四轮驱动电动汽车质心侧偏角与轮胎侧向力非线性鲁棒融合估计

针对四轮驱动电动汽车质心侧偏角和轮胎侧向力难以直接测量的问题，考虑系统未建模的动态特性、模型参数摄动、系统过程噪声及测量噪声等因素，提出了一种基于遗忘因子递归最小二乘法(FFRLS)与鲁棒容积卡尔曼滤波(RCKF)的联合估计方法。基于FFRLS法对整车质量进行实时估计，并将极大值背景下的估计误差最小化嵌入标准容积卡尔曼滤波(CKF)以实现RCKF,提出了联合估计算法的改进策略，有效提高了复杂工况下滤波对模型参数摄动以及未建模噪声的抗干扰能力，可以实现质心侧偏角与轮胎侧向力的精准估计。在CarSim/Simulink联合仿真环境下，采用不同工况验证了算法的准确性、鲁棒性和抗干扰性。在四轮驱动电动汽车实车平台上分析了算法的有效性。研究结果表明，所提方法比RCKF和CKF精度更高，解决了复合工况下四驱电动汽车质心侧偏角和轮胎侧向力的联合估计问题。

Nonlinear Robust Fusion Estimation of Centroid Sideslip Angle and Tire Lateral Forces for Four-wheel-drive Electric Vehicles

It was rather difficult to measure the centroid side slip angle and the tire lateral forces of the four-wheel-drive electric vehicles directly. Considering the unmodeled dynamic characteristics of the systems, model parameter perturbations, system process noises and measurement noises, a joint estimation method was proposed based on FFRLS and RCKF. Based on applying FFRLS to estimate the mass of the vehicle in real time and the estimation error minimization in the background of the maximum value embedded into the standard cubature Kalman filter to realize RCKF. The improved strategy of the joint estimation algorithm was proposed, which effectively improved the anti-interference ability of the filter to the model parameter perturbations and the unmodeled noises under composite conditions. It may  realize the accurate estimation of the centroid sideslip angle and tire lateral forces. By CarSim/Simulink, the accuracy, robustness and anti-interference of the algorithm were verified in different conditions. Through the actual vehicle platform of four-wheel-drive electric vehicle, the validity of the algorithm was verified. Research show that the results of the proposed method are more accurate than that of RCKF and the standard Cubature Kalman filter. The problems of joint estimation of centroid side slip angle and tire lateral forces of four-wheel-drive electric vehicle are solved under composite conditions.