As the requirements on powertrain efficiency of electric vehicles (EVs) are increasing, integrated motor-transmission (IMT) powertrain systems for EVs are becoming a promising solution. For the integration of IMT powertrain systems, the system state information and the actuator status are usually required for the closed-loop controller design or the on-board fault diagnosis. Embracing the demands, an observer for simultaneous estimation of input and system state of an IMT powertrain system is studied in this paper. It is well-known that controller area network (CAN) has been dominant in the vehicle network, which is used to communicate among controllers, sensors, and actuators. However, the CAN bus always induces time-varying delays when there are a number of communication nodes on the bus. The CAN-bus induced delay would result in vibrations in the vehicle powertrain or even deterioration of the entire closed-loop system. To deal with the CAN-bus induced delay in the estimation work for IMT powertrain systems, the potential random delays are considered in a three-state nonlinear model which represents the behavior of an IMT system. To estimate the input and state simultaneously, an adaptive unscented Kalman filter (AUKF) is adopted. As we know, the adopted AUKF has the benefits of dealing with system nonlinearities and calculating the noise covariance matrix automatically. Simulations and comparisons are carried out. We can see from the results that the proposed observer estimates the input and system state well. Moreover, the resulting estimation error is smaller comparing with the estimation error of the observer based on extended Kalman filter algorithm.

中文翻译：

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通过自适应无迹卡尔曼滤波器在控制器局域网环境中对集成电机传动系统进行同步输入和状态估计

随着对电动汽车 (EV) 动力总成效率的要求不断提高，用于 EV 的集成电机传动 (IMT) 动力总成系统正成为一种很有前景的解决方案。对于 IMT 动力总成系统的集成，闭环控制器设计或车载故障诊断通常需要系统状态信息和执行器状态。考虑到需求，本文研究了一种同时估计 IMT 动力系统输入和系统状态的观测器。众所周知，控制器局域网 (CAN) 在车辆网络中占据主导地位，用于在控制器、传感器和执行器之间进行通信。然而，当总线上有多个通信节点时，CAN 总线总是会引起时变延迟。CAN 总线引起的延迟会导致车辆动力系统的振动甚至整个闭环系统的恶化。为了在 IMT 动力系统估计工作中处理 CAN 总线引起的延迟，在代表 IMT 系统行为的三态非线性模型中考虑了潜在的随机延迟。为了同时估计输入和状态，采用了自适应无迹卡尔曼滤波器（AUKF）。众所周知，采用的 AUKF 具有处理系统非线性和自动计算噪声协方差矩阵的优点。进行了模拟和比较。从结果中我们可以看出，所提出的观察者很好地估计了输入和系统状态。而且，