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In hybrid vehicles, the drive motor is directly connected to the drive train and the inherent drive train damping is low. When subjected to external disturbance, the low damping characteristics of the transmission system may cause torsional vibration, which will continue to oscillate the transmission system and affect the driving performance of the vehicle. In this paper, we propose a harmonic injection wheel control method based on motor speed to suppress oscillations and improve the driving performance of hybrid electric vehicles. The harmonic injection control method based on motor speed is based on Fourier transform to decompose sinusoidal harmonics based on specific order of motor speed. RLS algorithm is used to estimate the amplitude and phase, and PI control is used to calculate the compensation torque for the actual amplitude and target amplitude. Simulation and test results show that the proposed control strategy is effective in suppressing oscillations.

The on-vehicle automation system is primarily designed to replace the human driver during driving to enhance the performance and avoid possible fatalities. However, current implementations in automated vehicles (AVs) generally neglect that human imperfection and preference do not always lead to negative consequences, which prevents achieving optimized vehicle performance and maximized road safety. Human-like Decision-making and Control for Automated Driving takes a step forward to address breaking through the limitation of future automation applications, investigating in depth: Human driving feature modeling and analysis Personalized motion control for AVs Human-like decision making for AVs Click here to access the full SAE EDGETM Research Report portfolio.

Forecasting the motion of the leading vehicle is a critical task for connected autonomous vehicles as it provides an efficient way to model the leading-following vehicle behavior and analyze the interactions. In this study, a personalized time-series modeling approach for leading vehicle trajectory prediction considering different driving styles is proposed. The method enables a precise, personalized trajectory prediction for leading vehicles with limited inter-vehicle communication signals, such as vehicle speed, acceleration, space headway, and time headway of the front vehicles. Based on the learning nature of human beings that a human always tries to solve problems based on grouping and similar experience, three different driving styles are first recognized based on an unsupervised clustering with a Gaussian Mixture Model (GMM).

Studying drifting dynamics and control could extend the usable state-space beyond handling limits and maximize the potential safety benefits of autonomous vehicles. Distributed electric vehicles provide more possibilities for drifting control with better grip and larger maximum drift angle. Under the state of drifting, the distributed electric vehicle is a typical nonlinear over-actuated system with actuator redundancy, and the coupling of input vectors impedes the direct use of control algorithm of upper. This paper proposes a novel automated drifting controller for the distributed electric vehicle. First, the nonlinear over-actuated system, comprised of driving system, braking system and steering system, is formulated and transformed to a square system through proposed integrative recombination method of control channel, making general nonlinear control algorithms suitable for this system.

The interactions between automatic controls, physics, and driver is an important step towards highly automated driving. This study investigates the dynamical interactions between human-selected driving modes, vehicle controller and physical plant parameters, to determine how to optimally adapt powertrain control to different human-like driving requirements. A cyber-physical system (CPS) based framework is proposed for co-design optimization of the physical plant parameters and controller variables for an electric powertrain, in view of vehicle’s dynamic performance, ride comfort, and energy efficiency under different driving modes. System structure, performance requirements and constraints, optimization goals and methodology are investigated. Intelligent powertrain control algorithms are synthesized for three driving modes, namely sport, eco, and normal modes, with appropriate protocol selections. The performance exploration methodology is presented.

As the essential of future driver assistance system, brake-by-wire system is capable of performing autonomous intervention to enhance vehicle safety significantly. Regenerative braking is the most effective technology of improving energy consumption of electrified vehicle. A novel brake-by-wire system scheme with integrated functions of active braking and regenerative braking, is proposed in this paper. Four pressure-difference-limit valves are added to conventional four-channel brake structure to fulfill more precise pressure modulation. Four independent isolating valves are adopted to cut off connections between brake pedal and wheel cylinders. Two stroke simulators are equipped to imitate conventional brake pedal feel. The operation principles of newly developed system are analyzed minutely according to different working modes. High fidelity models of subsystems are built in commercial software MATLAB and AMESim respectively.

Recently, the development of braking assistance system has largely benefit the safety of both driver and pedestrians. A robust prediction and detection of driver braking intention will enable driving assistance system response to traffic situation correctly and improve the driving experience of intelligent vehicles. In this paper, two types unsupervised clustering methods are used to build a driver braking intention predictor. Unsupervised machine learning algorithms has been widely used in clustering and pattern mining in previous researches. The proposed unsupervised learning algorithms can accurately recognize the braking maneuver based on vehicle data captured with CAN bus. The braking maneuver along with other driving maneuvers such as normal driving will be clustered and the results from different algorithms which are K-means and Gaussian mixture model (GMM) will be compared.

This paper presents a coordinated controller for comprehensive optimization of vehicle dynamics performance and energy consumption for a full drive-by-wire electric vehicle, which is driven by a four in-wheel motor actuated (FIWMA) system and steered by a steer-by-wire (SBW) system. In order to coordinate the FIWMA and SBW systems, the mechanisms influencing the vehicle dynamics control performance and the energy consumption of the two systems are first derived. Second, the controllers for each subsystem are developed. For the SBW system, a triple-step control technique is implemented to decouple the yaw rate and sideslip angle controls. The FIWMA system controller is designed with a hierarchical control scheme, which is able not only to satisfy the yaw rate and sideslip angle tracking demands, but also to deal with actuation redundancy and constraints.

Regenerative braking provided by an electric powertrain is far different from conventional friction braking with respect to the system dynamics. During regenerative decelerations, the nonlinear powertrain backlash would excite driveline oscillations, deteriorating vehicle drivability and blended brake performance. Therefore, backlash compensation is worthwhile researching for an advanced powertrain control of electrified vehicles during regenerative deceleration. In this study, a nonlinear powertrain of an electric passenger car equipped with a central motor is modeled using hybrid system approach. The effect of powertrain backlash gap on vehicle drivability during regenerative deceleration is analyzed. To further improve an electric vehicle's drivability and blended braking performance, an active control algorithm with a hierarchical architecture is studied for powertrain backlash compensation.

As the main power source of the electric vehicle, the electric motor has outstanding characteristics including rapid response, accurate control and four-quadrant operation. Being introduced into the dynamic chassis control of electrified vehicles, the electric motor torque can be used not only for driving and regenerative braking during normal operating conditions, but also offers a great potential to improve the dynamic control performance of the anti-lock braking under emergency deceleration situations. This paper presents a robust control algorithm for anti-lock braking of a front-wheel-drive electric vehicle equipped with an axle motor. The hydraulic and regenerative braking system of the electric vehicle is modeled as a LPV (linear parameter varying) system. The nonlinearities of the control system are considered as uncertain parameters of a linear fractional transformation.

Regenerative braking, which can effectively improve vehicle's fuel economy by recuperating the kinetic energy during deceleration processes, has been applied in various types of electrified vehicle as one of its key technologies. To achieve high regeneration efficiency and also guarantee vehicle's brake safety, the regenerative brake should be coordinated with the mechanical brake. Therefore, the regenerative braking control performance can be significantly affected by the structure of mechanical braking system and the brake blending control strategy. By-wire brake system, which mechanically decouples the brake pedal from the hydraulic brake circuits, can make the braking force modulation more flexible. Moreover, its inherent characteristic of ‘pedal-decouple’ makes it well suited for the implementation in the cooperative regenerative braking control of electrified vehicles.