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Lightweight electric vehicles (LEVs) possess great advantages in the viewpoint of fuel consumption, environment protection and traffic mobility. However, due to the drastic reduction of vehicle weights and body size, the effects of payload parameter variation in LEV control and estimation system become much more pronounced and have to be systematically analysed. This paper proposes a novel payload parameter sensitivity analysis to provide quantitative insight into the sensitivity of payload parameter on the LEV system responses and state estimation. The analysis-oriented LEV dynamic model considering payload parameter variations is developed. Then, the trajectory sensitivity index of the influential parameters is defined and derived with the perturbation approach, the median method is used to improve the calculation accuracy for the trajectory sensitivity of payload parameter. Finally, the extended Kalman filter is designed to show the effect and importance of the sensitive payload parameters on the observation accuracy, the payload parameter variations along with fundamental state estimation such as vehicle sideslip angle, longitudinal velocity and vehicle roll angle are analysed. Simulation results with Matlab/Simulink-Carsim® show that the proposed method can accurately describe the relationship between the vehicle payload parameters and system state estimation, which is helpful to design and evaluate LEV controller and observer performances. [ABSTRACT FROM AUTHOR]

This paper presents a stochastic stable control protocol for heterogeneous vehicle platoon subject to communication topologies change, external disturbance, and information delay. First, a random vehicle platoon system composed entirely of several pure electric vehicles is built. The random variation of data transmission link among the platoon in a natural traffic environment is considered and molded by the Markov chain combined with the directed graph method. The influence of delays and discrete data in wireless communication, road slope, and air resistance on the vehicle platoon is also considered by introducing the external interferences and equivalent information delays. Additionally, to ensure the vehicle platoon’s inner-vehicle stability, the variable-gain distributed controller is proposed based on the Markovian jumping system stability theory and $ H_\infty$ control. Finally, the $ \mathcal {L}_{2}$ stochastic string stability is defined to attenuate perturbations as they propagate through the platoon. Simulation studies about a vehicle platoon under four communication topologies random switching with two different control methods are provided to verify the theoretical result. It is shown that, compared to traditional platoon robust control, it is possible to achieve the vehicle platoon’s stability even if in the continuous mutation of unstable topologies by using the proposed control approach. [ABSTRACT FROM AUTHOR]

In this paper, we propose a two-layer overtaking control framework of the autonomous electric ground vehicle, including trajectory planning and tracking control. In the upper layer, a stable overtaking trajectory is derived by considering the duration of the overtaking, driving comfort and vehicle stability simultaneously. Besides, the safety spacing between the overtaking and overtaken vehicles is analyzed to prevent the potential collision. To track the generated reference trajectory precisely, we design a robust model predictive controller adopting the input-to-state stability property in the lower layer to reduce the affection of disturbances and parameter uncertainties. Two driving strategies with different lateral accelerations, that is, mild and aggressive, are simulated to verify the effectiveness of the proposed control framework. By comparing to the driver-vehicle control system, the proposed control framework can not only achieve safe, smooth, and rapid overtaking but also realize the accurate state tracking in the presence of tire cornering stiffness uncertainty. [ABSTRACT FROM AUTHOR]

• A new time-domain-based energy-oriented spacing policy is proposed. • Discrete-time vehicle platoon system integrating communication delay and disturbance is provided. • Less conservative discrete robust platoon control methodology is developed. • L 2 -based string stability and its criterion of the discrete platoon are given. This paper presents a discrete control framework for a homogeneous vehicle platoon, which can minimize the energy consumption of each vehicle while ensuring the platoon string stability under external interference and communication delay. First, a novel variable spacing strategy named as the energy-oriented spacing policy is proposed by comparing the geometry relationship of each vehicle which drives at energy-optimal speed and the ideal speed under the constant spacing policy. Then, based on the energy-oriented spacing policy, the dynamics of a homogeneous discrete vehicle platoon composed of several pure electric vehicles are built, including the communication delay and external disturbance. Second, a discrete robust platoon controller that can reduce the conservatism of the control system is designed based on the Lyapunov-Krasovskii theorem and H ∞ method, which drives each vehicle at their energy-optimal speed while ensuring the string stability of platoon under communication delays and interferences. Third, the L 2 -based string stability of the discrete platoon is defined, and its string-stable criterion is given under communication delays and external disturbances. Simulation results indicate that the proposed energy-oriented spacing policy and the discrete controller can enable efficient driving of each vehicle while ensuring the platoon string stability. The energy consumption of the whole platoon is, respectively, reduced by 7.3% and 5.7% compared to the vehicle platoon controllers with constant spacing policy and constant time headway policy. [ABSTRACT FROM AUTHOR]

To improve the safety of intelligent electric vehicles and avoid side slipping on curved roads with changing friction coefficients, an integrated speed planning and friction coefficient estimation algorithm is proposed. With this algorithm, the speeds of intelligent electric vehicles can be planned online using estimated road friction coefficients to avoid lane departures. When a decrease in the friction coefficient is detected on a curved road with a large curvature, the algorithm will plan a low and safe speed to avoid side slipping. When a normal friction coefficient is detected, the algorithm will plan a higher speed for normal driving. Simulations using MATLAB and CarSim have been performed to demonstrate the effectiveness of the designed algorithm. The simulation results suggest that the proposed algorithm is applicable to speed planning on curved roads with changing friction coefficients. [ABSTRACT FROM AUTHOR]

• An eco-driving control strategy is proposed for vehicles approaching a signalized intersection. • The discharge of the vehicle queue is modeled and predicted. • The proposed strategy is based on a computationally efficient iterative dynamic programming algorithm. • Monte-Carlo simulation using real-world traffic data is performed for in-depth energy efficiency analysis. • Energy-saving potential enabled by the proposed strategy is investigated for both internal combustion engine and electric vehicles. This paper takes into consideration of vehicle queues at the intersection and proposes an energy-efficient driving strategy to improve vehicle energy efficiency and overall traffic throughput in an urban traffic environment. The proposed strategy is applicable for both electric vehicle and internal combustion engine vehicle, and the control framework is formed by three sections, a vehicle queue discharge predictor, a spatial-domain optimal control strategy for energy consumption minimization, and a speed tracker with consideration of collision avoidance constraints. The former is based on the intelligent driver model, which predicts an accurate vehicle queue discharge time. Then the iterative dynamic programming is utilized to find the optimal solutions with fast computational speed. Finally, the optimal speed profile is followed by a Proportion-Integration controller while keeping a safe inter-vehicular distance. A Monte-Carlo simulation is designed to evaluate the energy efficiency of the proposed strategy in the stochastic traffic environment. Compared to the regular eco-approach and departure and constant speed strategies that lack awareness of the queue, significant energy saving can be achieved of the proposed strategy. In addition, three typical cases are selected to study the energy efficiency when the proposed strategy is applied to internal combustion engine and electric vehicles, respectively. The results show the energy efficiency of electric vehicles is less sensitive to the queuing effect at the intersection because of regenerative braking and the overall higher efficiency of the electric motor in contrast to the internal combustion engine, especially in stop-and-go scenarios. [ABSTRACT FROM AUTHOR]