Yugong Luo

Intelligent Environment-Friendly Vehicles: Concept and Case Studies

The concept of an intelligent environment-friendly vehicle (i-EFV) is proposed in this paper. It integrates three components, i.e., clean-energy powertrain, electrified chassis, and intelligent information interaction devices. By employing such technologies as structure sharing, data fusion, and control coordination, more comprehensive performances are achievable, in terms of traffic safety, fuel efficiency, and environmental protection. Based on its definition and configuration, some key technologies, including design for resource effectiveness, driving environment identification, and coordinated control, are studied. As a basic application, a platform of an intelligent hybrid electric vehicle (i-HEV), which incorporates a hybrid powertrain with adaptive cruise control, has been designed and implemented. Both simulation and experimental results demonstrated that the i-EFV performed better than a conventional vehicle.

Intelligent Hybrid Electric Vehicle ACC With Coordinated Control of Tracking Ability, Fuel Economy, and Ride Comfort

Adaptive cruise control (ACC) of hybrid electric vehicles (HEVs) has been traditionally developed without an efficient integration with active safety and energy management systems of hybrid power-trains, mainly for facilitating its implementation. This, however, leads to a compromise in the fuel economy of HEVs, since the predictive driving information provided by ACC is not exploited by the energy management system. In order to enhance the energy efficiency and control system integration, a novel ACC system for intelligent HEVs (i-HEV ACC) is developed in this study. The controller is proposed within the framework of nonlinear model predictive control, and a position-based nonlinear longitudinal intervehicle dynamics model is developed. A coordinated optimal control problem for both the tracking safety and the fuel consumption is formulated subject to the constraints on stable tracking. A multistep offline dynamic programming optimization and an online lookup table are used to implement the real-time control algorithm. Experiments are further conducted, which demonstrate that the proposed i-HEV ACC achieves enhanced performance and cooperation in traffic safety, fuel efficiency, and ride comfort.

Optimum tyre force distribution for four-wheel-independent drive electric vehicle with active front steering

Tyre workload is widely used as performance index in tyre force distribution. The existing methods are limited by either the uneven distribution result or too many constraints. To maximise vehicle stability margin while not involving too many constraints, an optimum tyre force distribution method based on minimising the variance and mean value of tyre workload for four-wheel-independent drive electric vehicle with active front steering (AFS) is proposed. The operations of driver are explained as the desired total longitudinal and lateral force and yaw moment constraints. The four longitudinal tyre forces and the lateral force of the front axle are obtained by using quasi-Newton method for solution. The simulation results show that the proposed method can increase the maximum safe speed by several percents when compared to the existing methods. Vehicle test verifies that the adhesion margin of the vehicle is kept to a high level, which will enhance the stability performance.

Real-time identification of the tyre–road friction coefficient using an unscented Kalman filter and mean-square-error-weighted fusion

Vehicle active safety systems can benefit significantly from a priori knowledge of the road conditions. This paper proposes a real-time tyre–road friction estimation algorithm based on an unscented Kalman filter and mean-square-error-weighted fusion, using measurements related to the electric vehicle and in-wheel motors. Because the modules are mutually independent, the dimensionality of the matrix used in the filtering process can be reduced to shorten the computational period. The approach can also work more effectively in various conditions than those techniques which focus on a specific direction of the vehicle dynamics. First, a modified Dugoff et al. tyre model is considered to express the non-linear characteristics of the tyres. Then, to observe the longitudinal tyre forces and the lateral tyre forces, a parameter identification technique that utilizes the information from an inertial sensor and a gyroscope is introduced. An unscented Kalman filter rather than the traditional linear filter is adopted to obtain higher accuracy in the saturation region. After the relationship between the mean square error and the side-slip angle or the slip ratio is taken into account, the final identified value is set by combining the results from the longitudinal unscented Kalman filter and the lateral unscented Kalman filter in order to improve the accuracy and the robustness further. Experiments conducted on both a dry road and a slippery road verify that the method based on an unscented Kalman filter can identify different tyre–road friction levels.

A Dynamic Model for Tire/Road Friction Estimation under Combined Longitudinal/Lateral Slip Situation

A new dynamic tire model for estimating the longitudinal/lateral road-tire friction force was derived in this paper. The model was based on the previous Dugoff tire model, in consideration of its drawback that it does not reflect the actual change trend that the tire friction force decreases with the increment of wheel slip ratio when it enters into the nonlinear region. The Dugoff model was modified by fitting a series of tire force data and compared with the commonly used Magic Formula model. This new dynamic friction model is able to capture accurately the transient behavior of the friction force observed during pure longitudinal wheel slip, lateral sideslip and combined slip situation. Simulation has been done under different situations, while the results validate the accuracy of the new tire friction model in predicting tire/road friction force during transient vehicle motion.

Maximum Tire Road Friction Estimation Based on Modified Dugoff Tire Model

Estimation of the tire-road friction using the signal of on-board sensors is very important for the vehicle dynamic control systems. This paper presented a tire -- Croad friction coefficient estimation algorithm based on a modified Dugoff model. The proposed algorithm first determined the tire slip ratio and the instantaneous longitudinal friction coefficient using vehicle and wheel dynamics parameters. Then, the friction coefficient was estimated through the modified Dugoff model with subsection method and converse solution method. The effectiveness and performance of the algorithm were demonstrated through vehicle dynamics simulations on different road surface conditions.

Multi-objective adaptive cruise control based on nonlinear model predictive algorithm

A multi-objective Adaptive Cruise Controller of Hybrid Electric Vehicle (so called i-HEV-ACC) is proposed in this paper. It integrates both advantages of Intelligent Transportation Systems (ITS) and HEV, and it reaches comprehensive performances on traffic safety, fuel efficiency and ride comfort. According to the analysis of i-HEV-ACC, a hierarchical control structure with steady-state optimization and dynamic coordination is presented. Furthermore, on the basis of global longitudinal dynamics model, the control strategy of i-HEV-ACC which incorporates comprehensive performances has been developed by employing nonlinear model predictive control algorithm. Finally, the i-HEV-ACC forward simulation platform is established. Through system simulation and analysis, the results demonstrate that i-HEV-ACC can realize coordinated performances of traffic safety, fuel efficiency and ride comfort.

Dynamic coordinated control of a downhill safety assistance system for hybrid electric buses

When driving downhill, downhill safety assistance control can ensure a safe speed. A downhill safety assistance system was developed by our research group to help hybrid electric vehicles to maintain a stable speed when driving downhill. For hybrid electric buses, in addition to the pneumatic braking system, the motor can quickly provide an electrical braking torque, and the engine can be considered a mechanical brake. The downhill safety assistance system for hybrid electric buses maintains the desired downhill speed on different road slopes. However, when and how to activate or deactivate the downhill safety assistance system because of the driver’s operation or the road conditions was not discussed in combination with the energy management strategy for the vehicle. Additionally, there is currently no dynamic control strategy for the transition process when the braking modes of the downhill safety assistance system change, which can lead to instability. To address the limitations of previous studies, a dynamic coordinated control strategy of the downhill safety assistance system is proposed considering practical application, which focuses on the above two aspects to keep the entire system stable. To improve the ride comfort and the vehicle safety when the downhill safety assistance system works in conjunction with the energy management strategy for the vehicle, the proposed control strategy is developed to activate or deactivate the downhill safety assistance system based on the driver’s driving habits and operation and the road conditions in order to reduce the workload and to improve the driveability of the buses. To maintain the ride comfort during the transient process of shift in the braking mode and to maintain a stable speed over the overall course, the mode-shift coordinated control strategy of the downhill safety assistance system is presented, which combines the braking modes to ensure that the braking torque changes steadily without saltation. The experimental results validate the performance of the entire dynamic coordinated control strategy of the downhill safety assistance system with a high stability, and the statistics demonstrate that the downhill safety assistance system obviously improves the fuel economy and reduces the driver workload.

Estimation of Road-Tire Friction with Unscented Kalman Filter and MSE-Weighted Fusion based on a Modified Dugoff Tire Model

Abstract This paper proposes an estimation method of road-tire friction coefficient for the 4WID EV(4-wheel-independent-drive electric vehicle) in the pure longitudinal wheel slip, lateral sideslip and combined slip situations, which fuses both estimated longitudinal and lateral friction coefficients together, compared with existing methods based on a tire model in one single direction. Unscented Kalman filter (UKF) is introduced to estimate one-directional friction coefficient based on a modified Dugoff tire model. Considering the output results for each direction as a signal for the same target with different noise, MSE-weighted fusion method is proposed to fuse these two results together in order to reach a higher accuracy. The tire forces are estimated with the benefits of the 4WID EV that the driving torque and rolling speed of each wheel can be accurately known. The sideslip angles and slip ratios of each tire are calculated with a vehicle kinematic model. With the observed dynamic and kinematic states of vehicle and tire, the tire-road friction coefficients in different directions are estimated simultaneously based on the modified Dugoff tire model. Numerical results verify that the estimator designed is capable of estimating tire-road friction coefficient with reasonable accuracy, and the algorithm proposed in this work has good robustness and wide applicability under various maneuvers.

Downhill safety assistance control for hybrid electric vehicles based on the downhill driver’s intention model

Assisting the control of a vehicle’s speed while driving downhill improves the vehicle’s safety and reduces the driver’s workload in both internal-combustion engine vehicles and hybrid electric vehicles. The current technology widely used in internal-combustion engine vehicles is a hill descent control system. However, hill descent control can be achieved only at lower speeds, but it may also lead to thermal wear of the brake components during prolonged intensive braking. There is currently no effective downhill safety assistance control technology for hybrid electric vehicles that is effective across the full range of speeds and can take advantage of regenerative braking. To address the limitations of previous studies, a novel downhill safety assistant control strategy for hybrid electric vehicles, which adapts to the characteristics of different drivers and takes advantage of all braking subsystems of hybrid electric vehicles, is proposed in this paper to improve the vehicle safety, the fuel economy and the ride comfort for the full range of speeds. To adapt to the characteristics of different drivers, the downhill driver’s intention model is established on the basis of a statistical data analysis of questionnaires and experiments, which is used to determine the control mode’s switching conditions and the control objective for downhill safety assistant control. To improve the vehicle safety, the fuel economy and the ride comfort for the full speed range, a coordinated control strategy for the electric motor’s braking subsystem, the engine’s braking subsystem and the hydraulic braking subsystem is developed, which includes six braking assistant modes, an identifying strategy and torque control of the electric motor based on coordinated control strategies. Simulations and experimental results show that the proposed control strategy improves the vehicle safety, the fuel economy and the ride comfort of hybrid electric vehicles during downhill driving.