The need for software to properly plan for and schedule EVs is often overlooked. EV planning and scheduling poses complex challenges.
The electrification of public transportation may be slow, but it’s bound to happen. All over the world, cities and governments look to electric vehicles in public transportation to reduce pollution and make cities cleaner. Major capitals are creating ultra low-emission zones or requiring full electrification of public transportation in the future.
Most experts focus on “hardware” choices: the buses, batteries and chargers agencies and operators should purchase. It makes sense to ask those questions: EV deployments are expensive and there is a need to mitigate risks, to not discover that the ranges, charging requirements or vehicle count required for a given service is different than planned. However, modelling this accurately will probably require software.
Indeed, what is often overlooked is the need for software to properly plan for and schedule EVs. Many legacy scheduling platforms do not support EV requirements, yet EV planning and scheduling pose complex challenges. If battery levels, charging speed and capacity as well as trips to charging locations aren’t taken into account, the results will be sub-optimal, increasing the risk of failed EV implementations.
The successful adoption of EVs into public transportation is dependent on our ability to optimize the resources available to us while complying with the new constraints created by charging requirements. Doing this with the help of advanced algorithms can allow us to create public transit that maximizes operational efficiency, while reaping the benefits of carbon independence.
As a result of battery range limitations, EVs need to be recharged throughout the day and, at the very least, planners must form a charging strategy compatible with available infrastructure. This introduces new challenges to the planning and scheduling process. Because suboptimal recharging threatens to increase the number of vehicles required to operate a timetable (and therefore increase operational costs), optimizing charging events is essential to create economically-viable electric transportation solutions.
Deciding when, where, and for how long to recharge electric vehicles is dependent on several variables:
Two important concerns factor here:
Range anxiety is when there are fears that the remaining battery levels are low enough to run out of power in the middle of a trip. This fear can lead to overly cautious scheduling that hedges against this – it isn’t a good practice to follow. The discharge rate for batteries isn’t linear, and often batteries behave differently than appears in OEM specifications. Additionally, ranges are also affected by weather conditions, ridership, road conditions, driving styles and more. However, scheduling can account for this, setting the right thresholds, preferably based on real-life information from the networks, and making sure that these thresholds aren’t exceeded.
Insufficient “electric miles”: in principle, the goal of any EV deployment is to service as many “electric miles” as were previously served with a fossil-fuel based vehicle fleet. When there are too many charging events, or they are inefficient, require excessive deadheads or layovers etc, the fear is that the EV buses will serve less “electric miles”, or require more buses to offer the same “diesel miles”.
The ability to easily express preferences and constraints with regards to electric vehicles, on a simple interface, as well as be informed (through a validation mechanism) to know when batteries are depleted, can help create different scenarios. For instance, a scenario considering different locations for chargers, different routes served by EVs, quick or slow charging etc. You can also check the impact of setting different minimum battery levels and more.
Often, EV adoption begins gradually, when several electric buses are purchased and used within an existing bus network. Since most scheduling systems do not support scheduling electric vehicles alongside ordinary buses, there is a tendency to try to simplify the problem and relegate electric buses to certain routes or only to be used only during peak times and charged during non-peak hours. This approach isn’t recommended, since it does not maximize electric miles, as it usually under-utilizes electric buses and is mostly a result of range anxiety. Under utilization means a high vehicle requirement, with a resulting effiiciency degradation.
We recommend scheduling mixed fleets while taking into account two vehicle “groups”. This creates a solution that considers the different costs associated with different vehicle types and optimizes accordingly. In cases like these one can create hierarchies, preferring one group over the other, taking EV and battery type into account.
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