EV CHARGING WITH LIMITED POWER SOURCES
Creators: name: Louis Rasolonjanahary, Manan'Iarivo
Publisher: name: University of Lincoln
Year: 2024
Subjects: H620 - Electrical engineering
H221 - Energy resources

description: Following the announcement of the UK government that the sales of all new petrol and diesel cars and vans would end in 2030, all UK vehicles on the road will be fed from non-fossil fuelled sources. However, the intermittent nature of some of these non-fossils fuelled sources leads to the need for energy storage equipment to assist the grid if required. Due to the requirements for charging Electric Vehicles (EVs) and the ability to store charge, the volume of EVs that are expected can pose advantages as well as possible supply disadvantages. Specifically, as an advantage, EVs can participate in balancing the grid using the concept of Vehicle-to-Grid (V2G) technology, albeit single-phase EV charging needs to be avoided in general to prevent unbalance. Also, challenging is the statistical nature of when vehicles will require charging and how much charge will be needed. Again, much of this can be ameliorated with sufficient energy storage, but this is typically very expensive and unlikely to satisfy all requirements in the near future. Dynamic constraints of available supply power for charging is also a key issue, and one which this thesis in particular wishes to address by investigating appropriate charging control methods for multi-vehicle bays. In particular, the scheduling of multi-bay EV charging stations using variants of Model Predictive Control (MPC) is investigated. The use of predictive scheduling schemes for the charging of multiple EVs when the total network power is constrained allows the accommodation of multi-vehicle charging scenarios and priority scheduling based on economic (payment) and battery charge state, among other scenarios. The techniques are particularly relevant to small islanded multi-bay residential or commercial (e.g. supermarket) charging stations. For public charging stations, the developed techniques can be used to prevent excessive queuing. The impact of various charging regimes on non-stiff grid supplies is also investigated to show that such techniques are also applicable to contributing to widely distributed and load levelling initiatives. In addition, whilst being excited during the charging process, it is possible to ‘interrogate’ the status of the battery charge and its impedance. This is also investigated. It is shown that MPC can be used in the coordination of EV charging to mitigate the negative impacts of EVs on the grid. It can be used in power balancing to alleviate the instantaneous demand of power from EVs. It allows with specific criteria to deal the queuing at public charging points.The battery state of health (SOH) and state of charge (SOC) can be derived from the identification algorithm using a second order battery model. This algorithm which does not require prior knowledge of the battery model can be implemented within the BMS on a low-cost processor and, allows an online determination of SOH and SOC.There will be enough power to feed EVs in 2035. An annual energy consumption of 460 TWh against a production of 484 TWh has been estimated. This has been estimated through the amount of existing power plants, including nuclear one, the trend in the development of renewable plant.It is widely expected that the mass charging of EVs could have a significant impact on the power quality of the grid network. The research content of the thesis therefore concludes with an investigation on the choice of converters allowing EVs to connect to the grid with less negative impacts.