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|Title:||Optimisation model of intelligent charging strategies for battery electric vehicles considering the power system and battery ageing||Authors:||Trippe, Annette Erika||Keywords:||DRNTU::Engineering::Electrical and electronic engineering::Electric power||Issue Date:||2018||Source:||Trippe, A. E. (2018). Optimisation model of intelligent charging strategies for battery electric vehicles considering the power system and battery ageing. Doctoral thesis, Nanyang Technological University, Singapore.||Abstract:||The emergence and upswing of battery electric vehicles fuels discussion and research on the impact of those on the power system and how they can be used beneficially. On the other hand, the battery is very sensitive to different modes of operation and can age rapidly. This can lead to high losses in value of the electric vehicle because the battery accounts for a substantial share in the cost. In this work, an optimisation model is developed in order to generate intelligent charging strategies for battery electric vehicles. The model considers both electricity price and battery ageing and thereby allocates charging strategies representing the optimal trade-off between electricity price controlled charging and battery lifetime. A mobility model is elaborated to simulate the energy consumption of the respective vehicles as well as the driving and parking schedules of the users. The simulated travel schedules and energy consumption serve as input for the optimisation model of intelligent charging strategies. Experimental data of battery ageing tests, designed to mirror the operation of lithium-ion batteries in electric vehicles, are the basis for a comprehensive battery ageing model. Both cycle and calendar ageing are examined and the influence of the state of charge, charge rate, as well as range of operation on battery ageing is investigated. A calendar ageing function as well as a three-dimensional cycle ageing function are derived, modelling the battery ageing within the optimisation. The charging optimisation model minimises the total charging costs, consisting of charging electricity cost and battery ageing cost. The mathematical optimisation problem is initially formulated as a mixed-integer non-linear programme and transformed into a mixed-integer linear programme by means of piecewise linear approximation and other linearisation techniques. The charging optimisation model is applied to a sample of 300 battery electric vehicles and different scenarios are computed and analysed. The battery ageing cost accounts for 13% to 45% of the total charging costs for the different scenarios, underlining the importance of the inclusion of battery ageing into the optimisation of charging strategies. The optimal operating range lies between a battery state of charge of 10% to 50% in most cases. Charging times coincide with times of low electricity prices, usually correlated to valleys in the electricity demand. Almost no fast charging is applied, indicating that the higher battery ageing cost due to fast charging cannot be outweighed by a reduction in electricity cost when charging more energy during low-priced periods.||URI:||https://hdl.handle.net/10356/88120
|DOI:||10.32657/10220/45666||Fulltext Permission:||open||Fulltext Availability:||With Fulltext|
|Appears in Collections:||IGS Theses|
Updated on Nov 25, 2020
Updated on Nov 25, 2020
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