Preview

Trudy NAMI

Advanced search

The KAMAZ electric bus simulation model movement verification

https://doi.org/10.51187/0135-3152-2021-4-60-67

Abstract

Introduction (problem statement and relevance). Currently, one of the main and promising directions in the automotive industry is the development of the electric vehicle and charging infrastructure sector. The constant tightening of environmental requirements, the development of traction batteries (TAB) and automotive electronics are the main factors in the development of wheeled electric vehicles. The operation of electric buses on urban routes in modern cities is one of the promising developments of electric buses use. But the problem is, the TAB capacity, its resource and cost are still limited, therefore a key task in the development of an electric vehicles the choice of the most effective control algorithms and components of the traction electric drive (TED). The solution to this problem requires working out a simulation model, the accuracy and complexity of which must satisfy the chosen goal.

The purpose of the study was to develop and verify a KAMAZ 6282 electric bus simulation model basing on experimental data.

Methodology and research methods. The article presents an experimental and calculated data analysis of the main electric bus movement modes when driving in a city: acceleration, coasting, braking, upward movement.

Scientific novelty and results. Basing on comparing the experimental and calculated data results, it has been determined that the presented simulation model of the electric bus was sufficient and adequate to determine the main performance indicators of the TED.

Practical significance. The presented simulation model made it possible to analyze the performance indicators, on the basis of which the selection of the optimal TED components could be carried out. The simplicity of the simulation model allowed it to be used as part of optimal control algorithms and evaluate the electric bus movement along a city route.

About the Authors

I. K. Maslennikov
Federal State Unitary Enterprise “Central Scientific Research Automobile and Automotive Engines Institute”; LLC “Innovation Center “KAMAZ”
Russian Federation

Postgraduate, lead software engineer

Moscow 125438;  Moscow 121205



K. E. Karpukhin
Federal State Unitary Enterprise “Central Scientific Research Automobile and Automotive Engines Institute”
Russian Federation

PhD (Eng), associate professor, project director

Moscow 125438



A. V. Klimov
LLC “Innovation Center “KAMAZ”
Russian Federation

PhD (Eng), head of electrified vehicles service

Moscow 121205



B. K. Ospanbekov
LLC “Innovation Center “KAMAZ”
Russian Federation

PhD (Eng), automotive electronics group leader, electrified vehicle services

Moscow 121205



References

1. Kositsyn B.B. [Method for determining the energy efficient law of movement of an electric bus along an urban route. Cand. eng. sci. diss.]. Moscow, BMSTU, 2017. 168 p. (In Russian)

2. Rios-Torres J., Sauras-Perez P., Alfaro R., Taiber J. et al. Eco-Driving System for Energy Efficient Driving of an Electric Bus. SAE Int. J. Passeng. Cars – Electron. Electr. Syst. 8(1):79-89, 2015.

3. Sciarretta A., De Nunzio G., Ojeda L. Optimal ecodriving control: Energy-efficient driving of road vehicles as an optimal control problem. IEEE Control Systems Megazine, Oct 2015, vol. 35, no. 5, pp. 71–90.

4. Wang J. Battery electric vehicle energy consumption modelling, testing and pre-diction: a practical case study. PhD thesis, Eindhoven University of Technology. Eindhoven, the Netherlands, 2016.

5. Schmitz M., Maag C., Jagiellowicz M., Hanig M. Impact of a combined accelerator-brake pedal solution on efficient driving. Intelligent Transport Systems IET, 2013, vol. 7, no. 2, pp. 203–209.

6. Wang, J., Besselink I. J. M., van Boekel J. J. P. & Nijmeijer H. (2015). Evaluating the energy efficiency of a one pedal driving algorithm. European Battery, Hybrid and Fuel Cell Electric Vehicle Congress (EEVC 2015), Brussels, Belgium, 2015, pp. 1–10.

7. Zhang Sh., Zhuan X. Model-Predictive Optimization for Pure Electric Vehicle during a Vehicle-Following Process.

8. Syed F.U., Filev D., Ying H. Fuzzy rule-based driver advisory system for fuel economy improvement in a hybrid electric vehicle. Proc. Annual Meeting of the North American Fuzzy Information Processing Society, Jun. 2007, pp. 178–183.

9. Syed F.U., Filev D.P., Tseng F. Adaptive real-time driver advisory control for a hybrid electric vehicle to achieve fuel economy: US20140012456A1.

10. Litvinov A.S., Farobin Ya.E. [Automobile: Theory of operational properties: Textbook for universities in the specialty “Automobiles and the automotive industry”]. Moscow, Mashinostroenie Publ., 1989. 240 p. (In Russian)

11. Maslennikov I.K., Karpukhin K.E., Klimov A.V., Ospanbekov B.K. [Research of operational indicators of traction electrical equipment of an electric bus in urban traffic conditions]. [Technologies and components of ground-based intelligent transport systems: conference proceedings, October 16-18, 2019]. Moscow, FSUE “NAMI”, 2019, pp. 377–384. (In Russian)

12. Karpukhin K.E., Terenchenko A.S., Shorin A.A. [Justification of the parameters of balancing batteries]. Vestnik mashinostroeniya, 2015, no. 11, pp. 25–27. (In Russian)


Review

For citations:


Maslennikov I.K., Karpukhin K.E., Klimov A.V., Ospanbekov B.K. The KAMAZ electric bus simulation model movement verification. Trudy NAMI. 2021;(4):60-67. (In Russ.) https://doi.org/10.51187/0135-3152-2021-4-60-67

Views: 327


Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 License.


ISSN 0135-3152 (Print)