Method for synthesizing optimal parameters of a hybrid powertrain with an electrochemical generator and a rechargeable energy storage system

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Abstract

BACKGROUND: Due to the rapid growth of the electrified transport sector, one of the key issues in designing electrified vehicles is determining the optimal parameters of hybrid powertrains. This paper contains the study of an extra-large class urban passenger vehicle equipped with a hybrid powertrain (HPT) consisting of a rechargeable electrical energy storage system and an electrochemical generator. In order to define optimal parameters of a HPT, the method for synthesis of optimal parameters that accounts for the main design requirements and operational features of the studied vehicle is proposed.

AIM: Determination of the optimal parameters of a hybrid powertrain for a vehicle, taking into account technical and operational parameters and the actual operating modes of an extra-large class urban passenger vehicle.

METHODS: Optimization of the hybrid powertrain parameters is performed using a global search optimization algorithm included in the GlobalToolbox package of the MATLAB software. Simulation modeling methods in the Simulink software are used to calculate the optimization criterion.

RESULTS: The paper presents a formulation of the optimization problem for the hybrid powertrain parameters, a description of the simulation mathematical model in the Simulink software, verification of the mathematical model using the experimental data, and the results of synthesis of optimal parameters for various cell chemistries of the rechargeable electrical energy storage system.

CONCLUSION: As the study result, optimal parameters of the HPT of the extra-large class urban passenger vehicle were defined considering main technical and operational parameters. The practical value of this study lies in the possibility of using the proposed method for determining optimal parameters of hybrid powertrains in the design of commercial vehicles, in particular extra-large class passenger vehicles with a hybrid powertrain based on an electrochemical generator and a rechargeable electrical energy storage system.

About the authors

Viktor R. Anisimov

Moscow Polytechnic University; KAMAZ Innovation Center

Author for correspondence.
Email: rabota.viktor.1999@mail.ru
ORCID iD: 0000-0003-1268-6604
SPIN-code: 5036-8965

Postgraduate of the Advanced Engineering School of Electric Transport

Russian Federation, Moscow; Moscow

Alexander V. Klimov

Moscow Polytechnic University; KAMAZ Innovation Center

Email: klimmanen@mail.ru
ORCID iD: 0000-0002-5351-3622
SPIN-code: 7637-3104

Cand. Sci. (Engineering), Assistant professor of the Advanced Engineering School of Electric Transport, Head of the Electric Vehicles Department

Russian Federation, Moscow; Moscow

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Supplementary files

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2. Fig. 1. Methods to control hybrid energy powertrains.

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3. Fig. 2. Simulation model of a vehicle in Simulink.

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4. Fig. 3. Verification of the model for calculating the heat balance of a rechargeable electric energy storage system: а, experimental study of battery cooling; b, mathematical modeling of battery cooling; c, experimental study of battery warming up; d, mathematical modeling of battery warming up.

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5. Fig. 4. Verification of the model for calculating the heat balance of a vehicle: а, experimental study of cabin heating; b, mathematical modeling of cabin heating; c, experimental study of cabin cooling; d, mathematical modeling of cabin cooling.

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6. Fig. 5. Verification of the vehicle energy balance calculation.

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7. Fig. 6. Results of the solution of the optimization problem for the chemistry of Lithium Iron Phosphate (LFP) battery cells: а, change in specific energy consumption depending on the battery capacity; b, change in battery capacity during the optimization process; c, change in specific energy consumption depending on the generator power; d, change in generator power during the optimization process.

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8. Fig. 7. Results of the solution of the optimization problem for the chemistry of NMC523 battery cells: а, change in specific energy consumption depending on the battery capacity; b, change in battery capacity during the optimization process; c, change in specific energy consumption depending on the generator power; d, change in generator power during the optimization process.

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9. Fig. 8. Results of the solution of the optimization problem for the chemistry of Lithium Titanate Oxide (LTO) battery cells: а, change in specific energy consumption depending on the battery capacity; b, change in battery capacity during the optimization process; c, change in specific energy consumption depending on the generator power; d, change in generator power during the optimization process.

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