The prediction of energy consumption is the primary goal of an intelligent energy management system (IEMS). Based on the actual road–traffic conditions, the vehicle energy consumption on the whole planned path can be predicted online by road condition recognition or speed sequence prediction. Because the speed sequence prediction required by the latter cannot accurately reflect the real dynamic characteristics of vehicle speed such as acceleration and deceleration changes due to the random factors of traffic or human beings, which will greatly affect the predicting accuracy, especially on the urban road with complex working conditions. Therefore, based on the analysis of the cumulative relationship between vehicle speed characteristics and energy consumption, this study proposes a prediction method of vehicle driving energy consumption based on the statistical characteristics of vehicle speed, regardless of the accuracy of the prediction of vehicle speed sequence, including the establishment of a long-term vehicle speed feature prediction model and energy consumption prediction model by BP and SVM algorithms. Finally, its rationality is validated based on the authentic data with an accuracy of about 95%, significantly improved compared with that based on long-term vehicle speed prediction.

中文翻译：

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基于速度特征预测的计划路线上的车辆能耗预测

能耗预测是智能能源管理系统（IEMS）的主要目标。根据实际的道路交通状况，可以通过道路状况识别或速度序列预测来在线预测整个规划路径上的车辆能耗。由于后者所需的速度序列预测不能准确反映车速的真实动态特性，例如由于交通或人为因素引起的加速和减速变化，这将极大地影响预测精度，尤其是在城市道路上。复杂的工作条件。因此，基于对车速特性和能耗之间累积关系的分析，这项研究提出了一种基于车速统计特性的车辆行驶能耗预测方法，而不论车速序列预测的准确性如何，包括建立长期的车速特征预测模型和能耗预测模型通过BP和SVM算法。最后，基于真实数据验证了其合理性，其准确性约为95％，与基于长期车速预测的准确性相比，显着提高。