Data-driven, Cloud-Based Optimization Engines
Connected services for electric vehicles (EVs) are pivotal for ensuring the best user experience for EV drivers and accurately managing electric fleets. However, global technical advances in electric mobility are not yet aligned with the EV movement, creating a behavioral hurdle for drivers as they try to adapt to new technology.
A complex environment for consumers and fleet managers
Transitioning to electric mobility isn’t easy for individuals and commercial drivers who perceive that it’s complicated to drive EVs effortlessly over long distances or with high intensity. Fleet operators face even greater risks. The fragmentation of the electric charge-and-drive ecosystem is rife with complexity and unpredictability.
EV drivers have to deal with different standards in plug-type and charge speeds along with dynamic variables such as charge availability and charge power as well as outside variables like temperature, and topography. Combined with driving habits, all these variables impact the range of their vehicle.
These flaws in the ecosystem cause three adoption barriers for individuals and fleet operators, namely:
1. Range anxiety: The fear of not having sufficient power to reach a destination.
Although most of the charging is done at home before driving, and less than half of the long-distance travellers charge weekly at fast chargers, range anxiety still affects EV drivers. This is especially true of prospective drivers who are still undecided as to whether to go electric or not. Most people research a vehicles’ range before switching to one. Consultancy and accountancy firm Ernst and Young identified driving range as a constraint for fleets when going electric. Chargetrip based in Amsterdam founded with the mission to remove all barriers to entry and accelerate the mass adoption of electric driving with intuitive software solutions argues that range anxiety is exacerbated by existing charge map applications that don’t connect charging data to navigation, making it difficult for drivers to find and drive to suitable charging points.
2. Charge anxiety: The uncertainty about the availability of charging stations and potential waiting lines.
This occurs in mature EV markets, such as Norway and California, and other locations during busy holiday periods when most cars are on the road. Charge anxiety is amplified by the lack of shared data regarding the status of the network.
3. Operational complexity: Intricate variables such as electricity price and charge time that fleet operators have to consider when organizing logistics.
Most current fleet management software isn’t suitable for electric vehicles. The transition to new technology that companies must adopt to electrify their fleet adds difficulty.
A context-based routing engine for EVs
To solve these issues, Chargetrip developed a smart EV routing engine that makes electric mobility predictable. It considers 15 static and dynamic variables (weather, elevation, traffic, charge station availability, energy price) in conjunction with proprietary algorithms to calculate the best route to a user’s destination, identifying the optimal charge stations in between. Built-in predictive models also optimize for total travel time and travel costs.
This EV routing engine overcomes the afore-mentioned adoption barriers.
The concept of “orchestrated charging” refers to the ability to optimize the entire charging chain: charge point operator (CPO) preference, charge card optimization, depot charging optimization, routing and on-route charging optimization, vehicle utilization optimization.
By communicating with the fleet management software, the smart charging software, and the dispatch software, the solution predicts the energy needs of a vehicle and the smart charging software charges it accordingly. Furthermore, it optimizes en-route charging by ingesting telematic data and liaising with the preferred EMSP (E-mobility Service Provider) and CPO.
Providing medium and heavy-duty (MD/HD) EV fleets with intelligent and integrated tools, will help fleet managers to overcome complexity and efficiently manage TCO (Total Cost of Ownership). If you electrify a fleet of diesel trucks to similar electric models, and you use smart charging and route orchestration, your refuelling costs decrease on average by 30%. This assumes you optimize off-peak/on-peak charging, charge at the cheapest HPC stations, and optimize charging times by accurately predicting consumption.
A crucial element to planning a route for an EV is knowing the exact energy consumption of each specific vehicle. Therefore, a proprietary up to date EV-consumption-model database is essential.
Harvesting an EV database allows market players that deal with EVs from different manufacturers (e.g., charge point operators, mobility service providers, fleet operators) to develop EV-route planning solutions for their EV drivers without needing to search for energy consumption models elsewhere.
After calculating the predicted consumption of a vehicle considering the specific circumstances (e.g. weather, elevation, terrain) we precompute a graph with all the possible route outcomes. In the graph, charge stations are represented as nodes, and roads are represented as the edges between nodes. This algorithm searches for the best way along the edges to reach a user’s destination. This is the core of the routing engine.
And unlike existing navigation and telematics solutions, the Chargetrip platform supports open integrations with third-party applications, is modern and cloud-based, and has an intuitive, easy-to-use API and user interface.
On the future of electric mobility and EV routing, Gideon van Dijk, CEO of Chargetrip, states, “Since we founded Chargetrip, our mission has been to make electric mobility intuitive and efficient. And while those six words — make electric mobility intuitive and efficient — may seem like a simple statement, accomplishing it is extremely complex.”
The EV market is still young, but we can safely state that the future of mobility is electric. It is one of the most crucial catalysts in the renewable energy transition and necessary to become carbon-neutral within one generation. Even EVs that plug into dirty grids emit less greenhouse gas than fossil-fuel-powered cars. However, the total cost of ownership for electric vehicles and electric fleets lags behind its environmental potential and is becoming a focus of global electrification efforts.
Stimulated by legislature, regulations, low-emission zones, and subsidies, some first-movers are making the switch. However, their new electrified permanence is complex and unpredictable. While cost components stay more or less constant, their TCO optimization strategy is turned upside down.
Teething problems are inevitable, though. Generally, this is such an exciting time to work in the mobility space. We’re convinced the next few years will offer incredible opportunities for challengers like us: data-driven, cloud-based optimization engines able to deal with large amounts of these new variables.
So what’s next? The technology doesn’t just tackle range and charge anxiety but slashes costs for fleets willing to take the plunge. We’re going to keep working on innovative ways to make owning and operating EVs, privately or commercially, more efficient, reliable, and seamless. But we’re also going to start exploring ways to connect our predictive technology to new forms of mobility services necessary for electrified fleets. Coordinating energy use across disparate charging scenarios is a critical next step to make operating an electric fleet efficient and cost-effective.
As our civilization faces unprecedented climate challenges we need to build data-driven solutions that can make the energy supply chain smarter and more efficient. Chargetrip is a mission-driven company that wants to fill the gap between breakthrough technology, policy, and commercial scalability that helps catalyze the renewable transitions and democratize energy in the long run.” van Dijk, concluded.