Use this URL to cite or link to this record in EThOS: http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.709878
Title: Advanced battery modelling and state estimation methods for electric vehicles
Author: Zhang, Cheng
Awarding Body: Queen's University Belfast
Current Institution: Queen's University Belfast
Date of Award: 2016
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Abstract:
Electric vehicles (EVs) are rapiding gaining popularity worldwide in recent years as a way of replacing the internal combustion engine vehicles to improve fuel efficiency and to reduce emissions in the transport sector. Lithium ion batteries have been widely used in EVs as the power source due to their several advantages, such as high energy/power density, long service life, high efficiency and environmentally friendly features. A battery management system (BMS) is essential in EV applications for safe and efficient operation of the battery pack where hundreds or even thousands of battery cells are connected in series/parallel configuration to fulfil the high power and high voltage needs of the vehicles. This thesis is focused on battery modelling, internal state estimation and control algorithms for BMS applications. In this thesis, a lithium ion battery is firstly characterized experimentally with different load profiles and at different temperature levels using a battery test system. After collecting test data and conducting literature survey, a novel simplified battery thermoelectric model is proposed, which includes an electrical submodel and a thermal submodel. The couplings between battery thermal and electrical behaviours are also captured. A novel hybrid parameter optimization method is proposed for model training by comining the least squares method and a meta-heuristic optimization algorithm. Based on the developed battery model, battery internal state estimation is then studied, such as state of charge and internal temperature, using the extended Kalman filter method. Finally, the proposed battery model and internal state estimation methods are used to develop battery management strategies, in particular for real-time battery thermal management control algorithms. Different controllers are proposed and compared, e.g., PID controller, bang-bang controller and optimal controller, in order to achieve optimal battery thermal control performance.
Supervisor: Not available Sponsor: Not available
Qualification Name: Thesis (Ph.D.) Qualification Level: Doctoral
EThOS ID: uk.bl.ethos.709878  DOI: Not available
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