The opportunities and challenges of range prediction for electric vehicles

Accurate range prediction is even more important in the day-to-day operation of electric buses as battery capacity does not decrease on a linear basis. Ideally, the prediction should accurately represent the actual energy demand over the course of the operating day. Accordingly, the predicted consumption should reflect the actual consumption as accurately as possible. Accurate predictions benefit many different areas for the company – firstly, deployment planning: Planning reserves can be reduced and consequently, electric vehicles can be deployed more efficiently. In other words, when fewer reserves are needed, a higher proportion of energy can be used productively. Transport companies with large fleets benefit even more because by using their electric vehicles more efficiently, they can reduce the number of required vehicles and ultimately reduce their costs. Good range prediction also brings many advantages for the control center. Dispatchers have to intervene less and, if the range does run low, it gives them more time to take appropriate action. At the end of the block, an accurate range prediction provides a reliable database for the charge management system, which can plan the required amount of charge for the next day based on the remaining range. For the next operating day, the depot management system can also use range prediction information to assign blocks.

A valid prediction that approximates actual consumption is the result of complex computing processes because energy consumption depends on a large number of influencing factors. These include the temperature during the operating day, because additional energy consumers such as heating and cooling systems consume a considerable amount of energy. Vehicle type, passenger volumes, topology, etc. also play a role. Most of these factors are known in advance for each block, so they can be taken into account for the prediction. Temperature information can be retrieved via a weather service. Operational information such as block, line and trip are of course also known in advance. Many of these factors depend heavily on the transport company’s general conditions: An electric vehicle used in a mountainous area consumes more energy than a similar vehicle on flat terrain. Based on recorded consumption data on the individual route sections, MOBILErange predicts the expected consumption for the blocks, trips and route sections, taking into account the current conditions.

Another factor that affects energy consumption is the volume of passengers. In the future, an interface could be used to predict occupancy levels from MOBILEguide, INIT's system for occupancy information, to further refine the range prediction.

Unforeseen events such as accidents or other factors which are not known in advance make it essential to plan energy reserves. These must be calculated in quantities as small or as large as necessary in order to run the vehicles as efficiently as possible. If a vehicle consumes more energy than predicted, it can run out of charge and may not reach the next charging station. Consequently, if energy consumption is predicted to be higher, this poses fewer problems than if it is predicted to be lower.

When determining optimal planning reserves, it is important to take into account the respective planning period because different systems used to manage electric vehicles all operate with different planning periods. For example, the planning system plans for the next few weeks, whereas the charge and depot management system plan for the next one or two operating days, and the ITCS usually only considers the current operating day. The longer the planning period, the larger the planning reserve should be, as there are likely to be greater uncertainties such as the weather. MOBILErange can adjust this planning reserve individually for each system.

In general, the more electric buses of a certain vehicle type are in operation on the same line or block, the faster the required amount of data can be obtained in order to refine the prediction so that only minimal planning reserves are required.