Uppsala’s population and infrastructure is expanding at a fast rate. This results in problems with supplying sufficient electrical power during peak hours such as early mornings and late evenings. One of the many ways to solve this issue is through peak shaving by using parked electrical vehicles as batteries to discharge into the power grid (vehicle-to-grid). In this report, the possibilies for peak shaving during peak hours in a mobility house called Dansmästaren are presented, as well as the possibilities for the vehicle-to-grid technology in the future. Dansmästaren has 60 available parking slots for electric vehicles, and a large central battery available.Through simulations using MATLAB, the results show that it’s possible to achieve a considarable degree of peak shaving, while battery degradation is kept reasonably low. Conclusions regarding vehicle-to-grid in the future are that there is a large potential for Vehicle-to-grid to become an important part of tomorrow’s energy system. However, continued research and development is necessary, as well as bigger focus on the social and economic aspects of this technology. A succesful implementation will require cooperation between the grid owners, the industry and the customers.

Data-driven prediction of battery health has gained increased attention over the past couple of years, in both academia and industry. Accurate early-stage predictions of battery performance would create new opportunities regarding production and use. Using data from only the first 100 cycles, in a data set of 124 cells where lifetimes span between 150 and 2300 cycles, this work combines parametric linear models with non-parametric Gaussian process regression to achieve cycle lifetime predictions with an overall accuracy of 8.8% mean error. This work presents a relevant contribution to current research as this combination of methods is previously unseen when regressing battery lifetime on a high dimensional feature space. The study and the results presented further show that Gaussian process regression can serve as a valuable contributor in future data-driven implementations of battery health predictions. / Datadriven prediktion av batterihälsa har fått ökad uppmärksamhet under de senaste åren, både inom akademin och industrin. Precisa prediktioner i tidigt stadium av batteriprestanda skulle kunna skapa nya möjligheter för produktion och användning. Genom att använda data från endast de första 100 cyklerna, i en datamängd med 124 celler där livslängden sträcker sig mellan 150 och 2300 cykler, kombinerar denna uppsats parametriska linjära modeller med ickeparametrisk Gaussisk processregression för att uppnå livstidsprediktioner med en genomsnittlig noggrannhet om 8.8% fel. Studien utgör ett relevant bidrag till den aktuella forskningen eftersom den använda kombinationen av metoder inte tidigare utnyttjats för regression av batterilivslängd med ett högdimensionellt variabelrum. Studien och de erhållna resultaten visar att regression med hjälp av Gaussiska processer kan bidra i framtida datadrivna implementeringar av prediktion för batterihälsa.