The path-following problem for four-wheel independently actuated autonomous ground vehicles is investigated in this paper. A novel output constraint controller is proposed to deal with the lateral offset control in path following and maintain the vehicle lateral stability in the presence of tire sliding effects. The innovations of this work lie in the following two aspects: 1) A novel output constraint control strategy, namely, the hyperbolic projection method, is proposed to strictly bound the lateral offset to prevent the vehicle from transgressing the safety bound in path following; 2) an adaptive and robust linear quadratic regulator controller is adopted to obtain the optimal active front-wheel steering and direct yaw-moment control inputs with vehicle lateral stability consideration and to eliminate the effect of parameter uncertainties. CarSim-Simulink joint simulation results indicate that the proposed controller can compactly bound the lateral offset to avoid transgressing the safe boundary during path following, particularly in extreme driving conditions, in the presence of tire sliding effects and system uncertainties.

State of health (SOH) is critical to evaluate the life expectancy of lithium-ion battery (LIB), thus should be estimated accurately in practical applications. This paper proposes a computationally efficient model-based method for SOH estimation of LIB. A revised Lorentzian function-based VC (RL-VC) model is exploited to accurately capture the voltage plateaus of LIB which reflect the material-level phase transition phenomenon. A full set of new features of interest (FOIs) are extracted by simply fitting the RL-VC model leveraging data collected from the constant-current charging process. Correlation analysis is then performed for the captured FOIs, based on which linear models are calibrated to estimate the battery SOH. The proposed method is validated with experimental data from different battery chemistries. Results show that the extracted FOIs have high linearities with the battery capacity, suggesting a good potential for SOH estimation and better feasibility over traditionally-used methods. The proposed method shows a high accuracy for battery SOH estimation and an expected robust performance against the initial aging status and practical cycling condition.

The accurate estimation of the state-of-health (SOH) is vital to the life management of lithium-ion batteries (LIBs). In this article, we propose a fusion-type SOH estimation method by combining the model-based feature extraction and data-based state estimate. Particularly, a novel model-based voltage construction method is proposed to eliminate the unfavorable numerical condition and reshape the disturbance-free incremental capacity (IC) curves. Leveraging the modified IC curves, a set of informative features-of-interest is extracted and evaluated, while eventually several cautiously selected ones are used to estimate the SOH of LIBs accurately. Furthermore, the impact of model order on the estimation performance is scrutinized to give insights into the parameterization in practical applications. Long-term cycling tests on different types of LIB cells are used for evaluation. The proposed method is validated with a good robustness to the cell inconsistency, temperature uncertainty, noise corruption, and a satisfied generality to different battery chemistries.

The state of charge (SOC) estimation is an enabling technique for the efficient management and control of lithium-ion batteries (LIBs). This paper proposes a novel method for online SOC estimation which manifest itself with both high accuracy and low complexity. Particularly, the particle swarm optimization (PSO) algorithm is exploited to optimize the model parameters to ensure a high modeling accuracy. Following this endeavor, the PSO algorithm is used to tune the error covariances of extended Kalman filter (EKF) leveraging the early-stage segmental data of LIB utilization. Within this PSO-based tuning framework, the searching boundary is derived by scrutinizing the error transition property of system. Experiments are performed to validate the proposed two-step PSO-optimized SOC estimation method. Results show that even by using a simple first-order model, the proposed method can give rise to a high SOC accuracy which is comparative to those using complex high-order models. The proposed method is validated to excavate fully the potential of model-based estimators so that the computationally expensive model upgrade can be avoided.

This study addresses the problem of distributed robust control resistance to vehicle parametric uncertainty and hybrid attacks in heterogeneous vehicular platoon systems. First, an invaded heterogeneous vehicular platoon system is modeled accompanied by the technique of inverse model compensation to deal with the problem of non-linearities in longitudinal dynamics, a nominal vehicle mass is introduced to tackle the problem of heterogeneity and variability of vehicle masses. Besides, we quantize the effects of false-data injection (FDI) and message-delay (MD) attacks and integrate them into the proposed heterogeneous platoon system. Additionally, to obtain the desired inner-vehicle stability of a heterogeneous platoon, an inner-vehicle ${H}_\infty $ distributed robust stable controller is presented based on Lyapunov-Krasovskii functional. Finally, a ${\mathcal{L}}_2$ -based string stability criterion is put forward to weaken the attacks' effects when they propagate along with the platoon. To explain the superiority of the derived theory more directly, simulations with two different control methods about the heterogeneous platoon under hybrid attacks are provided. The results show that, compared to regular platoon control method, the proposed robust controller performs better in achieving the desired inter-vehicle spacing tracking and can maintain string stability of the platoon.