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2000
Volume 18, Issue 2
  • ISSN: 2212-7976
  • E-ISSN: 1874-477X

Abstract

Background

Pure electric vehicles still have the problem with range anxiety, but hybrid vehicles can solve this problem well. Parameter optimization and adaptation of control strategies are the keys to improving the economy of hybrid vehicles.

Objective

This study aimed to improve the economy of hybrid vehicles to get more mileage in the same working conditions. On the basis of a large number of invention patents, this study establishes and optimizes the parameters and control strategy of hybrid vehicles in order to obtain better driving parameters and a more appropriate control strategy for hybrid vehicle drive system.

Methods

The key parameters of each component of the drive system are defined under dynamic objectives of hybrid vehicles. The control strategy adopts a logic gate-based approach to determine driving mode and braking of hybrid vehicles by limiting the speed, SOC, and power demand. Finally, the particle swarm optimization algorithm is used to optimize the key parameters to obtain the economic optimal solution without losing the vehicle power.

Results

In the China light-duty vehicle test cycle-passenger (CLTC-P) cycle condition, the optimized parameters can improve the fuel economy of fuel economy by 16.06%, and in the worldwide harmonized light vehicles test cycle (WLTC) cycle condition, the optimized parameters can improve the fuel economy of hybrid vehicles by 12.17%.

Conclusion

By establishing and optimizing driving system parameters and control strategy of the hybrid vehicles, this study improves the economy and achieves the expected effect without losing the vehicle power. However, in further research, driving conditions and mileage under different working conditions should be further studied, and on this basis, the optimization of control strategies should be continued.

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2024-05-23
2025-10-03

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Control Strategy and Parameter Optimization of Hybrid Vehicle Drive System
Recent Patents on Mechanical Engineering 18, 169 (2025); https://doi.org/10.2174/0122127976298241240506055008
/content/journals/meng/10.2174/0122127976298241240506055008
/content/journals/meng/10.2174/0122127976298241240506055008
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  • Article Type:     Research Article
Keyword(s): control strategy; drive system; Hybrid vehicle; modeling and simulation; parameter matching; parameter optimization; particle swarm optimization

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