Electric Vehicles as a Mobile Storage Device

International audienceElectricity is a quite recent energy (150 years old) that has developed very much as it allows a flexible use through converters (electrical machines and power electronics). At the beginning, the main use was for lighting and metro. Now, electricity is a major energy for developed countries: 17.7% of the world final energy consumption and 22% for the ECD countries (IEA, 2013a; b, Figure 1), and an economic growth is always linked to an electric consumption growth. Electricity has improved our daily life: washer, dryer, dishwasher, microwaves, internet, TV, air-conditioning, and so on. Humans have become very dependent on electricity consumptions. Nevertheless, electricity is a specific product in the sense that it is a nonmaterial energy, and thus it can only be stored through a costly transformation. Electricity can be classified as a tertiary or secondary energy produced from thermal, potential, hydro (see Volume 5, Chapter XX), wind hces137, or solar energy. For a thermal plant, the primary energy (coal, gas, or uranium) is converted into mechanical energy (secondary energy) by a turbine and is transmitted to the generator to be converted into electricity (tertiary energy). As electricity is difficult to store, it needs an infrastructure to be delivered to consumers: the electrical grid that makes the link between power plants and the consumers through transformers and overhead or cabled lines. At the beginning of the twentieth century, all countries made the choice of the alternating current technology as it allowed—thanks to a key device (the transformer) transmission of high power at high voltages to reduce losses. In the context of emissions reduction (CO2, NOx, etc.), objectives have been given for cleaner energies and the use of more efficient ones. In Europe, there are the “20–20–20” targets: 20% reduction for CO2 emissions, 20% reduction in energy consumption, and 20% increase in efficiency by 2020 (see Volume 6, Chapter XX). To reach these policy goals, electricity is an appropriate vector: it is a flexible energy that can be produced from renewable or CO2-free sources, electrical converters have high efficiency (80–90% for an electric motor) and are bidirectional what makes energy recovery possible for applications such as breaking (trains, vehicles, etc.). Transportation (cars, autobuses, and trucks) is often considered a major contributor to local pollution. Then, constraints for CO2 emissions reduction are more and more severe, especially in Europe. Automakers and their suppliers have optimized their engines with innovations such as start&stop starter/generator, kinetic energy recovery ystems, hybrid systems, and full battery electric vehicles (EVs) and plugin hybrid vehicles. For the two last cases, the energy stored in the batteries will totally or partially come from the electric grid

Identification and machine learning prediction of knee-point and knee-onset in capacity degradation curves of lithium-ion cells

ABSTRACT: High-performance batteries greatly benefit from accurate, early predictions of future capacity loss, to advance the management of the battery and sustain desirable application-specific performance characteristics for as long as possible. Li-ion cells exhibit a slow capacity degradation up to a knee-point, after which the degradation accelerates rapidly until the cell’s End-of-Life. Using capacity degradation data, we propose a robust method to identify the knee-point within capacity fade curves. In a new approach to knee research, we propose the concept ‘knee-onset’, marking the beginning of the nonlinear degradation, and provide a simple and robust identification mechanism for it. We link cycle life, knee-point and knee-onset, where predicting/identifying one promptly reveals the others. On data featuring continuous high C-rate cycling (1C–8C), we show that, on average, the knee-point occurs at 95% capacity under these conditions and the knee-onset at 97.1% capacity, with knee and its onset on average 108 cycles apart. After the critical identification step, we employ machine learning (ML) techniques for early prediction of the knee-point and knee-onset. Our models predict knee-point and knee-onset quantitatively with 9.4% error using only information from the first 50 cycles of the cells’ life. Our models use the knee-point predictions to classify the cells’ expected cycle lives as short, medium or long with 88–90% accuracy using only information from the first 3–5 cycles. Our accuracy levels are on par with existing literature for End-of-Life prediction (requiring information from 100-cycles), nonetheless, we address the more complex problem of knee prediction. All estimations are enriched with confidence/credibility metrics. The uncertainty regarding the ML model’s estimations is quantified through prediction intervals. These yield risk-criteria insurers and manufacturers of energy storage applications can use for battery warranties. Our classification model provides a tool for cell manufacturers to speed up the validation of cell production techniques

DREMUS:A Data-Restricted Multi-Physics Simulation Model for Lithium-Ion Battery Storage

This paper presents a modelling approach to support the techno-economic analysis of Li-Ion battery energy storage systems (BESS) for third party organisations considering the purchase or use of BESS but lacking the detailed knowledge of battery operation and degradation. It takes into account the severe data-limitations and provides the best possible approximation for its long-term electrical, thermal and ageing performance. This is achieved by constructing flexible and scalable ageing models from experimental data based on manufacturer's datasheets, warranties and manuals as key inputs. The precision of the individual models has been determined using experimental data and has been found with <8 % normalised root-mean-square deviation (NRMSD) in all cases to be sufficiently accurate. Through linearization methods, this model is able to compare the long-term performance of BESS and quantify the degradative impact of specific charge/discharge mission profiles, which improves the tangibility of BESS as value generating asset

Réduction de modèles thermiques par la théorie des réseaux, application à la surveillance d'une machine asynchrone par couplage d'un modèle thermique réduit avec un schéma équivalent électrique

UNE MACHINE ELECTRIQUE EST LE SIEGE DE DISSIPATIONS ET UNE TROP FORTE ELEVATION DE TEMPERATURE PEUT ENTRAINER UN ENDOMMAGEMENT IRREVERSIBLE. NOTRE OBJECTIF EST DE MONTRER LA FAISABILITE D'UN SYSTEME NUMERIQUE DE SURVEILLANCE CONTINUE DES TEMPERATURES ET DES PERTES D'UNE MACHINE ELECTRIQUE FONCTIONNANT EN TEMPS REEL. UNE MACHINE ASYNCHRONE DE 5,5 KW, 4 POLES A ETE ASSEMBLEE ET EQUIPEE D'UNE SOIXANTAINE DE THERMISTANCES, Y COMPRIS AU ROTOR, PUIS MONTEE SUR UN BANC D'ESSAIS INSTRUMENTE. UN MODELE THERMIQUE NODAL DETAILLE D'ENVIRON 1200 NUDS A ETE CONSTRUIT SUIVANT LES PLANS DE LA MACHINE. LE MODELE THERMIQUE A ETE RECALE A PARTIR D'ESSAIS SUR LA MACHINE. UNE METHODE ORIGINALE DE REDUCTION DE MODELES THERMIQUES PAR LA THEORIE DES RESEAUX A ETE DEVELOPPEE, INCLUANT DEUX ETAPES D'OPTIMISATION DES REPONSES DYNAMIQUES POUR LE CHOIX DES NUDS A CONSERVER ET POUR LA VALEUR DE CERTAINS ELEMENTS DU MODELE REDUIT. UN SCHEMA EQUIVALENT ELECTRIQUE DE CALCUL DES PERTES DE LA MACHINE ASYNCHRONE A EGALEMENT ETE DEVELOPPE A PARTIR D'UN SCHEMA CLASSIQUE ET AUGMENTE PAR LA PRISE EN COMPTE DE PHENOMENES SUPPLEMENTAIRES. LE SYSTEME DE PREDICTION EN TEMPS REEL DES TEMPERATURES ET DES PERTES A FINALEMENT ETE CONSTRUIT PAR LE COUPLAGE DU MODELE THERMIQUE REDUIT ET DU MODELE ELECTRIQUE.POITIERS-BU Sciences (861942102) / SudocNANCY/VANDOEUVRE-INPL (545472102) / SudocSudocFranceF