Accurate vehicle state information is crucial for safe driving and dynamic control of vehicles. Vehicle state estimation under unknown noise conditions is an important research topic. A state estimation method based on enhanced adaptive unscented Kalman filter (EAUKF) is proposed to solve vehicle estimation under unknown noise conditions. The general exponential attenuation adaptive Kalman filter algorithm does not attenuate the historical data enough when the noise statistics change rapidly, thus leading to the state variable’s inaccurate estimation. To improve the estimation accuracy of vehicle state variables, the exponential attenuation factor $B$ was further designed according to the variation of noise variance, and the influence of the latest data on state estimation was more considered. Based on the longitudinal dynamics modeling, the EAUKF method is applied to vehicle state estimation. Compared with the standard exponential weighted adaptive Kalman filtering algorithm and the average weighted adaptive Kalman filtering algorithm, the state variable estimation accuracy of the vehicle in this article is improved.

The lithium-ion battery is the key power source of a hybrid vehicle. Accurate real-time state of charge (SOC) acquisition is the basis of the safe operation of vehicles. In actual conditions, the lithium-ion battery is a complex dynamic system, and it is tough to model it accurately, which leads to the estimation deviation of the battery SOC. Recursive least squares (RLS) algorithm with fixed forgetting factor is widely used in parameter identification, but it lacks sufficient robustness and accuracy when battery charge and discharge conditions change suddenly. In this paper, we proposed an adaptive forgetting factor regression least-squares–extended Kalman filter (AFFRLS–EKF) SOC estimation strategy by designing the forgetting factor of least squares algorithm to improve the accuracy of SOC estimation under the change of battery charge and discharge conditions. The simulation results show that the SOC estimation strategy of the AFFRLS–EKF based on accurate modeling can effectively improve the estimation accuracy of SOC.

Domain generalization detection of fault categories in industrial equipment diagnosis is a challenging problem, as it demands a model with high generalization performance. Previous methods have primarily focused on a closed set, implying that the label spaces of the training and testing sets are identical. However, this approach is insufficient to reason about the intricate industrial dynamics. In this article, we fuse domain generalization and open-set recognition to introduce a new domain generalization fault diagnosis scenario, called open-set domain generalization. It learns from different source domains to achieve high performance on unknown target domains, where the distribution and label set can be different for each source and target domain. The problem can be more applicable to real-world industrial applications. In addition, we propose a multidomain contrastive coding (MDCC) framework to learn open-set domain generalizable representations. We conduct multidomain contrastive coding by designing a new contrastive coding task and loss to preserve domain-unique knowledge and generalize knowledge across domains simultaneously. Experimental results on two multidomain datasets demonstrate that the proposed MDCC framework outperforms prior methods in open-set domain generalization.

This paper proposes a dual level reinforcement estimator (DLRE) to estimate the electric mining haul trucks' (EMHTs') total mass and road slope. To design the estimator, the longitudinal dynamics model of EMHT is built and the model's parameters are identified by a recursive least square (RLS) with double forgetting factors. This DLRE consists of an initial level estimation and an extended level estimation. In the initial estimation, the total mass and the road slope are preliminarily estimated. In the extended estimation, to prevent the estimated mass error from leading to great errors in the road slope, the road slope is first estimated and then the total mass is estimated based on the estimated road slope. The DLRE overcomes the defects of the traditional recursive-strategy-based estimators whose cumulative estimation errors often lead to poor estimation performance or even lead to estimation divergence. The DLRE performance is validated with an EMHT of 930E, and the experimental results show that the total mass and the road slope are estimated with higher accuracy using the DLRE.