One of the interesting features of the end-user App of ELECTRIFIC is that it allows to introduce easily all the details needed to provide the user with the best solutions, whether if he/she is an electric car sharer or an electric car owner. Car sharers do not always know the specifications of the vehicle they are driving, but it is as easy as to enter the license plate. On the other hand, if they are owners, they can select their vehicle in the list and adapt the details to those of their vehicle.

A direct way to ease grid-pressure, decrease an EV’s CO2 – emissions and increase its range is more energy efficient driving. Driving smooth rather than acceleration-prone saves up to 50% of energy – in EV’s and CEV alike.

To help maintain a smooth style, EV’s are equipped with an energy-efficient driving modes that limit acceleration and discourage high speeds. A trial with more than 200 monitored drives in Renault Zoé’s tested two ways to promote the use of the eco-mode.


When the mode was activated at the beginning of the drive, i.e. when it was the default for the drive, it was active 70% of the drive time. In contrast, when the mode was not the default for the drive, it was active only 10% of the drive time. ELECTRIFIC solutions will therefore be conscious about setting defaults.

In this trial analysis, two EVs of type Nissan Leaf are 

used. They have the same age, mileage and battery health (SoH, State of Health). The homogenous EV usage is guaranteed, as both EVs are used by colleagues of THD (Technische Hochschule Deggendorf) to commute to work daily and each week they switch the EVs between them.

The trial consists in seeing the influence of the type of charging. That is why one of the EVs is strictly charged slow and the other one fast. Slow means power less or equal to 3.65 kW and Fast is power equal or more than 20 kW. Both EVs are equipped with data acquisition systems of THD in order to analyse driving data later on.


Hypothesis: Fast and slow charging influence battery degradation differently

It is assumed, that fast charging is worsening the battery health state

On the contrary, slow charging could prolonger or improve the health


click to enlarge


The graph on the right shows the change of the SoH in steps of two-weeks

Difference of SoH between slow charged and fast charged EV (e.g. positive values: SoH of slow charged EV higher then SoH of fast charged EV)

Ambient temperature development in two-week steps
(measured by the EVs)

After a half year, the battery of the fast charged EV reaches its initial health state

During the same period, the SoH of the slow charged EV battery improves by 1 %


SoH decreases, if ambient temperature is high

SoH increases, if ambient temperature is low





If temperature is high, slow charging is improving SoH

Ff temperature is low, fast charging is improving SoH




PREDICTION for second half of trial

Fast charged battery SoH could improve during winter, but drop in the spring

It is assumed that fast charging is more harmful to EV batteries than slow charging

The slow charged battery SoH could also slowly decrease

Dynamic pricing of charging services can help balance the needs of EV drivers, charging station operators and grid operators. As an input, the charging station receives charging requests created as users plans their trips. Based on the predicted demand, existing bookings, availability of power and its cost, the pricing algorithm determines the price for the incoming charging requests. Users then decide, whether they accept the offered price or whether they will charge elsewhere or at different time. The dynamic pricing model is based on solving Markov Decision Process, framework for decision making in stochastic environments.

Dynamic pricing using Markov Decision Processes achieves much better allocation efficiency and revenue than currently most common flat rate pricing methods. Changing price is used to incentivize charging when charging resources are plentiful and discourage charging when they are scarce. Additionally, dynamic pricing can proactively help prevent electrical grid failures with same incentives.

As an input, the EV user needs to define his/her activities. The activities are defined by their time (duration, earliest start, latest end) and spatial attributes.  The planning algorithm then selects the best ordering of the activities together with the locations and routes between the activities. The algorithm also considers the limited range of the EV and selects the best option where, when and how (fast/slow) to charge. The algorithm is a heuristic forward state space search based on multi-objective A* and is able to find the best plan according to one of many possible criteria (time,cost,…).

The whole day planning approach saves time and money in comparison with the naïve single-trip approach and as a consequence also decreases the amount of used energy.


Decentralized load management controller

Traffic light model

  • Green: Increase charging
  • Yellow: follow charging profile
  • Red: Decrease the charging


  • Indicating the Power quality locally
  • Effect of the smart charging at CSs on the power quality
  • Behaviour of the car regarding to the limitation of capacity
  • Fairness among the running charging processes


  • FlexEVELab in context of Eu-project “ Erigrid
  • Interactions between EV, EVSE, the electric power system and the ICT environment of the smart grid
  • EV/EVSE connection to low voltage grid simulation
  • Vehicle and charging station interaction with the smart grid
  • Validation of smart charging algorithm in interaction with the grid
  • Testing modes




©AIT Austrian Institute of Technology GmbH


  • The smart charger react gradually to the changes in the grid
  • The Smart Charger can keep the voltage in the allowed boundaries
  • The Smart Charger guarantee the fairness among the different charging process