Search Results

The courier express parcel service industry (CEP industry) has experienced significant changes in the recent years due to increasing parcel volume. At the same time, the electrification of the vehicle fleets poses additional challenges. A major advantage of battery electric CEP vehicles compared to internal combustion engine vehicles is the ability to regenerate the kinetic energy of the vehicle in the frequent deceleration phases during parcel delivery. If the battery is cold the maximum recuperation power of the powertrain is limited by a reduced chemical reaction rate inside the battery. In general, the maximum charging power of the battery depends on the state of charge and the battery temperature. Due to the low power demand for driving during CEP operation, the battery self-heating is comparably low under cold ambient conditions. Without active conditioning of the battery, potential regenerative energy is lost as a result of the cold battery.

With the modernization of agriculture, the application of unmanned agricultural special vehicles is becoming increasingly widespread, which helps to improve agricultural production efficiency and reduce labor. Vehicle path-tracking control is an important link in achieving intelligent driving of vehicles. This paper designs a controller that combines path tracking with vehicle lateral stability for four-wheel steer/drive agricultural special electric vehicles. First, based on a simplified three-degrees-of-freedom vehicle dynamics model, a model predictive control (MPC) controller is used to calculate the front and rear axle angles. Then, according to the Ackermann steering principle, the four-wheel independent angles are calculated using the front and rear axle angles to achieve tracking of the target trajectory.

Vehicle yaw stability control (YSC) can actively adjust the working state of the chassis actuator to generate a certain additional yaw moment for the vehicle, which effectively helps the vehicle maintain good driving quality under strong transient conditions such as high-speed turning and continuous lane change. However, the traditional YSC pursues too much driving stability after activation, ignoring the difference of multi-objective requirements of yaw maneuverability, actuator energy consumption and other requirements in different vehicle stability states, resulting in the decline of vehicle driving quality. Therefore, a vehicle yaw stability model predictive control strategy for dynamic and multi-objective requirements is proposed in this paper. Firstly, the unstable characteristics of vehicle motion are analyzed, and the nonlinear two-degree-of-freedom vehicle dynamics models are established respectively.

Abrasion of the Electromechanical brake (EMB) brake pad during the braking process leads to an increase in brake gap, which adversely affects braking performance. Therefore, it is imperative to promptly detect brake pad abrasion and adjust the brake gap accordingly. However, the addition of extra gap adjustment or sensor detection devices will bring extra size and cost to the brake system. In this study, we propose an innovative EMB gap active adjustment strategy by employing modeling and analysis of the braking process. This strategy involves identifying the contact and separation points of the braking process based on the differential current signal. Theoretical analysis and simulation results demonstrate that this gap adjustment strategy can effectively regulate the brake gap, mitigate the adverse effects of brake disk abrasion, and notably reduce the response time of the braking force output. Monitoring is critical to accurately control EMB clamping force.

Intelligent vehicle-to-everything connectivity is an important development trend in the automotive industry. Among various active safety systems, Autonomous Emergency Braking (AEB) has garnered widespread attention due to its outstanding performance in reducing traffic accidents. AEB effectively avoids or mitigates vehicle collisions through automatic braking, making it a crucial technology in autonomous driving. However, the majority of current AEB safety models exhibit limitations in braking modes and fail to fully consider the overall vehicle stability during braking. To address these issues, this paper proposes an improved AEB control system based on a risk factor (AERF). The upper-level controller introduces the risk factor (RF) and proposes a multi-stage warning/braking control strategy based on preceding vehicle dynamic characteristics, while also calculating the desired acceleration.

The Electronic Mechanical Braking (EMB) system, which offers advantages such as no liquid medium and complete decoupling, can meet the high-quality active braking and high-intensity regenerative braking demands proposed by intelligent vehicles and is considered one of the ideal platforms for future chassis. However, traditional control strategies with fixed clamping force tracking parameters struggle to maintain high-quality braking performance of EMB under variable braking requests, and the nonlinear friction between mechanical components also affects the accuracy of clamping force control. Therefore, this paper presents an adaptive clamping force control strategy for the EMB system, taking into account the resistance of nonlinear friction. First, an EMB model is established as the simulation and control object, which includes the motor model, transmission model, torque balance model, stiffness model, and friction model.

This paper presents a torque distribution strategy for four-wheel independent drive electric vehicles (4WIDEVs) to achieve both handling stability and energy efficiency. The strategy is based on the dynamic adjustment of two optimization objectives. Firstly, a 2DOF vehicle model is employed to define the stability control objective for Direct Yaw moment Control (DYC). The upper-layer controller, designed using Linear Quadratic Regulator (LQR), is responsible for tracking the target yaw rate and target sideslip angle. Secondly, the lower-layer torque distribution strategy is established by optimizing the tire load rate and motor energy consumption for dynamic adjustment. To regulate the weights of the optimization targets, stability and energy efficiency allocation coefficient is introduced. Simulation results of double lane change and split μ road conditions are used to demonstrate the effectiveness of the proposed DYC controller.

As the automotive industry accelerates its virtual engineering capabilities, there is a growing requirement for increased accuracy across a broad range of vehicle simulations. Regarding control system development, utilizing vehicle simulations to conduct ‘pre-tuning’ activities can significantly reduce time and costs. However, achieving an accurate prediction of, e.g., stopping distance, requires accurate tire modeling. The Magic Formula tire model is often used to effectively model the tire response within vehicle dynamics simulations. However, such models often: i) represent the tire driving on sandpaper; and ii) do not accurately capture the transient response over a wide slip range. In this paper, a novel methodology is developed using the MF-Tyre/MF-Swift tire model to enhance the accuracy of ABS braking simulations.

The installation of the Electronic Braking System (EBS) could effectively improve braking response speed, shorten braking distance, and ensure driving safety of commercial vehicles. However, during longitudinal deceleration control process, the commercial vehicles face not only challenges such as large inertia mass and random road gradient resistance of the vehicle layer, but also non-linear characteristics of the EBS actuator layer. In order to solve these problems, this paper proposes a commercial vehicle’s longitudinal deceleration precise control strategy considering vehicle-actuator dynamic characteristics. First, longitudinal dynamics of commercial vehicle is analyzed, and so is the EBS’ non-linear response hysteresis characteristics. Then, we design the dual layer deceleration control strategy. In vehicle layer, the recursive least squares with forgetting factor and Kalman filtering are comprehensively applied to dynamically estimate the vehicle mass and driving road slope.

Lithium-ion cells operate under a narrow range of voltage, current, and temperature limits, which requires a battery management system (BMS) to sense, control, and balance the battery pack. The state of power (SOP) estimation is a fundamental algorithm of the BMS. It operates as a dynamic safety limit, preventing rapid ageing and optimizing power delivery. SOP estimation relies on predictive algorithms to determine charge and discharge power limits sustainable within a specified time frame, ensuring the cell design constraints are not violated. This paper explores various approaches for real-time deployment of SOP estimation algorithms for a high-power lithium-ion battery (LIB) with a low-cost microcontroller. The algorithms are based on a root-finding approach and a first-order equivalent circuit model (ECM) of the battery.

In recent years, an increase in vehicle weight due to the electrification of automobiles, specifically EVs, has increased the input loads on anti-vibration rubber parts. Moreover, the characteristics of these loads have also changed due to the rotational drive of electric motors, regenerative braking, and other factors. When designing a vehicle, in advance it is necessary to set specifications that take into account the spring characteristics and durability of the anti-vibration rubber parts in order to meet functional requirements. In this study, the hyperelastic and fatigue characteristics (S-N diagram and Haigh diagram) of Rubbers which is widely used for anti-vibration rubber parts, were experimentally obtained, and structural and fatigue analyses using FEM (Finite Element Method) were conducted in conjunction with spring and fatigue tests of anti-vibration rubber parts to determine the correlation between their spring and fatigue characteristics.

This paper presents an optimal control co-design framework of a parallel electric-hydraulic hybrid powertrain specifically tailored for heavy-duty vehicles. A pure electric powertrain, comprising a rechargeable lithium-ion battery, a highly efficient electric motor, and a single or double-speed gearbox, has garnered significant attention in the automotive sector due to the increasing demand for clean and efficient mobility. However, the state-of-the-art has demonstrated limited capabilities and has struggled to meet the design requirements of heavy-duty vehicles with high power demands, such as a class 8 semi-trailer truck. This is especially evident in terms of a driving range on one battery charge, battery charging time, and load-carrying capacity. These challenges primarily stem from the low power density of lithium-ion batteries and the low energy conversion efficiency of electric motors at low speeds.

Reference velocity (i.e. the absolute velocity of vehicle center of gravity) is a key parameter for vehicle stability control functions as well as for the powertrain control functions of hybrid electric vehicle (HEV). Most reference velocity estimation methods employ the vehicle kinematic and tire dynamic equations to construct high order linear or nonlinear model with a set of parameters and sensor measurements. When using those models, delicate algorithm should be designed to prevent the estimates from deviating along with the increase of nonlinearity, modeling error and noise that introduced by high order, parameter approximation, and sensor measurements, respectively. Alternatively, to improve the function robustness and calibration convenience, a straightforward online estimation method is developed in the paper by using a second-order powertrain dynamic model that only need a small set of vehicle parameters and sensor values.

To improve the braking energy recovery rate of pure electric garbage removal vehicles and ensure the braking effect of garbage removal vehicles, a strategy using particle swarm algorithm to optimize the regenerative braking fuzzy control of garbage removal vehicles is proposed. A multi-section front and rear wheel braking force distribution curve is designed considering the braking effect and braking energy recovery. A hierarchical regenerative braking fuzzy control strategy is established based on the braking force and braking intensity required by the vehicle. The first layer is based on the braking force required by the vehicle, based on the front and rear axle braking force distribution plan, and uses fuzzy controllers.

This paper presents the analysis of an innovative braking system as an alternative and environmentally friendly solution to traditional automotive friction brakes. The idea arose from the need to eliminate emissions from the braking system of an electric vehicle: traditional brakes, in fact, produce dust emissions due to the wear of the pads. The innovative solution, called Zero-Emissions Driving System (ZEDS), is a system composed of an electric motor (in-wheel motor) and an innovative brake. The latter has a geometry such that it houses MagnetoRheological Fluid (MRF) inside it, which can change its viscous properties according to the magnetic field passing through it. It is thus an electro-actuated brake, capable of generating a magnetic field passing through the fluid and developing braking torque. A performance analysis obtained by a simulation model built on Matlab Simulink is proposed.

The pursuit of maintaining a zero-sideslip angle has long driven the development of four-wheel-steering (4WS) technology, enhancing vehicle directional performance, as supported by extensive studies. However, strict adherence to this principle often leads to excessive understeer characteristics before tire saturation limits are reached, resulting in counter-intuitive and uncomfortable steering maneuvers during turns with variable speeds. This research delves into the phenomenon encountered when a 4WS-equipped vehicle enters a curved path while simultaneously decelerating, necessitating a reduction in steering input to adapt to the increasing road curvature. To address this challenge, this paper presents a novel method for dynamically regulating the steady-state yaw rate of 4WS vehicles. This regulation aims to decrease the vehicle's sideslip angle and provide controlled understeer within predetermined limits.

The tire cornering stiffness plays a vital role in the functionality of vehicle dynamics control systems, particularly when it comes to stability and path tracking controllers. This parameter relies on various external variables such as the tire/ambient temperature, tire wear condition, the road surface state, etc. Ensuring a reliable estimation of the cornering stiffness value is crucial for control systems. This ensures that these systems can accurately compute actuator requests in a wide range of driving conditions. In this paper, a novel estimation method is introduced that relies solely on standard vehicle sensor data, including data such as steering wheel angles, longitudinal acceleration, lateral acceleration, yaw rate, and vehicle speed, among others. Initially, the vehicle's handling characteristics are deduced by estimating the understeer gradient.

Multiple actuators equipped in electric vehicles, such as four- wheel steering (4WS) and four-wheel drive (4WD), provide more degrees of freedom for chassis motion control. However, developing independent control strategies for distinct actuator types could result in control conflicts, potentially degrading the vehicle's motion performance. To address this issue, a model predictive control (MPC) based steering-drive cooperated control strategy for enhanced agility and stability of electric vehicles with 4WD and 4WS is proposed in this paper. By designing the control constraints within the MPC framework, the strategy enables single-drive control, single-steering control, and steering-drive cooperative control. In the upper control layer, a linear time-varying MPC (LTV-MPC) is designed to generate optimal additional yaw moment and additional steering angles of front and rear wheels to enhance vehicle agility and lateral stability.

This paper proposes a thorough investigation of steady-state cornering equilibria for cars. Besides equilibria corresponding to normal driving behaviour - herein denoted as stable-normal turn, drifting is attracting increasing attention. When discussing drifting, it is typically assumed that yaw rate and steering angle have opposite signs, i.e. the driver is countersteering, and the rear axle is saturated. Interestingly, another unstable equilibrium is possible, herein referred to as unstable-normal turn. In this work, an attempt to give a comprehensive definition of drift is made. An inverse model is proposed to compute the driver inputs needed to perform a steady-state turn for a given radius and sideslip angle. The mathematical meaning of all equilibria is explored by linearizing the system and analyzing eigenvalues and eigenvectors of the resulting state matrices.

Complex chassis systems operate in various environments such as low-mu surfaces and highly dynamic maneuvers. The existing metrics for lateral motion hazard by Neukum [13] and Amberkar [17] have been developed and correlated to driver behavior against disturbances on straight line driving on a dry surface, but do not cover low-mu surfaces and dynamic driving scenarios which include both linear and nonlinear region of vehicle operation. As a result, an improved methodology for evaluating vehicle yaw dynamics is needed for safety analysis. Vehicle yaw dynamics safety analysis is a methodical evaluation of the overall vehicle controllability with respect to its yaw motion and change of handling characteristic.