement au cours du vieillissementest nécessaire. L’objectif de cette thèse est donc la compréhension et lamodélisation des phénomènes de vieillissement observés sur lessupercondensateurs. Pour cela, des méthodologies de caractérisation électriques etdes protocoles expérimentaux originaux combinant les différentes contraintes devieillissement ont été mis en oeuvre. Des mesures du courant de fuite et des tests devieillissement combinés ont été effectués. Les résultats obtenus sont présentés. Ilsont ainsi servis au développement et l’implémentation de modèles permettant le suivide l’évolution des performances des supercondensateurs avec le vieillissement.

The Sustainability Assessment of Second Life Applications of Automotive Batteries (SASLAB) exploratory research project of the European Commission&rsquo, s Joint Research Centre (JRC) aims at developing and applying a methodology to analyse the sustainability of deploying electrified vehicles (xEV) batteries in second use applications. A mapping of industrial demonstration and publicly-funded research projects in the area is presented, followed by an experimental assessment of the capacity and impedance change of lithium-ion cells during calendar and cycle ageing. Fresh cells and cells aged in the laboratory, as well as under real-world driving conditions, have been characterised to understand their application-specific remaining lifetime, beyond the 70% to 80% end-of-first-use criterion. For this purpose, pre-aged cells were examined under duty-cycles that resemble those of second use grid-scale applications.