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电动汽车滑行制动能量回收过程驾驶性综合评价方法研究 被引量:1

Study on Comprehensive Drivability Evaluation Method During Coasting Energy Recovery for Electric Vehicles
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摘要 电动汽车滑行制动能量回收过程的驾驶性是车辆纵向动力学瞬态品质的定性描述,为定量评价能量回收制动的驾驶性,对滑行制动过程中的减速特性进行分析,提取了最大减速度、减速滑行距离、最大减速度变化率、减速度变化率稳态占比4个客观指标,并通过相关性分析验证主观评价与客观参数的一致性。基于客观评价指标,利用非线性回归方法建立了主观评价预测模型。通过7台新能源车型共计18种能量回收模式下的综合评价,验证了评价体系的有效性和实用性。结果表明,基于主客观综合分析建立的评价体系能把主观感受和客观数据有机结合,实现驾驶性的量化评价。 The drivability in the process of coasting energy recovery is vital to the transient quality of vehicle longitudinal dynamics for electric vehicles.In order to quantitatively evaluate the drivability during energy recovery,the deceleration characteristics were analyzed when the vehicle was coasting,and the four objective indexes were obtained,i.e.the maximum deceleration,the deceleration distance,the maximum deceleration rate and the steady-state proportion of deceleration rate.The consistency between the subjective evaluation and objective parameters was verified by correlation analysis.Based on the objective evaluation indexes,the subjective evaluation prediction model was established by using the nonlinear regression method.The effectiveness and practicability of the evaluation system were verified by the comprehensive evaluation considering 7 models of new energy vehicles with 18 energy recovery modes.The results show that the comprehensive evaluation system combining the subjective assessments based on human feelings and the objective evaluation data can achieve the quantitative evaluation of drivability.
作者 李朝斌 易侃 LI Chaobin;YI Kan(State Key Laboratory of Vehicle NVH and Safety Technology,Chongqing 401122,China;China Automotive Engineering Research Institute,Chongqing 401122,China;Intelligent Connected Vehicle Inspection Center(Hunan)of CAERI Co.,Ltd.,Changsha 410000,China)
出处 《汽车工程学报》 2022年第3期314-320,共7页 Chinese Journal of Automotive Engineering
基金 重庆市自然科学基金博士后科学基金(cstc2020jscx-dxwtBX0019)。
关键词 驾驶性 主观评价 客观指标 相关性分析 非线性回归 主观预测模型 drivability subjective evaluation objective indicators correlation analysis nonlinear regression subjective prediction model
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