modelBatteryElectricVehicle

Extends from TransiEnt.Basics.Icons.Model (Icon for models).

Information

1. Purpose of model

This model calculates the empiric electrical behavior of an electric vehicle in combination with non-public charging infrastructure (wallbox in low voltage grids) in combination with stochastic driving profiles.

2. Level of detail, physical effects considered, and physical insight

Model is build upon an Onboard Charger model and an Battery model. Please have a look at the Onboard Charger and the Battery documentation

3. Limits of validity

Only valid for low voltage AC grid integration. No DC or high charging power is implemented.

4. Interfaces

Input:

isConnected for the electric connection dependent on the stochastic drivign profile.

controlBus for P_limit, SignalActive and P_input which are further connected to the Onboard Charger.

Output:

epp for connection to a complex root mean square grid simulation

5. Nomenclature

See parameter and variable descriptions in the code.

6. Governing Equations

The equations 6.3 - 6.6 are calculated first before 6.1 and 6.2 are calculated due to the sequential structure of the model.

6.1. State of Charge Alteration

when isConnected then

reinit(bEVBattery.E, max(bEVBattery.SOC - SOC_consumption, 0)* Capacity);

  

end when;


7. Remarks for Usage

Choose the type of empirical charger data but altering the parameter Charger_params. This changes the Onboard Charger Efficiency and Reactive Power Profile.

8. Validation

The model has been validated against the measurement data which were the bases for the empirical approach. Additionally a comparison with literature information about efficiency and reactive power was performed. The quality of the efficiency measurements was confirmed. After revalidating the data, the reactive power still differed from the data in the literature source. [1]

9. References

[1] K. Sevdari, L. Calearo, B. H. Bakken, P. B. Andersen, und M. Marinelli, “Experimental Validation of Onboard Electric Vehicle Chargers to Improve the Efficiency of Smart Charging Operation,” Sustainable Energy Technologies and Assessments, Bd. 60, S. 103512, Dec. 2023.


10. Version History

ElectricVehicle by Tom Steffen, Techische Universität Hamburg, Institut für Elektrische Energietechnik, 2025

Parameters

TypeNameDefaultDescription
TransiEnt.Consumer.Electrical.ElectricVehicle.Characteristics.Renault_Zoe_R110Charger_ParamsTransiEnt.Consumer.Electrical.ElectricVehicle.Characteristics.Renault_Zoe_R110()
Battery
Modelica.Units.SI.PowerP_nominal11000Nominal charge and discharge power
Modelica.Units.SI.VoltageV_nominal400Nominal voltage
Modelica.Units.SI.EnergyCapacity90e3*3600Battery capacity in Joule
RealSOC_initial0.5Battery Charge at start
RealSOC_th0.95If SOC > SOC_th reduce power linearly
RealSOC_stop1If SOC > SOC_stop stop charging
Reallambda_Pmin0.02Minimum relative power at SOC limits (0 or 1)
Control
Realt_deadTime2Battery reaction dead time on set value
Realtau_dynamics3Battery dynamic reaction on set value
External Control
ControlTypecontrolTypeTransiEnt.Basics.Types.ControlType.InternalType of control (see enumeration)

Connectors

TypeNameDefaultDescription
TransiEnt.Basics.Interfaces.Electrical.ComplexPowerPortepp
TransiEnt.Basics.Interfaces.General.ControlBuscontrolBus
Modelica.Blocks.Interfaces.RealInputSOC_consumption
Modelica.Blocks.Interfaces.BooleanInputisConnectedBoolean Input considering wether the EV is connected to the grid when not driving

Components

TypeNameDefaultDescription
BEVOnboardChargerbEVOnboardCharger
TransiEnt.Storage.Electrical.BEVBatterybEVBattery
Modelica.Blocks.Sources.RealExpressionP_
Modelica.Blocks.Sources.RealExpressionP_max_
Modelica.Blocks.Sources.RealExpressionQ_