High-accuracy driving cycle test set construction framework for vehicle calibration and measurement
車両校正と計測のための高精度運転サイクルテストセット構築フレームワーク (AI 翻訳)
Fanyu Meng, Yanteng Li, Ziliang Zhao, Bin Guo, Man Zhang, Jun Zhao, Zhangu Wang
🤖 gxceed AI 要約
日本語
本研究は、車両のエネルギー消費と排出評価のための代表的な運転サイクルを高精度かつ高速に生成するマルコフ連鎖発散収束領域法（MC-DCD）を提案する。青島市の実運転データを用いた検証では、平均統計偏差0.11%を達成し、従来法と比較して97倍の精度向上と100倍以上の高速化を実現した。生成された高品質データセットは、ニューラルネットワークモデルの訓練基盤としても有用である。
English
This study proposes a Markov Chain Divergence-Convergence Domain (MC-DCD) method for generating representative driving cycles with high accuracy and speed. Validated using real-world data from Qingdao, it achieved an average statistical deviation of 0.11%, improving accuracy by 97-fold and speed by over 100-fold compared to classical methods. The generated high-fidelity datasets also serve as training data for neural networks.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
本手法は、日本の自動車メーカーや部品サプライヤーが燃費・排出ガス試験の効率化に活用できる可能性があるが、日本の実走行データへの適用やJC08/WLTCとの比較が必要。また、SSBJや温暖化対策における運輸部門の脱炭素に向けた評価基盤としても間接的に貢献しうる。
In the global GX context
This technical method improves the accuracy and speed of driving cycle generation for vehicle energy and emissions testing, which supports global efforts to decarbonize transportation. However, its direct relevance to GX disclosure frameworks (e.g., TCFD, ISSB) is limited, as it focuses on testing rather than accounting or reporting.
👥 読者別の含意
🔬研究者:A novel Markov Chain method for driving cycle generation that outperforms classical approaches in both accuracy and speed, useful for vehicle energy and emissions research.
🏢実務担当者:Automotive calibration and testing teams can adopt this method to generate high-fidelity driving cycles for more efficient vehicle certification and optimization.
📄 Abstract（原文）
The construction of representative driving cycles is crucial for vehicle energy consumption analysis, emission evaluation, and control strategy optimization. However, classical approaches face trade-offs between exploration scope, computational efficiency, and perturbation controllability. This study proposes a novel Markov Chain Divergence-Convergence Domain (MC-DCD) method to address these challenges. The proposed approach first implements reachable domain screening for candidate cycle sub-segments through divergence-convergence domain analysis, ensuring thorough exploration of potential optimal solutions. Subsequently, it achieves minimal perturbation to existing high-quality cycles through rapid reconstruction of local segments in suboptimal cycles. Validated using 1 million seconds of real-world driving data from Qingdao, the MC-DCD method generated 100,000 candidate segments in just 0.66 s. The final driving cycle showed an average statistical deviation of only 0.11%. Compared with classical Markov Chain (MC) and Markov Chain-Genetic Algorithm (MC-GA) methods, the proposed approach improved accuracy by 97-fold and 12.5-fold, respectively, while achieving over 100-fold and 10-fold acceleration in candidate cycle generation speed. Beyond enhancing traditional vehicle evaluation, the massive high-fidelity datasets generated by this method serve as a critical training foundation for neural network-based models, effectively mitigating the challenge of large-scale data scarcity.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.1177/09544070261431699first seen 2026-05-05 22:07:50
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。