This is a web app demonstrating a back looking simulation using GPS-logs. By using a simple vehicle and accumulator model the calculation allows testing different charging strategies. The test case should cover at least on complete run at one bus line
set Power of charging pause 1 to zero (actually the stop is due to congestion)
click on calculate
you might enter one additional charging pause by clicking in the map (timestamp should be before cut-off time), position 2 is a candidate
Beware, given data are estimates and bugs might be still leading to invalid results!
It is now possible uploading own GPS/GNSS files (first click on the checkbox nearby), please test with GLONASS readings.
If you are bus operator please also fill in the questionnaiere, so we may adapt our work to your needs, many thanks
Development level 24.4.2018
The model ist charging agnostic, i.e. the charging method may be conductive or inductive. The only type of charging which is not implemented, is charging while driving. For inductive charging the charging efficinecy may be lowered.
Vehicle translation: complete taking real efficiency of a battery electric power train, but no resistive losses in the accumulator, assumption for rotational masses to be validated, vehicle library to choose from appreciated, define passengees leaving/boarding at stops.
Drive train: efficiency map introducted for motor and generator (estimation), max. powe and efficiency when exceeding max. power assumed.
Charging: max. charging power depends on the SOC (simple model with linear dependency),, efficiency could be detailed, time to disconnect to be considered in the simulation
Heat demand: actually minimum convective heat loss defined, velocity adds to that: model to be refined, heat pump to be included, ancillary energy demand to be validated (also for BMS).
Data quality velocity: 5 past measurements (measured at 5 Hz) taken, with different filters depending on variance, targeting to take an average of two GPS modules, positioned at different locations in the bus, filtering should be evaluated, python software for recording from two GPS receivers at 5 Hz to be improved, so average position is taken, or weighted average (with HDOP).
Customisation: choosing own GPS log files possible, GPS-logs may be restricted to a defined timestamp.
User Support: potential pauses are indicated via numbers, non active strech is greyed out.
Usability: validation of user entries against boundaries, storing user input (with user registration).
Added functionality: variation of energy storage and chargung power to satisfy SOC-requirements, calculation of total cost of ownership, automatic position determination for first completed tour not yet implemented but colored circles for start and stop .
What-If-scenarios to be applied manually:
How will costs change if the time at the turning point is elongated?
Where placing the turning point for two lines running partly parallel.
should parallel lines be separated allowing faster cheaper charging
is ubiquitious inductive charging an option - cost-wise?
how local energy storage may reduce infrastructure cost?
Cost Calculation Scheme
ICcharging (per kW and tour) =ICcharging (per kW) / (numbertours per day * 360 * lifespancharging infrastructure)
TCOvehicle = ICvehicle / hours in operation total * hourstour*(1+ maintenance cost fractionvehicle per year)
TCOdriver = costdriver *hourstour
TCOcharger=ICspec. charging (per kW and tour) * Total kWcharging stations * (1 + maintenance cost fractioncharger per year
TCOenergy =EnergyCharging * ( spec. costenergy (per kWh) + costbattery per kWh turn around )
Total cost =7.8 € vehicle cost=1.8 € driver cost=3.7 € cost charging infrastructure =0.9 € energy and accumulator cost=1.4 €
One round was simulated: min SOC = 75.3 %. For a loop iterating the SOC at thhe start of this round, please tick the checkbox "solve for charging power and accumulatoir size"
Number of calculations in the time domain=2128, of those were invalid, 215 for the motor and 115 for the generator.