Browsing by Author "Lv, Jiaming"

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    Enhanced data-driven economic assessment of fuel cell electric buses utilizing an improved Markov chain Monte Carlo approach
    (Elsevier, 2025-02-10) Yuan, Xinjie; Xu, Miao; Hou, Zhongjun; Chen, Wenchuang; Huang, Yun; Lv, Jiaming; Xu, Xudong; Huang, Luofeng
    Accurate economic assessment of proton exchange membrane fuel cell (PEMFC) vehicles is essential for optimizing control strategies in the PEMFC industry, which is largely driven by the need to reduce costs. Traditional data-driven approaches have focused on reconstructing typical driving cycles from real-world speed data, often overlooking the intensity and acceleration of these cycles. These factors are crucial for water and heat management in PEMFCs and can lead to inaccurate estimates of hydrogen consumption. This paper introduces a novel algorithm for typical driving cycles reconstruction based on real-world data, named the improved two-dimensional Markov Chain Monte Carlo (2D MCMC) approach using Metropolis-Hastings (M − H) sampling. The approach innovatively encodes the integration of real-time vehicle speed and acceleration sequences into a hierarchical 2D state transition probability matrix. To optimise both accuracy and computation time, the M − H based sampler is newly introduced to generate typical driving cycle without the computational burden of multiplying large matrices. Moreover, by integrating the agglomerative nesting (AGNES) alongside a comprehensive evaluation system that incorporates simulation and bench testing, the proposed approach effectively weights real-world route conditions in the economic assessment. Case studies involving 10 PEMFC hybrid buses in Shanghai, China, validate the effectiveness and robustness of the proposed method. Comparative analyses show that the relative errors in hydrogen consumption per 100 km between the reconstructed and real-world driving cycles are within 1.20–3.01% for all ten buses in Shanghai, with computation times reduced by up to 12.60% compared to the existing methods.