## 1 Introduction  
The European Commission plans to ban the sale of new non zero-emission commercial and passenger light vehicles from 2035 [1]. This decision will accelerate the development of full Battery Electric Vehicles (BEV), which represented 9% (880k units) of sales of light vehicles in Europe over 2021 [2]. This growth needs to be supported by a rapid development of the Electric Vehicle Charging Infrastructure (EVCI). By the end of 2021, there were 225,000 charging points available in the European Union [3] but by 2030, a total of four million charging points are expected [4], for around 34 million BEVs and 14 million Plug-in Hybrid Electric Vehicles (PHEVs). The impacts of this growth on the power grid must to be carefully anticipated: greater electricity production will be needed, and electricity transmission and distribution networks will have to be improved [5, 6]. For example, the French transmission system operator, named RTE, has estimated in a recent study [7] that, in France, the electricity production will have to be increased by 100 TWh in 2050 in a \"reference scenario\", considering that 95% of light vehicles and 21% of duty vehicles are electrified. This energy will represent approximately 15% of the projected electricity consumption in this scenario. Nonetheless, in the same study, RTE concludes that the control of the charge of electric vehicles (EVs) is a \"no regret\" solution from a technical and economic standpoint, i.e., it is profitable in all situations. Many control algorithms and associated systems have been proposed in the literature [8, 9]. For example, authors propose to provide primary reserve [10, 11], i.e., to change the charging power of the EV according to the frequency deviation of the network, or to limit EV load to the power network capacity available [12; 13; 14]. Other authors propose to provide reactive power [15] or to minimize the effects on the grid of the rapid photovoltaics (PV) production output fluctuations due to cloud transients [16] or even to balance wind energy [17]. Other control algorithms aim to minimize the charging cost for the user [18], for a fleet manager [19] or for a parking operator [20]. Some studies also analyze the EV charging when integrated in a microgrid with the consumption of household or/and commercial buildings, solar carport and/or solar plant and eventually an energy storage system [21; 22; 23; 24; 25; 26; 27]. All these articles are either based on synthetic data (i.e., generated from mathematical models only) or use real data (i.e., obtained from physical measures) as inputs for simulations. Such real data are obtained from laboratories or from field scale demonstrators. [PERSON] and al. propose a review of projects that deal with such demonstrators in Europe [28]. Descriptions of other demonstrators may also be found in [29; 30; 31; 32; 33; 34]. Nonetheless, the literature lacks papers or public reports that describe in detail experiments that control the charge of EVs in a field scale demonstrator, except in the following four studies. In [30], the authors described tests that have been conducted from January 2017 to December 2018 in the UK with more than 600 EV drivers involved. During one of these tests, called \"Trial 3\", the users were financially incentivized to let the system charge their EVs outside the peak hours. The results show that the incentive, combined with smart charging, had a significant impact on drivers' behavior. In [35], the authors describe an experimental set-up with commercial EVs and two commercial unidirectional charging stations in the test-site of University Campus Lyngby. The authors demonstrate that it is technically feasible to control the unidirectional charging of EVs to provide primary frequency regulation. The project ChargeForward [36; 31] has managed a set of more than 400 EV-driving households (approximately 250-300 at a given time in the project) that have participated in real-world experiments in the San Francisco Bay area. Several use cases of EV grid integration were studied.