基于数据驱动的商用车能耗预测及优化模型研究  

Optimized Data-Driven Model for Predicting Commercial Vehicle Energy Consumption

作　　者：李帅雨 师国东 胡明茂[1,2] 宫爱红 龚青山[1] 方剑[3] 谭浩 LI Shuaiyu;SHI Guodong;HU Mingmao;GONG Aihong;GONG Qingshan;FANG Jian;TAN Hao(School of Mechanical Engineering,Hubei University of Automotive Technology,Shiyan 442002,Hubei,China;Hubei Key Laboratory of Automotive Power Transmission and Electronic Control,Shiyan 442002,Hubei,China;Dongfeng Commercial Vehicle Co.,Ltd.,Shiyan 442002,Hubei,China)

机构地区：[1]湖北汽车工业学院机械工程学院,湖北十堰442002 [2]汽车动力传动与电子控制湖北省重点实验室,湖北十堰442002 [3]东风商用车有限公司,湖北十堰442002

出　　处：《汽车工程学报》2025年第2期164-176,共13页Chinese Journal of Automotive Engineering

基　　金：国家自然科学基金项目(52375508);湖北省教育厅重点项目(D20211803);湖北汽车工业学院博士基金项目(BK202001)。

摘　　要：以国内某燃油商用车为例,利用车联网大数据平台和神经网络模型构建适用于商用车的能耗优化预测模型。将车辆历史运行数据进行预处理,分析车辆运行特征数据之间的相关性。基于双向长短期记忆网络(BiLSTM),结合车辆数据特征引入自适应权重的注意力机制,使用改进鲸鱼优化算法(IWOA)对模型的网络超参数组合进行优化,构建了IWOA-BiLSTM-Attention商用车能耗优化预测模型。对比分析了多个模型在不同驾驶工况下的预测效果,结果显示,在实际驾驶工况下,优化模型相较于原模型的均方根误差、平均绝对误差分别降低了约26.73%和20.0%,验证了该优化模型在商用车能耗预测上的可行性。Taking a domestic fuel commercial vehicle as an example,an energy consumption optimization prediction model suitable for commercial vehicles was constructed using the Internet of Vehicles big data platform and a neural network model.Firstly,the historical vehicle operation data was preprocessed to analyze the correlation between different vehicle operation characteristic data.Secondly,an adaptive weight attention mechanism was introduced based on Bi-directional Long Short-Term Memory(BiLSTM)and the characteristics of vehicle data.The Improved Whale Optimization Algorithm(IWOA)was used to optimize the network hyperparameters of the model,leading to the construction of the IWOA-BilSTM-Attention commercial vehicle energy consumption optimization prediction model.Finally,the prediction performance of multiple models under different driving conditions were compared and analyzed.The results show that under actual driving conditions,the root mean square error and the mean absolute error of the optimized model are reduced by approximately 26.73%and 20.0%,respectively,compared with the original model.This verifies the feasibility of the optimized model for predicting the energy consumption of commercial vehicles.

关 键 词：商用车能耗预测 时间序列 神经网络 改进鲸鱼优化算法 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] U473.1[自动化与计算机技术—控制科学与工程]