LOTUS - Long-Haul Truck Simulation

Stand Alone Version of the longitudinal dynamics, weight and cost model of the Truck2030 project (formerly named HDVSim).
Please also see the project homepage Truck2030 and our researchgate profile to find more information about us and our work.

Steps for running the simulation

Open the script called Main_file.m; most of the simulation parameters and properties can be accessed there.
Choose whether the values after the optimization or custome values to be run. This is controlled by the flag "ifOptimized".
If optimized values are chosen, assign 1 to ifOptimized and choose which vehicle is needed to run by assigning a number from 1 to 16 to the variable "DrvTrn".
If custom values are needed, assign 0 to ifOptimized. Then choose the type of fuel to run by uncommenting the required line in "Fuel type" block. After that, assign the custom values to the parameters found in Parameterizing().
Choose which driving cycle is needed by uncommenting the required line in the "Driving cylce" block.
Some simulation properties regarding the results can be adjusted. If displaying figures is required, assign 2 to Param.VSim.Display; otherwise leave it as 3. If output results in te command window is not needed, then assign "True" to the variable Param.VSim.Opt; otherwise leave it as "False". This is found in "Simulation properties" block.
When everything is set up, click on run. The steps will be shown in the command window, and a notification will indicate whether the simulation was successful or not. In the case of success, the results will be save in the folder "Results".

Prerequisites

Matlab
Curve Fitting Toolbox
Optimization Toolbox
Simulink
Simscape
Powertrain Blockset
Simscape Electrical (Replaces Simscape Power Systems and Simscape Electronics 2018b and later)
Simscape Power System (2018a and earlier)
Simscape Electronics (2018a and earlier)
Stateflow
Simulink Coder
Parallel Computing

Running the Model/Code

Standard simulation

Main_file.m

Influence of the Cooling System and Road Topology on Heavy Duty Truck Platooning

C. Mährle, S. Wolff, S. Held, und G. Wachtmeister, in The 2019 IEEE Intelligent Transportation Systems Conference - ITSC: Auckland, New Zealand, 27-30 October 2019, [Piscataway, New Jersey]: IEEE, 2019, S. 1251–1256.

platooningEvaluationParfor

Technoecological analysis of energy carriers for long‐haul transportation

S. Wolff, M. Fries, und M. Lienkamp, Journal of Industrial Ecology, Bd. 49, Rn. 11, S. 6402, 2019.

addpath('Post-processing\JIE');
Infrastruktur_Fahrzeuge_Auswertung
Auswertung_JounralIndEco

Influence of Powertrain Topology and Electric Machine Design on Efficiency of Battery Electric Trucks–A Simulative Case-Study

S. Wolff, S. Kalt, M. Bstieler, und M. Lienkamp, Energies, Bd. 14, Rn. 2, S. 328, 2021.

topologiesMain

Multi-disciplinary design optimization of life cycle eco-efficiency for heavy-duty vehicles using a genetic algorithm

S. Wolff, M. Seidenfus, M. Brönner und M. Lienkamp, Journal of Cleaner Production, Jg. 318, S. 128505, 2021, doi: 10.1016/j.jclepro.2021.128505

To calculate the eco-efficiency for a specific vehicle, run:

CalculateEcoEff(Param, Weighting, 'ElectricityMix', 'UsePhase Diesel', 'UsePhase Hydrogen')

See the function description for possible parameters.

To reproduce the data, scenarios and figures from the publication run

JCP_Scenarios
JCP_PlotScript

Deployment

Built with

Matlab R2020b

Tested with

Matlab R2017b
Matlab R2018b
Matlab R2019b

Contributing and Support

If you want to contribute to this project, please contact the correspondence author.

Versioning

V1.0 Consumption simulation, weight and cost model for heavy-duty trucks

V1.1 Platooning

V1.2 Powertrain topologies for electric heavy-duty trucks and VECTO driving cycles

V1.3 Eco-efficiency analysis

V1.3.1 Bugfix in Plotsscripts

V1.3.2 Include Hydrogen Combustion Engine in Eco-Eficiency Assessment

V1.3.3 Bugfixes in Eco-Eff calculation, functional unit and plotscripts

Authors

Alexander Süßmann - Consumption Simulation for Diesel Trucks, Validation

Michael Fries - Hybrid Drivetrains, CNG, LNG, Dual Fuel, Cost and Weight Model

Sebastian Wolff* - Battery Electric, Fuel Cell, Overhead Catenary/Inductive Charging, 3 Truck Platooning, Infrastructure Cost Model, Electric Powertrain Topologies, Eco-Efficiency Analysis

*Correspondence Author
sebastian.wolff[at]tum.de
Technical University of Munich
Institute of Automotive Technology

Contributors (chronological order)

The following authors contributed substantial parts to the simulation during their student thesis's.

Bert Haj Ali
Stefan Weiß
Cheng Pan
Aonan Shen
Moritz Seidenfus
Paul Mauk
Maunel Bstieler
Niclas Eidkum

License

This project is licensed under the LGPL 3.0 License - see the LICENSE.md file for details

Publications

The simulation is featured in the following publications:

Dissertations

M. Fries, “Maschinelle Optimierung der Antriebsauslegung zur Reduktion von CO2-Emissionen und Kosten im Nutzfahrzeug,” Dissertation, Lehrstuhl für Fahrzeugtechnik, Technische Universität München, München, 2018.
A. Süßmann, „Kundenspezifische Bewertung von Maßnahmen zur Reduktion des Kraftstoffverbrauchs bei schweren Nutzfahrzeugen [Customer-specific evaluation of measures to reduce fuel consumption in heavy commercial vehicles]“. Dissertation, Lehrstuhl für Fahrzeugtechnik, Technische Universität München, München, 2021.

Articles

M. Fries, M. Kruttschnitt, und M. Lienkamp, “Multi-objective optimization of a long-haul truck hybrid operational strategy and a predictive powertrain control system,” in Twelfth International Conference on Ecological Vehicles and Renewable Energies (EVER), 2017, S. 1–7.
M. Fries, M. Lehmeyer, und M. Lienkamp, “Multi-criterion optimization of heavy-duty powertrain design for the evaluation of transport efficiency and costs,” in IEEE ITSC 2017: 20th International Conference on Intelligent Transportation Systems : Mielparque Yokohama in Yokohama, Kanagawa, Japan, October 16-19, 2017, Piscataway, NJ: IEEE, 2017, S. 1–8.
M. Fries, A. Baum, M. Wittmann, und M. Lienkamp, “Derivation of a real-life driving cycle from fleet testing data with the Markov-Chain-Monte-Carlo Method,” in 2018 21st International Conference on Intelligent Transportation Systems (ITSC): IEEE, 2018, S. 2550–2555.
S. Wolff, M. Fries, und M. Lienkamp, “Technoecological analysis of energy carriers for long‐haul transportation,” Journal of Industrial Ecology, Bd. 49, Rn. 11, S. 6402, 2019.
S. Wolff, S. Kalt, M. Bstieler, und M. Lienkamp, “Influence of Powertrain Topology and Electric Machine Design on Efficiency of Battery Electric Trucks–A Simulative Case-Study,” Energies, Bd. 14, Rn. 2, S. 328, 2021.
S. Wolff, M. Seidenfus, M. Brönner und M. Lienkamp, „Multi-disciplinary design optimization of life cycle eco-efficiency for heavy-duty vehicles using a genetic algorithm“, Journal of Cleaner Production, Jg. 318, S. 128505, 2021, doi: 10.1016/j.jclepro.2021.128505 .

Reports

C. Mährle et al, “Bayerische Kooperation für Transporteffizienz - Truck2030: Status Report 2016,” 2017.

TUMFTM/LOTUS