欢迎访问《汽车安全与节能学报》，
汽车安全与节能学报

汽车安全与节能学报 ›› 2025, Vol. 16 ›› Issue (5): 747-756.DOI: 10.3969/j.issn.1674-8484.2025.05.009

• 汽车节能与环保 • 上一篇    下一篇

基于车辆动力学和改进的FFRLS算法在线估算电动公交能耗

张馨方1(), 闫艺萍2,*(), 张哲3, 徐志刚1, 张立成1   

  1. 1.长安大学 信息工程学院，西安 710064，中国
    2.清华大学 车辆与运载学院，北京 100084，中国
    3.上海交通大学 船舶海洋与建筑工程学院，上海 200240，中国
  • 收稿日期:2024-09-11 修回日期:2025-06-12 出版日期:2025-10-31 发布日期:2025-11-10
  • 通讯作者: *闫艺萍，博士后研究员。E-mail：yan_yiping@mail.tsinghua.edu.cn。
  • 作者简介:张馨方（1998—），女（汉），山东，硕士研究生。E-mail：2022124113@chd.edu.cn。
  • 基金资助:
    国家自然科学基金重点国际（地区）合作研究项目(52220105001)

Online estimation of electric bus energy consumption based on vehicle dynamics and improved FFRLS algorithm

ZHANG Xinfang1(), YAN Yiping2,*(), ZHANG Zhe3, XU Zhigang1, ZHANG Licheng1   

  1. 1. School of Information Engineering, Chang’an University, Xi’an 710064, China
    2. School of Vehicle and Mobility, Tsinghua University, Beijing, 100084, China
    3. School of Naval Architecture, Ocean & Civil Engineering, Shanghai Jiao Tong University, Shanghai 200240, China
  • Received:2024-09-11 Revised:2025-06-12 Online:2025-10-31 Published:2025-11-10

RichHTML

5

PDF

31

摘要： 为提高电动公交汽车能耗预测模型在实时性、精度和可解释性方面的表现，该文提出了一种融合车辆动力学模型和数据驱动参数辨识的分工况能耗预测模型。该模型根据加速、匀速和减速3种工况，分别建立瞬时功率方程，并通过驾驶段划分计算累计能耗；通过引入带遗忘因子的递推最小二乘法（FFRLS）对模型参数进行在线识别，并结合粒子群优化算法（PSO）优化初始参数和遗忘因子，构建了具备实时在线预测能力的能耗模型IFFRLS。结果表明：所提模型的预测能力优异，最高决定系数（R2）达0.977，平均绝对百分比误差（MAPE）为11.16%，明显优于未改进的模型。

关键词: 电动公交汽车, 能耗, 参数辨识, 车辆动力学, 带遗忘因子的最小二乘法（FFRLS）

Abstract:

To improve the performance of electric bus energy consumption prediction models in terms of real-time capability, accuracy, and interpretability, this paper proposed a hybrid energy consumption prediction model that combined vehicle dynamics modeling and data-driven parameter identification for different operating conditions. The model established instantaneous power equations for acceleration, constant speed, and deceleration conditions, and calculated cumulative energy consumption through driving segment partitioning. The forgetting factors recursive least squares (FFRLS) method was introduced for online parameter identification, and the particle swarm optimization algorithm (PSO) was used to optimize the initial parameters and forgetting factors, resulting in the development of the real-time online predictive energy consumption model IFFRLS. The results show that the proposed FFRLS model performs excellently, achieving a maximum R-squared (R2) of 0.977 and a mean absolute percentage error (MAPE) of 11.16%, significantly outperforming the unmodified model.

Key words: electric bus, energy consumption, parameter identification, vehicle dynamics, forgetting factor recursive least squares (FFRLS)

中图分类号: