In the implementation phase
- Simulations as a basis for cost-effective investment decisions
- Determination of an optimal charging concept
- Design of a secure, regulated and calculable implementation process
In the operational phase
- Comprehensive solution that includes all operational processes
- Permanent overview of the charging status of electric buses
- Smooth operation of electric bus fleet through reliable range forecasting
- Cost savings by avoiding charge peaks, optimized calculations of required charging quantities, energy-efficient driving and optimized maintenance
Efficient operational planning of electric bus fleets with eMOBILE-PLAN
With the help of simulations, INIT’s planning system, eMOBILE-PLAN assists you gather important information in order to make strategic decisions and to minimize risks. Which e-buses are the right ones for your transport system? What is the best charging strategy for your public transport company? What are the investment and operating costs associated with the different options? In addition, eMOBILE-PLAN contains electromobility – specific parameters such as different outside temperatures or the route topology. With the use of efficient optimization algorithms, blocks are created that are both economical and robust – even for electric buses.
Precise range forecasting and monitoring with eMOBILE-ITCS
Always know the current state-of-charge: This is essential when using e-buses because the range depends on many factors and is therefore more difficult to predict than it is for diesel engines. That is why ranges must be monitored in the public transport company’s central management tool – the Intermodal Transport Control System. With the eMOBILE-ITCS extension, dispatchers can manage the state-of-charge and can intervene if necessary.
Before a dispatching measure is executed, the system checks whether the remaining range is sufficient to safely finish the block. The basis for all of this is a reliable range prediction that becomes increasingly precise through machine learning. The range prediction application MOBILErange uses historical operating data from the MOBILEefficiency evaluation system and generates a model for the battery consumption of individual vehicles, based on individual route segments using the latest machine learning techniques.