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Parameter Optimization of Urban Bus Powertrain Based on Operation Data
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Huiping Lai1, Zhengzhong Zheng1, Shaojie Wang1, Liang Hou1, Liang Su2
Automobile Technology | 2023, (1) : 55 - 62
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Automobile Technology | 2023, (1): 55-62
Parameter Optimization of Urban Bus Powertrain Based on Operation Data
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Huiping Lai1, Zhengzhong Zheng1, Shaojie Wang1, Liang Hou1, Liang Su2
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  • 1 Xiamen University, Xiamen 361104
  • 2 Xiamen King Long Motor Group Co., Ltd., Xiamen 361023
Published: 2023-01-24 doi: 10.19620/j.cnki.1000-3703.20210887
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In order to bring the energy saving potential of urban bus hybrid system to full play under variable passenger capacity and complex conditions during passenger transportation, the paper presents a powertrain parameter optimization method of hybrid electric bus based on operation data. Firstly, based on the Internet of Vechicle(IOV) data, K-Means clustering analysis using Kernel Principal Component Analysis (KPCA) and Particle Swarm Optimization (PSO) is used to construct representative driving conditions of an urban bus. In view of the characteristic of random variation of passenger number of urban bus in operation, a hybrid electric bus powertrain parameter bi-level optimization model is constructed based on the Optimal Latin Hypercube Design (Opt-LHD), Opt-LHD is used in the inner layer to generate number of passengers, the optimal control strategy of engine is used to transfer system response to outer layer optimization algorithm. Simulation results show that the optimized powertrain parameters have better adaptability to uncertain factors, and the fuel consumption is reduced by 9.97% compared with that before optimization.

Optimization of powertrain parameters  /  Number of passengers  /  Construction of driving cycle  /  Opt-LHD  /  Hybrid electric bus
Huiping Lai, Zhengzhong Zheng, Shaojie Wang, Liang Hou, Liang Su. Parameter Optimization of Urban Bus Powertrain Based on Operation Data[J]. Automobile Technology, 2023 , (1) : 55 -62 . DOI: 10.19620/j.cnki.1000-3703.20210887
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doi: 10.19620/j.cnki.1000-3703.20210887
  • Online Date：2025-12-07
  • Published：2023-01-24
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  • Revised：2021-10-28
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    1 Xiamen University, Xiamen 361104
    2 Xiamen King Long Motor Group Co., Ltd., Xiamen 361023
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