The invention provides a new energy bus driving range estimation method oriented to working conditions and drivers. According to the method, operation characteristic parameters are extracted, principal component analysis and clustering are carried out in sequence when different persons drive a vehicle to obtain different working condition type classifications which have correlation with objective factors such as road conditions and environment and subjective factors such as specific driver behaviors and habits, and the defects caused by manual explanation of principal components are avoided; real complex working conditions are covered as comprehensively as possible by utilizing machine learning. Through training and online application of the unit distance average energy consumption prediction model based on the deep belief network, the new energy public transport vehicle can rapidly identify the real-time working condition according to the actual driver and vehicle operation parameters, and the remaining driving mileage of the vehicle under the current working condition is accurately estimated. And the driving range estimation result can be updated along with the change of the vehicle position and the working condition.
本发明提供了一种面向工况与驾驶员的新能源公交车续驶里程估计方法,其通过依次对不同人员驾驶车辆时运行特征参数进行提取、主成分分析及聚类得到与路况、环境等客观因素以及特定驾驶员行为、习惯等主观因素均具有相关性的不同工况类型划分,避免了对主成分进行人为解释时的弊端,利用机器学习实现了对真实复杂工况尽可能全面地覆盖。通过对基于深度置信网络的单位距离平均能耗预测模型的训练及在线应用,能够使新能源公交车辆根据实际的驾驶员及车辆运行参数迅速地识别出实时工况,较为准确地估计出当前工况下车辆的剩余续驶里程,并可随着车辆位置与工况的变化更新续驶里程估计结果。
New energy bus driving range estimation method oriented to working conditions and drivers
面向工况与驾驶员的新能源公交车续驶里程估计方法
2023-10-10
Patent
Electronic Resource
Chinese
IPC: | B60L PROPULSION OF ELECTRICALLY-PROPELLED VEHICLES , Antrieb von elektrisch angetriebenen Fahrzeugen |
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