The increasing democratization of EV brings with it the need to improve user comfort . For example, the need for large-scale chargers has not yet been met. Indeed, a recent report Eurelectric and EY estimates that 5 million public chargers are needed in Europe by 2030.
However, the implementation of these infrastructures at large scale is a real challenge for companies but also for public authorities. One of the challenges of this implementation is to ensure a reliable but above all to users. This is essential to reinforce the confidence of drivers in this technology and to overcome the famous and overhyped “range anxiety”.
To achieve such massive deployment comprising tens of thousands of charging points, it requires an electric vehicle charging management platform that can scale while being stable. That’s what we’ll explore in this article, we’ll explore the features that support large-scale electric vehicle charging optimization.
Future charging solutions will create satisfying charging experiences for electric vehicle drivers
Drivers are used to know fuel prices at gas stations. This transparency on prices per liter is ingrained in driving habits and enforced by legislation. Price transparency is the cornerstone to build trust with customers, still there is an opportunity to go further to provide an even more enjoyable experience for electric vehicle drivers by providing real time information about the network and energy quality. Protocols and standard will allow this.
The availability and precise location of charging stations, the price of energy but also its carbon footprint and real-time assistance are real elements that will advance the democratization of electric vehicles.
Future of charging will have real-time problem identification and repair at anytime
In order to make charging even more efficient, the charging stations must be operational at any time of the day or night. It is therefore necessary that the charging stations are all functional! The supervision of the charging stations must therefore be optimal and facilitate the intervention in case of problems. The creation of algorithms that identify and automate the repair of problems is necessary to ensure a stable network.
Proactive and automated problem resolution
Our experience during the last 7 years shows that 80% of the errors related to chargers can be solved remotely, the implementation of an AI to manage these issues can save time on the supervision of a large network of charging stations. Indeed, automating a process before human intervention brings comfort to the teams in charge of network maintenance. More advanced algorithms can solve most problems before human intervention. They can therefore help to keep the network afloat without mobilizing too many human resources, reducing cost and improving user experience.
Future of EV charging station will have digital solutions needed to manage a network in an optimal way
Support for industry standards, protocols, and vendor-agnostic charging support is the future of ev charging infrastructure
EV charging is still in rapid evolution with new protocols and new usages, therefore charging point operators cannot be certain that chargers that are in use today will still be usable tomorrow. Technology is evolving so quickly that this parameter must be taken into account : for example, V2G, Rewable energy…. Thus, to expand their network and makes it evolve, they must operate a future-proof platform able to evolve over time embracing technology changes
It must be compatible with a large subset of themany chargers on the market. It is also necessary that it is compatible with the standards and protocols that are currently the reference today but that it is also capable of integrating smoothly and timely new versions or new protocols.
The integration of dashboards to manage a constantly evolving network
The mass operation of such large networks requires the integration and management of a large amount of data. This data must be accessible to the network operators, whether for business purposes (such as billing management),for real-time network intervention. Operational data must be able to help its technical managers provide reliable service to drivers in a sustainable way, because if the network expands, reliability must follow. Last not least data should enable to get insights about the different facets of network operations (eg. ROI, TCO, usage, reliability) enabling to pilot its evolution over time.
Ensure the creation of customizable solutions designed for large-scale EV charging networks
The constant development of the network is made possible by actors who install their charging stations in new cities or even in new countries. Others are directly buying up entire networks. In either case, the use of a platform designed to provide access and associated services to different population of drivers (eg. family, professional, fleet, roaming users) is key to make a sustainable and resilient offer.
Especially since this type of network evolves rapidly, it is necessary that the various platforms on the market have the ability to adapt. Thus, platforms must have the ability to integrate new sites and be able to seamlessly integrate the various components of the company such as expense management, business networks or HR systems.
Large-scale EV charging optimization drives EV adoption
It is only by making the grid better that the adoption of electric vehicles will happen. Providing a transparent offer with a recognized level of services is key to overcome the main barriers to EV adoption such as range anxiety, the unreliability or unavailability of charging stations that cause the fear of not being able to continue one’s journey.
It is therefore up to an entire industry to anticipate these problems in order to be able to respond to them before they even arise. Optimizing the scale of electric vehicle charging is a challenge for our industry, and the reliability and availability of charging stations must be the driving force behind the industry. This is also one of the requirements that Open E-Mobility solution was designed for.