A Machine Learning Approach for the Smart Charging of Electric Vehicles
|Authors:||Lopez, Karol Lina|
|Abstract:||With the increasing adoption of electric vehicles, there is an interest to use dynamic tariffs where the price depends on the current demand, encouraging users to charge their vehicles in periods of low demand, avoiding electricity peaks that may exceed the installed capacity. The issue an electric vehicle user must tackle is that it should ensure that its electric power is sufficient for its trips and that the recharge periods correspond to periods where the price of electricity is low. Most current charge scheduling approaches assume a perfect knowledge of the future prices and car usage, which hinders their applicability in practice. This thesis considers the modelling of the intelligent recharge of electric vehicles to determine, during the connection sessions, the times when the vehicle may be charged in order to minimize the overall energy cost. The thesis has four main contributions: 1) Optimum electric vehicle recharge model to generate a series of decisions using full knowledge of the price of electricity and energy used using dynamic programming as a method of optimization. 2) Creation of an information system model which includes variables relevant to the recharging model of electric vehicles in a framework data-driven. 3) Method of selecting relevant data using the stratification by clusters which can significantly decrease the time required to train forecasting models with results close to those obtained using the complete dataset. 4) Classification model which allows the determination of whether or not to charge the vehicle using machine learning models that can generate, in real time, a near-optimal recharge decision without considering perfect knowledge of the future information. We demonstrated how combining an offline optimization method, such as dynamic programming with machine learning models and a coherent information system can provide a solution very close to the global optimum without loss of applicability in real-world. Moreover, the versatility of the proposed approach allows the consideration of the integration of a larger set of variables at the input of the model, as well as other actions such as for example supplying energy to the network to further help reducing demand peaks which could be useful in a vehicle-to-grid context (V2G).|
|Document Type:||Thèse de doctorat|
|Open Access Date:||7 May 2019|
|Collection:||Thèses et mémoires|
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