Semiconductor Superlattices: A model system for nonlinear transport

Electric transport in semiconductor superlattices is dominated by pronounced negative differential conductivity. In this report the standard transport theories for superlattices, i.e. miniband conduction, Wannier-Stark-hopping, and sequential tunneling, are reviewed in detail. Their relation to each other is clarified by a comparison with a quantum transport model based on nonequilibrium Green functions. It is demonstrated how the occurrence of negative differential conductivity causes inhomogeneous electric field distributions, yielding either a characteristic sawtooth shape of the current-voltage characteristic or self-sustained current oscillations. An additional ac-voltage in the THz range is included in the theory as well. The results display absolute negative conductance, photon-assisted tunneling, the possibility of gain, and a negative tunneling capacitance.Comment: 121 pages, figures included, to appear in Physics Reports (2001

Co-Design Based Lateral Motion Control of All-Wheel-Independent-Drive Electric Vehicles with Network Congestion

All-wheel-independent-drive electric vehicles (AWID-EVs) have considerable advantages in terms of energy optimization, drivability and driving safety due to the remarkable actuation flexibility of electric motors. However, in their current implementations, various real-time data in the vehicle control system are exchanged via a controller area network (CAN), which causes network congestion and network-induced delays. These problems could lead to systemic instability and make the system integration difficult. The goal of this paper is to provide a design methodology that can cope with all these challenges for the lateral motion control of AWID-EVs. Firstly, a continuous-time model of an AWID-EV is derived. Then an expression for determining upper and lower bounds on the delays caused by CAN is presented and with which a discrete-time model of the closed-loop CAN system is derived. An expression on the bandwidth utilization is introduced as well. Thirdly, a co-design based scheme combining a period-dependent linear quadratic regulator (LQR) and a dynamic period scheduler is designed for the resulting model and the stability criterion is also derived. The results of simulations and hard-in-loop (HIL) experiments show that the proposed methodology can effectively guarantee the stability of the vehicle lateral motion control while obviously declining the network congestion

Nonlinear Model Predictive Control with Terminal Cost for Autonomous Vehicles Trajectory Follow

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

Prediction for the Remaining Useful Life of Lithium–Ion Battery Based on RVM-GM with Dynamic Size of Moving Window

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

Direct Yaw-Moment Control of All-Wheel-Independent-Drive Electric Vehicles with Network-Induced Delays through Parameter-Dependent Fuzzy SMC Approach

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