基于混合算法的纯电动车整车质量及道路坡度估计

Estimation of Gross Mass of Battery Electric Vehicle and Road Gradient Based on Hybrid Algorithm

针对纯电动车行驶中整车质量与道路坡度的参数估计问题,根据整车质量具有稳变性及道路坡度具有时变性的特点,在车辆纵向动力学的基础上,提出了一种基于混合算法的整车质量及道路坡度估计方法,并将该方法应用于纯电动汽车起步阶段。使用对稳变量具有较好估计效能的遗忘因子递归最小二乘法进行了整车质量的估计,并将输出的质量结果作为坡度估计的输入参数之一。使用自适应卡尔曼滤波进行道路坡度估计,通过引入带有遗忘因子的噪声估计器,降低了外界噪声统计特性无法归纳的噪声影响,进而提高坡度估计精度。选取平地与微斜坡路段进行电动车起步试验,根据车辆起步过程速度低的特点,忽略空气阻力。使用全球导航卫星系统(GNSS)终端和车辆控制器局域网(CAN)总线采集所需数据,进行离线计算。结果表明:选择一段变坡度道路验证算法的有效性,该路段整车质量估计结果最终收敛至真实值10 kg以内,坡度估计结果具有较小误差;混合算法可以较为准确地估计整车质量和不断变化的道路坡度;二次起步试验质量估计结果具有相同的收敛趋势且质量误差小于2%,虽然坡度估计误差略有增大,但误差仍低于0.6%,表明混合算法在纯电动汽车起步过程中具有较高的估计精度。

In view of the parameter estimation of gross vehicle mass and road gradient during the driving of battery electric vehicles,according to the characteristics of the vehicle mass with steady variability and road gradient with time variability,on the basis of vehicle longitudinal dynamics,an method for estimating gross vehicle mass and road gradient based on a hybrid algorithm is proposed,and the method is applied to the starting stage of battery electric vehicles.The gross vehicle mass is estimated by using the forgetting factor recursive least square algorithm with good estimation efficacy for steady variables,and the output result of mass is regarded as one of the input parameters for gradient estimation.The road gradient is estimated with adaptive Kalman filtering,and the noise influence that cannot be concluded with external noise statistical characteristics is reduced by introducing a noise estimator with forgetting factor to improve the estimation accuracy of road gradient.The electric vehicle starting test is carried out on the selected flat ground and micro-slope sections,and air resistance is ignored based on the characteristics of low speed during vehicle starting.The required data are collected with the Global Navigation Satellite System(GNSS)terminal and vehicle controller area network(CAN)bus to perform offline calculations.The result shows that(1)a road section with variable gradient is selected to verify the validity of the algorithm,and the gross vehicle mass estimation result of the section is converged within 10 kg of the true value eventually with small error in the gradient estimation result;(2)the estimations of gross vehicle mass and changing of road gradient can be accurately estimated by using hybrid algorithm;(3)the mass estimation result of the twice starting test has the same convergence trend and the mass error is less than 2%,although the gradient estimation error is increased slightly,it is still less than 0.6%,which indicates the high estimation accuracy at starting stage of battery electric vehicle by using the hybrid algorithm.