AUTHORS: Arnaud Sivert, Franck Betin, Bruno Vacossin, Thierry Lequeu, Philippe Dondon
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ABSTRACT: Electric vehicles have a relatively long charging time and a relatively short range. Estimating a vehicle’s consumption and range is therefore crucial. Range will depend on the average speed, top speeds, ascent and descent, battery temperature and battery condition. Judging the range of a vehicle is thus not a directly linear function of the distance to cover. This nonlinearity leads to mistrust, where a user cannot have total confidence in electric vehicles (bicycle, scooter, car ...). Moreover, the fail of only one cell of the battery can reduce drastically the range or even lead the vehicle to stop. The user must be able to diagnose the battery and its state of health to see if the proposed trip is feasible. This article presents a simple strategy to monitor the behavior of the battery during charge and discharge and provides a quick diagnosis of each element and also of the whole battery. Then, it’s possible to determine the range remaining in a vehicle’s battery based on all the above parameters. To find out if a vehicle can make a given journey without recharging, a web application for estimating the vehicle’s consumption was completed and successfully tested.
KEYWORDS: lithium battery, diagnostics, calculating range, balancing, eco marathon, velomobile, ebike
REFERENCES:
[1] J. Neubauer, E. Wood, “The impact of range anxiety and home, workplace, and public charging infrastructure on simulated battery electric vehicle lifetime utility” Journal of Power Sources, 257, 12– 20, 2014
[2] S. Santhanagopalan, K. Smith, J. Neubauer “Design and Analysis of Large Lithium-Ion Battery Systems” 2015 artec house
[3] A. Addahiech “ Modélisation du vieillissement de l’état de santé de batteries lithium pour l’application de véhicule électrique hybride” thèse 2014 Université Bordeaux
[4] J. Wang, J. Purewal, P. Liu “ Degradation of lithium ion batteries employing graphite negatives and nickel-cobalt-manganese oxide+ spinel manganese oxide positives: mécanismes de vieillissement et estimation de la vie ” Journal of Power Sources, 269, 2014
[5] A123 “Battery pack design, validation and assembly” feb 2014 note manufacturer
[6] A. Sivert, F. Betin, S. Carriere, “Management and design of embedded energy for an electric vehicle with low consumption” Symposium de Génie Électrique (SGE’14): EF-EPF-MGE juillet 2014, ENS Cachan.
[7] A. Sivert, F. Betin, B. Vacossin, T. Lequeu, M. Bosson “Optimization of the mass for a lowpower electric vehicle and consumption estimator (e-bike, e-velomobile and e-car)” WSEAS 2015
[8] Web Forum (bentrider) December 2016 http://velorizontal.bbfr.net/t17956-velomobileelectric-leiba-x-stream-iut-aisne
[9] A. Sivert, F. Betin, T. Lequeu, “Pedagogical study of an electric bike with low energy consumption, management and dimensioning of onboard energy: eco marathon” WSEAS, World Scientific and Engineering Academy and Society, 2014.
[10] Application: Estimate of energy consumption of a vehicle 2016
[11] Web site : http://www.ebikemaps.com/ green race http://www.jurassictest.ch/GR/
[12] A. Samba, N. Omar, H. Gualous “ Modélisation Electrothermique 2D d’une batterie lithium-ion de type pouch ” Revue 3EI N°78, oct 2014
[13] N. Omara, M. Monema, Y. Firouza, J. Salminenc “Lithium iron phosphate based battery – Assessment of the aging parameters and development of cycle life model” Applied Energy 113 (2014)
[14] M. Hung, C. Lin, L. Lee, “State-of-charge and state-of-health estimation for lithium-ion batteries based on dynamic impedance technique” Journal of Power Sources, December 2014
