通过改善驾驶人驾驶行为是降低车辆能耗的重要方式，目前众多学者针对生态驾驶行为开展了大量研究，提出多种生态驾驶建议，但是目前缺少对纯电动公交车驾驶人驾驶水平的评估方法。为了降低纯电动公交车运营成本，减少能量消耗，本研究采集纯电动公交车自然驾驶数据。首先，对原始数据进行了统一采样频率、数据清洗、参数补充等预处理；其次，选择驾驶人操作特征参数和车辆运行参数，分析公交车驾驶人驾驶行为对能耗的影响，根据影响能耗的驾驶行为特征参数，以公交车起始站点到终点站之间的行程为单位，提出平均起步加速时间、猛踩加速踏板次数、持续高踏板开度时长、急加速次数、减速时制动占比、低速行驶时长、经济车速行驶时长7类影响能耗的驾驶事件；之后，通过各类驾驶事件参数与百公里能耗的Pearson相关性系数，建立生态驾驶水平评估的多元回归模型，对驾驶人行程中的驾驶水平进行评分；最后，基于评估模型搭建生态驾驶辅助反馈平台，帮助车队管理者更好地了解驾驶人的生态驾驶水平。结果表明：提出的基于驾驶事件的纯电动公交车生态驾驶水平评估模型对于驾驶行为生态性的评估精确度为93.52%，平均误差为6.48%，模型对于生态驾驶得分的计算具有较好的效果，以此搭建的纯电动公交驾驶人生态驾驶辅助反馈平台可使车队管理者了解公交车运行状况以及驾驶人的生态驾驶水平，帮助驾驶人了解自身驾驶情况。

Improving driver behavior is an important way to reduce vehicle energy consumption. Currently, many scholars have conducted extensive research on eco-driving behavior and proposed various eco-driving suggestions. However, there is a lack of evaluation methods specifically aimed at assessing the driving performance of pure electric bus drivers. In order to reduce the operating costs and energy consumption of electric buses, this study co-llected naturalistic driving data from electric buses. Firstly, the original data was pre-processed with unified sampling frequency, data cleaning, and parameter supplementation. Secondly, by selecting driver behavior characteristic parameters and vehicle operation parameters, the impact of bus driver driving behavior on energy consumption was analyzed. Based on the identified behavioral parameters that influence energy use, and taking each trip from the departure station to the terminal station as a unit, seven energy-related driving events were proposed: average start-up acceleration time, number of rapid accelerator pedal presses, duration of sustained high pedal opening, number of sudden accelerations, braking proportion during deceleration, duration of low-speed driving, and duration of economic speed driving. Afterwards, a multiple regression model for eco-driving level evaluation was established by analyzing the Pearson correlation coefficients between various driving event parameters and energy consumption per 100 kilometers, and the driving level of the driver during the journey was scored. Finally, based on the evaluation model, an eco-driving assistance feedback platform was built to help fleet managers better understand the eco-driving level of drivers. The results show that the proposed eco-driving level evaluation model for electric buses based on driving events has an accuracy of 93.52% and an average error of 6.48% in evaluating the eco-driving behavior. The model has a good effect on calculating eco-driving scores. The eco-driving assistance feedback platform can help fleet managers understand the operation status of buses and the eco-driving level of drivers, and help drivers understand their own driving situation.