The project


The ELECTRIFIC project addresses the call “GV.8-2015. Electric vehicles’ enhanced performance and integration into the transport system and the grid”. Specifically, it targets the third domain of the call: Integration  of  the  overall  cycle  of  EV  energy  management  into  a  comprehensive EV battery  and ICT-based  re-charging  system management,  providing  ergonomic  and seamless user support.



"Because of its shortcomings — driving range, cost and recharging time — the electric vehicle is not a viable replacement for most conventional cars," Takeshi Uchiyamada, Toyota [1]

Changing, this is the challenge. And the prize is: the electric vehicle (EV) theoretically can be run entirely by renewable energy resources if travel plans and charging schedules are coordinated among multiple users and aligned with power supply and grid requirements.

ELECTRIFIC will develop novel technologies and theoretical understanding that enable highly attractive and sustainable electro-mobility through smart vehicle-grid integration.  The technologies will be developed at three layers – the grid, the EV and the user. Seamless and ergonomic collaboration between all layers will be created to make using EVs at least as convenient and attractive as combustion engine vehicles, all the while optimizing the grid, the EV infrastructure utilization and maximizing the use of renewable energy resources.

An intelligent charging infrastructure that integrates grid optimization, renewable energy and user guidance will be bundled into a Decision Making Engine (DME) with an advanced drivers assistant system (ADAS) as human machine interface (HMI). The DME provides multi-criteria routing alternatives customized to user based energy-aware EV routing and charging needs. Through the provision of APIs and a common data layer, the system is agnostic to car manufacturers and navigation systems so it can become a European standard solution for energy-aware EV mobility.

Before the driver enters, the DME has available all data about the grid stability and grid optimisation opportunities, the EV and battery management system (BMS) parameters like State of Charge (SOC),  State of Health (SOH), the country and weather conditions, and the geography. When identifying the user and the organisational requirements respectively, the system has all data about user policies, schedules and user contacts which have been collected from previous uses, the Personal Information Manager and/or certain Enterprise Resource Planning Tools.



Objective 1
To radically simplify the use of EVs by seamlessly integrating charging into the EV usage cycle, thus making the convenience of EVs match or even surpass the convenience of combustion-engine vehicles.
Research Area Driver’s need - and grid-aware EV travel planning and energy management algorithms
Result/Metric Scalable EV travel planning and energy management algorithms capable of considering the planned (daily) activities of the user, the availability of the charging capacity and the state of the EV itself. The algorithms will be open to integration with in-car navigation and battery management systems, integration with charging capacity allocation procedures and persuasive EV user interfaces. Metric: Within ELECTRIFY an approach to an ordinal evaluation of attractiveness by users will be developed. Through the research and technical activities in ELECTRIFIC attractiveness of EV will for a majority of user groups be rated higher than that of combustion-engine vehicles.
Expected impact Significant improvements to the EV usage ergonomy, the eco-friendliness of EVs and the cost-performance ratio of EV, all contributing to faster and broader market take-up. Higher utilization of renewable energy sources for EV charging
Objective 2
To improve the interoperability within the electromobility ecosystem by creating a normalized EV data layer that homogenizes all kinds of external data sources and is agnostic to car and batteries manufacturers and to ADAS developers.
Research areas Development of battery friendly charging algorithms Development of battery health monitoring to support battery friendly long term allocation of EVs in a car fleet
Result/Metric The creation of a common EV mobility data model and the provision of OpenAPIs and services related to EV mobility. Metric: Ensure compatibility between ELECTRIFIC ADAS approach and two major manufacturers of batteries, cars and ADAS.
Expected impact European standardization for interfaces that allow energy-aware charging routing and BMS, including data extraction from EV batteries, grid, charging stations and personal calendars.
Objective 3
To improve the grid friendliness of EV charging and to increase the intake of renewable energy at charging stations through decentralized monitoring and control of the charging process..
Research areas · Maximising the intake of renewables generated at the same geographical location as the charging stations, in addition to those in the mix of the power grid. · Charging station grid-friendliness by taking into account the quality of power.
Result/Metric Design and implementation of a completely decentralized and non-intrusive distribution grid monitoring and control scheme. Voltage band: Reduce number of necessary interventions of grid operators due to voltage band fluctuations by 20%. Increase share or renewable energy at the computed energy mix in EV batteries by 20%, depending on renewable resource availability.
Expected impact To charge EVs cost-effectively, maximally sustainable and yet conveniently, by helping to solve one of the most pressing and still unsolved management challenges of distribution grid operators.
Objective 4
To better align the behaviour of EV users with the requirements of the grid by incentivizing behaviours that maximize battery lifetime, range, and the intake of renewables.
Research Area · Specify psychological variables that predict which types of rewards, monetary or psychological, allow the user to best adopt optimal behaviour patterns. · Market-based EV charging capacity allocation and demand management · Identify the optimal solution for the design of financial incentives to change routing, charging and driving behaviour.
Result/Metric Insights into the behavioural economics, and consumer psychology of EV usage. Optimised and scalable design for communicating between optimized DME and user. Novel algorithms through which limited charging capacity can be allocated to EVs in a way that optimally balances the convenience of using EVs to the user, the utilization of the charging infrastructure and the use of renewable energy resources, all while taking into account power transmission constraints of the grid. The techniques will be able to operate in a heterogeneous environment involving a potential large number of grid operators, EV charging facility providers and EV vehicle fleet operators. The algorithms will be able to accommodate different levels of interaction between the actors in the EV ecosystem. Metrics: Increase the adherence to ADAS recommendation and thereby the number of grid friendly charging operations by 40% compared to baseline.
Expected impact Seamless integration of batteries, smart grid and renewable sources leading to a) enormous savings in grid maintenance; b) increased attractiveness of EVs Dynamic balance between the demand for EV charging and available charging capacity, which maximizes the utilization of available charging capacity while it minimizes the occurrence of situations in which charging capacity is not available to EVs than need it most.