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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.

Plug-in electric vehicle (PHEV) has a promising prospect to reduce greenhouse gas (GHG) emission and optimize engine operating in high-efficiency region. According to the maximum electric power and all-electric range, PHEVs are divided into two categories, including “all-electric PHEV” and “blended PHEV” and the latter provides a potential for more rational energy distribution because engine participates in vehicle driving during aggressive acceleration not just by motor. However, the frequent use of engine may result in severe emissions especially in low state of charge (SOC) and ahead of catalyst light-off. This study quantitatively investigates the impact of oil viscosity and driving mode (hybrid/conventional) on oil dilution and emissions including particle number (PN).

This paper developed a control system for the auxiliary power unit (APU) in off-road series hybrid electric special vehicle. A control system configuration was designed according to the requirements of the high voltage system in series hybrid electric special vehicle. Then optimal engine operating areas were defined. A gain scheduling engine speed PI controller was designed based on these areas. A closed loop voltage regulator was designed for the synchronous generator. The proposed control system was first validated on an APU control test bench. The test results showed the control system guaranteed the diesel APU good dynamic response characteristics while remaining stable output voltage. Finally, the APU control system was implemented on a diesel APU in an off-road series hybrid electric vehicle and a road test was conducted. The road test results showed the APU control system promised good performance in both vehicle dynamics and vehicle high voltage system.

This paper presents a preliminary design and analysis of an optimal energy management and control system for a power-split hybrid electric vehicle (HEV) using hybrid dynamical control system theory and design tools. The hybrid dynamical system theory is applied to formulate HEV powertrain dynamical system in which the interactions of discrete and continuous dynamics are involved. The Sequential Quadratic Programming (SQP) method is applied to optimize power distribution. An improved dynamic programming method is employed to determine the optimal power distribution and the vehicle operating mode transitions.

Power-split configuration is highlighted as the most popular concept for full hybrid electric vehicles (HEV). However, the energy management and design of power-split heavy duty truck under Chinese driving conditions still need to be investigated. In this paper, the parametric design, a rule-based control strategy and an equivalent consumption minimization strategy (ECMS) for the power-split heavy duty truck are presented. Besides, the influence of a penalty factor also discussed under ECMS algorithm. Meanwhile, two different methods to search the engine operation point have been proposed and the reason of different economy performance is presented by using energy flow chart. And the simulation results show both fuel consumption can satisfy the second phase fuel consumption standard and the third phase fuel consumption standard which will be implemented in 2020, under C-WTVC (Chinese-World Transient Vehicle Cycle).

Engine torque fluctuation is a great threat to vehicle comfort and durability. Former researches tried to solve this problem by introducing active damping system, which means the motor is controlled to produce torque ripple with just the opposite phase to that of the engine. By this means, the torque fluctuation produced by the motor and the engine can be reduced. In this paper, a new method is raised. An attempt is proposed by changing the traditional structure of the motor, making it produce ripple torque by itself instead of controlling the motor. In this way a special used ISG (Integrated Starter Generator) motor for HEV (Hybrid Electrical Vehicles) is made to achieve active damping. In order to study the possibility, a simulation, which focus on the motor instead of the whole system, is developed and series-parallel configuration is used in this simulation. As for the motor that used in this paper, four kinds of motors have been investigated and compared.

Hybridization of vehicles is considered as the most promising technology for automakers and researchers, facing the challenge of optimizing both the fuel economy and emission of the road transport. Extensive studies have been performed on power-split hybrid electric vehicles (PS-HEVs). Despite of the fact that their excellent fuel economy performance in city driving conditions has been witnessed, a bottle neck for further improving the fuel economy of PS-HEVs has been encountered due to the inherent engine-generator-motor power circulation of the power-split system under medium-low speed cruising scenarios. Due to the special mechanical constraints of the power-split device (PSD), the conventional periodic cruising strategy like Pulse and Glide cannot be applied to PS-HEVs directly.

The coordinated control of stability and steering systems in collision avoidance steering evasion has been widely studied in vehicle active safety area, but the studies are mainly aimed at autonomous vehicle without driver or conventional combustion engine vehicle. This paper focuses on the control of hybrid vehicle integrated with rear hub in emergency steering evasion situation, and considering the driver’s characteristics. First, the mathematics model of vehicle dynamics and driver has been given. Second, based on the planned steering evasion path, the model predictive control method is presented for achieving higher evasion path tracking accuracy under driver’s steering input. The prediction model includes an adaptive preview distance driver model and a vehicle dynamics model to predict the driver input and the vehicle trajectory.

Hybrid electric vehicles (HEV) are essential for reducing fuel consumption and emissions. However, when analyzing different segments of the transportation industry, for example, public transportation or different sizes of delivery trucks and how the HEV are used, it is clear that one powertrain may not be optimal in all situations. Choosing a hybrid powertrain architecture and proper component sizes for different applications is an important task to find the optimal trade-off between fuel economy, drivability, and vehicle cost. However, exploring and evaluating all possible architectures and component sizes is a time-consuming task. A search algorithm, using Gaussian Processes, is proposed that simultaneously explores multiple architecture options, to identify the Pareto-optimal solutions.

The accuracy of road input identifiaction for autonomous vehicles (AVs) system, especially in state-based AVs control for improving road handling and ride comfort, is a challenging task for the intelligent transport system. Due to the high fatality rate caused by inaccurate state-based control algorithm, how to precisely and effectively acquire road rough information and chose the reasonable road-based control algorithm become a hot topic in both academia and industry. Uncertainty is unavoidable for AVs system, e.g., varying center of gravity (C.G.) of sprung mass, controllable suspension damping force or variable spring stiffness. To tackle the above mentioned, this paper develops a novel observer approach, which combines unscented Kalman filter (UKF) and Minimum Model Error (MME) theory, to optimize the estimation accuracy of the road rough for AVs system. A full-car nonlinear model and road profile model are first established.

To further explore the potential of fuel economy for hybrid electric vehicle (HEV), a methodology of demand power optimization is proposed. The fuel consumption depends not only on the EMS, but also on the way to operate vehicle. A control strategy to adjust driver’s demand before power splitting is necessary. To get accurate and reliable control strategy, two aspects are the most important. First, a rigorous and organized modeling approach is a base to describe complicated powertrain system of HEV. The energetic macroscopic representation (EMR) is a graphical synthetic description of electromechanical conversion system based on energy flow. A powertrain architecture of HEV is described explicitly via the EMR. Second, the effectiveness of EMS and the reasonability of driving operations are vital.

Due to the potential on decreasing fuel consumption and design flexibility, parallel configurations are widely used for hybrid electric vehicles (HEVs). However, the fuel economy and economic profitability of parallel HEVs for heavy-duty truck applications under Chinese driving conditions still need to be investigated. It is uneasy to improve the fuel economy of parallel HEVs with a single electric motor from control perspective only. In this article, the battery size of the architecture is optimized by using the dynamic programming (DP) approach, based on a dynamic degradation model of the LiFePO4 battery. Moreover, based on the DP results, a near-optimal control strategy of the hybrid powertrain system for online application is proposed. Finally, with two economic assumptions, the initial costs, operation costs, and payback periods are obtained in a total cost of ownership framework perspective.

Seatbelt and airbags provide effective occupant restraint, but are also potential to induce intrusive deformation and submarining injuries in motor vehicle crashes. To address these issues, this study puts forward a new restraint concept that applies restraint loads on shoulders and knees/femurs, i.e., the sturdiest regions of human body, via a combined use of shoulder bolster and knee bolster based on biomechanical computational analysis. The load characteristics of the two bolsters were optimized to obtain protection effectiveness superior to conventional use of seatbelt and airbag. Occupant kinematics and kinetics were taken into account, including the excursions of head, shoulders and knees, the accelerations of head and chest, and the compressions of thorax on several locations on the ribcage. The injury risk of rib fractures was monitored based on the strain levels of ribcage.

Low back pain has a higher prevalence among drivers who have long term history of vehicle operations. Vehicle vibration has been considered to contribute to the onset of low back pain. However, the fundamental mechanism that relates vibration to low back pain is still not clear. Little is known about the relationship between vibration exposure, the biomechanical response, and the physiological responses of the seated human. The aim of this study was to determine the vibration frequency that causes the increase of muscle activity that can lead to muscle fatigue and low back pain. This study investigated the effects of various vibration frequencies on the lumbar and thoracic paraspinal muscle responses among 11 seated volunteers exposed to sinusoidal whole body vibration varying from 4Hz to 30Hz at 0.4 g of acceleration. The accelerations of the seat and the pelvis were recorded during various frequency of vibrations. Muscle activity was measured using electromyography (EMG).

The electromagnetic regenerative suspension has attracted much attention recently due to its potential to improve ride comfort and handling stability, at the same time recover kinetic energy which is typically dissipated in traditional shock absorbers. The key components of a ball-screw regenerative suspension system are a motor, a ball screw and a nut. For this kind of regenerative suspension, its damping character is determined by the motor's torque-speed capacity, which is different from the damping character of the traditional shock absorber. Therefore, it is necessary to establish a systematic approach for the parameter matching of ball-screw regenerative suspension, so that the damping character provided by it can ensure ride comfort and handling stability. In this paper, a 2-DOF quarter vehicle simulation model with regenerative suspension is constructed. The effects of the inertia force on ride comfort and handling stability are analyzed.

This work presents a multi-objective adaptive cruise control (ACC) system via deep reinforcement learning (DRL). During the control period, it quantitatively considers three indexes: tracking accuracy, riding comfort, and fuel economy. The system balances contradictions between different indexes to achieve the best overall control results. First, a hierarchical control architecture is utilized, where the upper level controller is synthesized under DRL framework to give out the vehicle desired acceleration. The lower level controller executes the command and compensates vehicle dynamics. Then, four state variables that can comprehensively determine the car-following states are selected for better convergence. Multi-objective reward function is quantitatively designed referring to the evaluation indexes, in which safety constraints are considered by adding violation penalty. Thereafter, the training environment which excludes the disturbance of preceding car acceleration is built.

Due to the extended range hybrid vehicle powertrain system having multivariable and non-linear characters, this paper proposed a real-time simulation development platform scheme based on model design. First, a segmented energy management strategy (thermostat + power following) was proposed, which aims to improve the engine operation efficiency and reduce the losses during both charging and discharging. Second, the offline simulation model of the extended range hybrid vehicle powertrain system is established, which can realize the control function and meet the requirements of the real vehicle. Third, the hardware in the loop simulation platform of the vehicle controller is established, and the vehicle control program can run correctly in the real-time controller. The test of the offline simulation by Matlab/Simulink and the controller’s hardware in the loop (HIL) test are completed.

An imperceptible engine start is critical to the acceptance of hybrid vehicles. This paper focusses on an optimal control problem that tries to reduce vibration during engine start. Efforts are made to obtain the optimal speed trajectory that could cause minimum vibration during engine start. In the first section, the target diesel powertrain is introduced. A four cylinder diesel engine is coaxially paralleled with an ISG motor. The ISG motor serves as the engine starter and engine flywheel. Its dynamic model is established using crank-link dynamics. Secondly, an index is brought out to evaluate the severity of vibration. The cylinder pressure variation is the main cause of engine torque ripple, which in turn results in engine speed fluctuation. The square of the angular acceleration is chosen as the index of vibration. The index shows a positive relation of cylinder pressure in terms of amplitude.

This paper presents an investigation of drivability issue of engine start-stop. Hybrid vehicles provide excellent benefits regarding fuel efficiency and emission. However, vibration results from constant engine start and stop events generate drivability issues, thus compromising driving comfort. This paper has designed a high speed torque sensor to capture instantaneous torque at the engine shaft. Its consequences help to find out the most suitable index of vibration severity. This paper is organized in four sections. The first section introduces the powertrain to be studied. The second section introduces development of a specially designed torque sensor. The torque sensor is installed between the engine and ISG (Integrated Starter Generator), alongside with an encoder. The torque sensor is utilized to collect the instantaneous shaft torque on occasion of engine start. In the third section, this paper has performed two experiments.

With the increasing regulatory stringency on emission reduction and efficiency improvement, the automotive industry has experienced a significant shift in the hardware platform. Among technology candidates, hybrid technology is still considered one of the most viable approaches to meet the regulation requirement (both emission and efficiency) at an affordable cost to both the customer and the manufacturer. New engine operating characteristics are expected in hybrid applications which would potentially result in different performance requirements for the engine oil. Therefore, it is crucial to understand those characteristics of a hybrid powertrain, from which the insights of fluid requirements can be derived. A hybrid vehicle test study was conducted to evaluate the engine operation of different kinds of hybrid platforms. The hybrid operation has been well characterized by thoroughly analyzing parameters on each engine.