Online Capacity Estimation of Li-on Batteries for Heavy-Duty Electric Vehicles
Tid: To 2019-06-20 kl 09.00 - 10.00
Plats: Seminar room (Rumsnr: A:641), Malvinas väg 10, Q-huset, våningsplan 6, KTH Campus
Respondent: Nikolaos Karavalakis
Opponent: Marwan Salem
Handledare: Rodrigo Gonzalez Vidal (KTH), Johan Lundström (Scania)
Examiner: Cristian Rojas
Abstract: Sustainable transport has lead to recent technological advancements for electric vehicles. A weak component of the electric vehicles is the energy storage units and their efficient operation. A battery management system is usually employed to ensure the safe and efficient operation of the batteries. State of charge (SOC), parameter and capacity estimation are vital functions of such a device. However, the development and performance evaluation of these processes is difficult. In this thesis, capacity estimation algorithms are developed and tested under various scenarios, like parameter initialisation and SOC error compensation. The investigated algorithms are based on recursive version of the least squares method. The validation of the algorithms is performed on data, provided by Scania AB. The experimental results proved that the errors-in-variables (EIV) solution performs overall better than the ordinary recursive least squares in terms of bias compensation and convergence. However, an improved identification of the battery model will eliminate considerable inaccuracies. The M-estimator achieves similar accuracy to the EIV method in terms of bias and outlier removal. However, its convergence speed is undoubtedly moderate. The validation and testing are substantial obstacles in the development of such algorithms due to huge amount of data and long simulations. Further investigation is required in terms of different battery temperatures.