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Approach to optimizing charging infrastructure of autonomous trolleybuses for urban routes

https://doi.org/10.37661/1816-0301-2021-18-4-79-95

Abstract

P u r p o s e s.  When designing a system of urban electric transport that charges while driving, including autonomous trolleybuses with batteries of increased capacity, it is important to optimize the charging infrastructure for a fleet of such vehicles. The charging infrastructure of the dedicated routes consists of overhead wire sections along the routes and stationary charging stations of a given type at the terminal stops of the routes. It is designed to ensure the movement of trolleybuses and restore the charge of their batteries, consumed in the sections of autonomous running.

The aim of the study is to create models and methods for developing cost-effective solutions for charging infrastructure, ensuring the functioning of the autonomous trolleybus fleet, respecting a number of specific conditions. Conditions include ensuring a specified range of autonomous trolleybus running at a given rate of energy consumption on routes, a guaranteed service life of their batteries, as well as preventing the discharge of batteries below a critical level under various operating modes during their service life.

M e t ho d s. Methods of set theory, graph theory and linear approximation are used.

Re s u l t s. A mathematical model has been developed for the optimization problem of the charging infrastructure of the autonomous trolleybus fleet. The total reduced annual costs for the charging infrastructure are selected as the objective function. The model is formulated as a mathematical programming problem with a quadratic objective function and linear constraints.

Co n c l u s i o n. To solve the formulated problem of mathematical programming, standard solvers such as IBM ILOG CPLEX can be used, as well as, taking into account its computational complexity, the heuristic method of "swarm of particles".  The solution to the problem is to select the configuration of the location of the overhead wire sections on the routes and the durations of charging the trolleybuses at the terminal stops, which determine the corresponding number of stationary charging stations at these stops.

About the Authors

М. Ya. Kovalyov
The United Institute of Informatics Problems of the National Academy of Sciences of Belarus
Belarus

Mikhail Ya. Kovalyov - Dr. Sci. (Phys.-Math.), Professor, Deputy General Director, The United Institute of Informatics Problems of the National Academy of Sciences of Belarus.

Surganova st., 6, Minsk, 220012.



B. M. Rozin
The United Institute of Informatics Problems of the National Academy of Sciences of Belarus
Belarus

Boris M. Rozin - Cand. Sci. (Eng.), Head of the sector, The United Institute of Informatics Problems of the National Academy of Sciences of Belarus.

Surganova st., 6, Minsk, 220012.



I. A. Shaternik
The United Institute of Informatics Problems of the National Academy of Sciences of Belarus
Belarus

Ilya A. Shaternik - Engineer-programmer, The United Institute of Informatics Problems of the National Academy of Sciences of Belarus.

Surganova st., 6, Minsk, 220012.



References

1. Grygar D., Koháni M., Štefún R., Drgoňa P. Analysis of limiting factors of battery assisted trolleybuses. Transportation Research Procedia, 2019, vol. 40, pp. 229–235.

2. Hwang I., Jang Y. J., Ko Y. D., Lee M. S. System optimization for dynamic wireless charging electric vehicles operating in a multiple-route environment. IEEE Transactions on Intelligent Transportation Systems, vol. 19, iss. 6, рр. 1709–1726. https://doi.org/10.1109/TITS.2017.2731787

3. Ensuring sustainable development of urban public transport: A case study of the trolleybus system in Gdynia and Sopot (Poland) / M. Wołek [et al.] // J. of Cleaner Production, 2021, vol. 279. https://123807.doi.org/10.1016/j.jclepro.2020.123

4. Bartlomiejczyk M. Practical application of in motion charging: Trolleybuses service on bus lines. 18th International Scientific Conference on Electric Power Engineering (EPE), Kouty nad Desnou, 17–19 May 2017. Kouty nad Desnou, 2017, рр. 1–6. https://doi.org/10.1109/epe.2017.7967239

5. Jang Y. J. Survey of the operation and system study on wireless charging electric vehicle systems. Transportation Research Part C, 2018, vol. 95, pp. 844–866.

6. Chen Z., Liu W., Yin Y. Deployment of stationary and dynamic charging infrastructure for electric vehicles along traffic corridors. Transportation Research Part C, 2017, vol. 77, pp. 185–206.

7. Sevcik, J., Prikryl J. A Vehicle device tailored for hybrid trolleybuses and overhead wires implementation in SUMO. SUMO User Conference 2019, EPiC Series in Computing, Berlin, Germany, 13–15 May 2019. Berlin, 2019, vol. 62, pp. 145–157.

8. Ko Y. D., Jang Y. J. The optimal system design of the online electric vehicle utilizing wireless power transmission technology. IEEE Transactions on Intelligent Transportation Systems, 2013, vol. 14(3), pp. 1255–1265.

9. Goehlich D., Fay T.-A., Park S. Conceptual design of urban e-bus systems with special focus on battery technology. Proceedings of the 22nd International Conference on Engineering Design (ICED19), Delft, The Netherlands, 5–8 August 2019. Delft, 2019. https://doi.org/10.1017/dsi.2019.289

10. Han S. K. A practical battery wear model for electric vehicles charging applications. Applied Energy, 2014, vol. 113, pp. 1100–1108.

11. Millner A. Modeling lithium ion battery degradation in electric vehicles. 2010 IEEE Conference on Innovative Technologies for an Efficient and Reliable Electricity Supply, Waltham, MA, 27–28 September 2010. Waltham, 2010, pp. 349–356.

12. Pelletier S., Jabali O., Laporte G. Charge scheduling for electric freight vehicles. Transportation Research Part B, 2018, vol. 115, pp. 246–269.

13. Guschinsky N. N., Kovalyov М. Y., Rozin B. М. Optimization of slow-charging infrastructure for electric buses of one depot. Tanaevskie chteniya: Doklady` Devyatoj Mezhdunarodnoj nauchnoj konferencii, 30 marta 2021 g. [Proceedings of 9th International Scientific Conference ''Tanaev’s Readings'', Minsk, 30 March 2021], Minsk, Ob''edinennyj institut problem informatiki Nacional'noj akademii nauk Belarusi, 2021, pp. 153–157.

14. Scobtsov Yu. A., Fedorov E. E. Metae`vristiki. Metaheuristics, Donetsk, Noulidzh, 2013, 426 p. (In Russ.)

15. Poli, R. Analysis of the Publications on the Applications of Particle Swarm Optimisation / R. Poli // Journal of Artificial Evolution and Applications, 2008, vol. 2008, рp. 1–10. https://doi.org/10.1155/2008/685175

16. Guschinsky N. N., Zdanovich V. E., Rozin B. M. Optimization of part placement on a multi-position rotary table of a machine-tool. Informatika [Informatics], 2015, no. 4(48), pp. 57–72 (In Russ.)

17. Guschinsky N., Kovalyov M. Y., Rozin B., Brauner N. Fleet and charging infrastructure decisions for fast-charging city electric bus service. Computers and Operations Research, 2021, vol. 135. https://doi.org/10.1016/j.cor.2021.105449


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For citations:


Kovalyov М.Ya., Rozin B.M., Shaternik I.A. Approach to optimizing charging infrastructure of autonomous trolleybuses for urban routes. Informatics. 2021;18(4):79-95. (In Russ.) https://doi.org/10.37661/1816-0301-2021-18-4-79-95

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ISSN 1816-0301 (Print)
ISSN 2617-6963 (Online)