The inconvenient nature of non‐ideal charging characteristics is demonstrated from a power system point of view. A new adaptive charging algorithm that accounts for non‐ideal charging characteristics is introduced. The proposed algorithm increases the local network capacity utilization rate and reduces charging times. The first unique element of the charging algorithm is exploitation of the measured charging currents instead of ideal or predefined values. The second novelty is the introduction of a short‐term memory called expected charging currents. This makes the algorithm capable of adapting to the unique charging characteristics of each vehicle individually without the necessity to obtain any information from the vehicle or the user. The proposed algorithm caters to various non‐idealities, such as phase unbalances or the offset between the current set point and the real charging current but is still relatively simple and computationally light. The algorithm is compatible with charging standard IEC 61851 and is validated under different test cases with commercial electric vehicles.

The sub-aggregation of electric vehicles provides significant potential to power systems in the form of ancillary services. This means with smart charging it is possible to shift loads from peak to off-peak hours. For the flexibility from privately owned electric vehicles to be offered to the electricity market, customer participation is crucial; however, the impacts of sub-aggregation on customers have not been studied thoroughly. In this paper, charging data covering over 80,000 real-world charging sessions from various commercial charging sites are introduced and the charging characteristics are analyzed. Importantly, a method for finding an optimal pattern for load reduction with the least impact on customers is presented. We suggest an optimal schedule for load reduction from the customer viewpoint at different types of charging sites, including public car parks, offices, residential sites, and shopping centers. The findings indicate that residential and office charging sites offer the greatest potential for load reduction with the least impact on customers. The most flexibility is available during peak charging hours, which on average are at 08:00 at car parks, 07:30 at office sites, 19:00 at residential sites, and 10:00 at shopping centers.

As electric vehicles (EVs) are emerging, smart and adaptive charging algorithms have become necessary to ensure safe and efficient operation of the grid. In the scientific literature, most of the proposed charging control algorithms focus solely on EV usage‐related behaviour, while the charging characteristics of EVs are overlooked. Herein, realistic charging characteristics are illustrated and discussed. More notably, to overcome the issues caused by the non‐idealities in charging characteristics, a new adaptive charging characteristics expectation algorithm is proposed. The objective of this algorithm is to enable accurate estimation of the non‐ideal charging characteristics. This can be used to reallocate any unused charging capacity and to ensure that the intended total capacity is used effectively. The effectiveness of the proposed algorithm is demonstrated using hardware‐in‐the‐loop simulations with commercial EVs and real charging data. The results show that the proposed algorithm achieves an 88%–97% capacity usage rate, while the current benchmark solution achieves only 45%.

This study presents a practical control method for electric vehicle (EV) charging optimisation for detached and attached houses. The developed EV charging control method utilises real‐time measurements to minimise charging costs of up to two EVs in a single household. Since some Finnish distribution system operators have already launched peak power‐based distribution tariffs for small‐scale customers and because there is a lot of discussion on this kind of tariff development, the control method considers peak power‐based charges. Additionally, the proposed smart charging control method utilises charging current measurements as feedback to reallocate unused charging capacity if an EV does not utilise the whole capacity allocated for it. The control method is implemented and tested with commercial EVs. The conducted hardware‐in‐the‐loop simulations and measurements confirm that the control method works as intended. The proposed smart charging control reduces EV charging electricity distribution costs around 60% when compared to the uncontrolled EV charging.

The adaptive charging algorithms of today divide the available charging capacity of a charging site between the electric vehicles without knowing how much current each vehicle draws in reality. Thus, they are not able to detect deviations between the current set point at the charging station and the real charging current. This leads to a situation where the charging capacity of the charging site is not used optimally. This paper presents an algorithm including a novel feature, Expected Characteristic Expectation and tested under realistic circumstances. It is demonstrated that the proposed algorithm enhances the adaptability of the charging site, increasing the efficiency of the used network capacity up to about 2 kWh per charging point per day in comparison with the previous benchmark algorithm. The algorithm is able to increase the average monetary benefits of the charging operators by up to around 5.8%, that is 0.6 € per charging point per day. No input, such as departure time, is required from the user. The proposed algorithm has been tested with real electric vehicles and charging stations and is compatible with the IEC 61851 charging standard. The charging algorithm is applicable in practice as it is described in this paper.