Modelling And On Load Parameter Estimation Of Lithium Ion Battery In EV Application

Abstract

The functioning concept of the electric vehicle (EV), as well as its construction and newlineauxiliary components, are examined in this thesis. The impact of various forces on vehicle newlinedynamics and performance with respect to various battery properties is investigated. The newlinebattery performance at various temperatures is reviewed in order to determine the vehicle newlinerange on different driving cycles. With the help of basic electrical components, battery models newlinein the time domain are constructed utilising electrochemical impedance spectroscopy (EIS) newlinetests. Numerous metaheuristic optimization strategies are used to obtain the best-fitting model newlineparameter solutions. Hardware tests are carried out at three different temperatures to develop newlineelectrical equivalent circuit models (EECM) that can depict the dynamic behaviour of the cell newlinein its principal use in EVs. For all of the temperatures examined, the Levenberg-Marquardt newlinetechnique is used to determine the best value for the various model parameters at different newlineSOC levels. The limitations of the linear Kalman filter (KF) are investigated, and extended and newlinedual extended Kalman filters are used for SOC estimation to alleviate the problems. Data from newlineseveral driving cycles, in particular, is acquired in the lab to validate the suggested algorithms, newlinewhich took voltage and current bias into account separately and simultaneously. To validate newlinethe accuracy and superiority of multiple models for this specific purpose, a deep neural (DNN) newlinenetwork based SOC estimation strategy was investigated. Voltage, current, and temperature newlineare the only sensor-based quantities, hence they have been used as input features in the newlinemodels. Various feed forward neural network (FNN), convolution neural network (CNN), and newlinelong-short term memory (LSTM) architectures are proposed, and the best one is chosen. The newlineability of the battery to store and distribute energy decreases with age, lowering the EV s performance. As a result, this thesis investigates the elements that cause battery capacity fading in depth.