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Domain-based electronic and electrical (E/E) architectures have been regarded as a possible upgrade to distributed E/E architectures currently used in electric vehicles. In a distributed E/E design, E/E components are directly connected to the automobile bus. Domain-based architectures split E/E components into distinct domains depending on their functions, which clearly benefits software upgrading and wire harness reduction. However, due to its heterogeneous topology with multiple network protocols, domain-based E/E architecture introduces complicated multilink and multinode delays into the control loop. The delays may degrade and even deteriorate the stability of adaptive cruise control (ACC) employing domain-based E/E architecture. To this end, this article proposes a heterogeneous-topology loop delay analysis by introducing a notion of poly-service loop delay. With a graphical pattern, the analytical process is presented in depth. The worst-case loop delay is calculated using an upper-boundary mathematic equation. Then, a hierarchical cyber-physical control method for ACC is designed. The upper level is intended to achieve desired acceleration based on vehicle and intervehicle motion states. And the lower level is intended to mitigate the negative impact of loop delays and provide reliable acceleration tracking. The results of cosimulation and hardware-in-loop experiment verify effectiveness of proposed approaches. [ABSTRACT FROM AUTHOR]

This paper presents a nonlinear model predictive control with terminal cost (NMPC–WTC) algorithm and its open/closed-loop system analysis and simulation validation for accurate and stable path tracking of autonomous vehicles. The path tracking issue is formulated as an optimal control problem. In order to improve the squeezing phenomenon of traditional NMPC, a discrete-time nonlinear model predictive controller with terminal cost is then designed, in which the state error of last step is augmented. The cost function of NMPC–WTC consists of two parts: (1) the traditional NMPC cost function responding to tracking errors and controller output, and (2) the augmented terminal cost. The algorithm was implemented on CasADi numerical optimization framework, which is free, open-source and developed for nonlinear optimization. The open-loop and closed-loop simulation results are then presented to demonstrate the improved performance in tracking accuracy and stability compared to traditional model predictive controller. [ABSTRACT FROM AUTHOR]

Considering the battery-failure-induced catastrophic events reported frequently, the early fault warning of batteries is essential to the safety of electric vehicles (EVs). Motivated by this, a novel data-driven method for early-stage battery-fault warning is proposed in this paper by the fusion of the short-text mining and the grey correlation. In particular, the short-text mining approach is exploited to identify the fault information recorded in the maintenance and service documents and further to analyze the categories of battery faults in EVs statistically. The grey correlation algorithm is employed to build the relevance between the vehicle states and typical battery faults, which contributes to extracting the key features of corresponding failures. A key fault-prediction model of electric buses based on big data is then established on the key feature variables. Different selections of kernel functions and hyperparameters are scrutinized to optimize the performance of warning. The proposed method is validated with real-world data acquired from electric buses in operation. Results suggest that the constructed prediction model can effectively predict the faults and carry out the desired early fault warning. [ABSTRACT FROM AUTHOR]

This article aims to address the coupled problem of the multiple-package transmissions (MPT) and time-varying network-induced delays (TND) within the networked motion control systems of smart electric vehicles (EVs). The recent development of advance digital sensors, actuators, and controllers leverage the worldwide upgrade of transportation in terms of electrification and intellectualization. However, for smart EVs, this updating may challenge the in-vehicle network and then the vehicle control performance. In particular, the nonsynchronous MPT and the TND are the key issues in the vehicle's integrated motion control due to the absence of the synchronization mechanism and limitation of communication bandwidth. To deal with the effects of coupled MPT and TND, a hybrid schedule-control framework is developed, where a new MPT scheduler is developed with a flexible time-triggered schedule strategy with fractional-type basic period to eliminate the effect of the MPT and reorganize the TND to a bounded range of delay. Furthermore, an H∞-based linear quadratic regulator control approach is designed to deal with the uncertainties caused by the TND as well as guarantee the system's stability. The effectiveness of the proposed scheme is verified in three cases by the real-time hardware-in-the-loop bed tests. [ABSTRACT FROM AUTHOR]

This paper presents a robust predictive control scheme with a graphic-based delay boundary analysis to mitigate the electric vehicle (EV) drivetrain oscillating issues, subject to the multi-channel compounding-construction loop delays. The application of Controller Area Network (CAN) in autonomous electric vehicles (AEVs) inevitably induces multi-channel compounding-construction loop delays into the control loop. The in-deep analyzing and understanding of the network-induced loop delays is critical for the electrified powertrain and its motion control. This study aims to guarantee, explicitly, the motion stability of AEV drivetrains as safe-critical and hard real-time applications. Firstly, a graphic-based constructional representation approach is presented for modeling of the compounding-construction loop delays. To resolve the upper bound of the compounding-construction loop delays further, a mathematic expression of delay boundary-envelopment analysis is derived. Secondly, based on the reasonable upper bound, Taylor series expansion is applied to make the system model with nonlinear uncertainties caused by the network-induced loop delays represent in the form of the convex polytope. Then, with the convex polytope of the drivetrain system model, a robust model predictive control (RMPC) approach is developed to enhance the system robustness against the unexpected network-induced delays. To attenuate the online calculation burden, a scheme combining off-line design and on-line synthesis is provided. Finally, the satisfactory motion control performance in both the co-simulations (Matlab&Carsim) and bench experimental tests can strongly verify the effectiveness of the proposed approaches. [ABSTRACT FROM AUTHOR]

Byline: Chuan Yin Tang, Tian Xia Zhang, Wanke Cao, Wei Zhou A four-degree-of-freedom half-body dynamic model of vehicle active suspension system is established. Road roughness height is modelled as a filtered white noise stochastic process. Based on control theory, the fuzzy control system of the active suspension is proposed. Using the Matlab/Simulink software, the simulation model is obtained. Simulation results indicate that the proposed active suspension system proves to be effective in the vibration isolation of the suspension system. What is more, a mechanical model of four-degree-of-freedom half-body of vehicle passive suspension system is constituted with the aid of software ADAMS. The combined simulation results demonstrate the effectiveness of the proposed mathematical model.

This paper investigates the robust direct yaw-moment control (DYC) through parameter-dependent fuzzy sliding mode control (SMC) approach for all-wheel-independent-drive electric vehicles (AWID-EVs) subject to network-induced delays. AWID-EVs have obvious advantages in terms of DYC over the traditional centralized-drive vehicles. However it is one of the most principal issues for AWID-EVs to ensure the robustness of DYC. Furthermore, the network-induced delays would also reduce control performance of DYC and even deteriorate the EV system. To ensure robustness of DYC and deal with network-induced delays, a parameter-dependent fuzzy sliding mode control (FSMC) method based on the real-time information of vehicle states and delays is proposed in this paper. The results of cosimulations with Simulink® and CarSim® demonstrate the effectiveness of the proposed controller. Moreover, the results of comparison with a conventional FSMC controller illustrate the strength of explicitly dealing with network-induced delays. [ABSTRACT FROM AUTHOR]

Accurate prediction of the remaining useful life of a lithium–ion battery (LiB) is of paramount importance for ensuring its durable operation. To achieve more accurate prediction with limited data, this paper proposes an RVM-GM algorithm based on dynamic window size. The method combines the advantages of the relevance vector machine (RVM) algorithm and grey predictive model (GM). The RVM is applied to provide the relevance vectors of fitting function and output probability prediction, and the GM is used to obtain the trend prediction with limited data information. The algorithm is further verified by the NASA PCoE lithium–ion battery data repository. The experimental prediction results of different batteries data show that the proposed algorithm has less error while applying a dynamic window size compared with a fixed window size, while it has higher prediction accuracy than particle filter algorithm (PF) and convolutional neural network (CNN), which has verified the effectiveness of the proposed algorithm. [ABSTRACT FROM AUTHOR]