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This paper introduces the characteristics of the 4 wheel independent driving/4 wheel independent steering (4WID/4WIS) electric vehicle (EV). Models of Subsystems and the vehicle are constructed based on Matlab/simulink. The vehicle model allows the inputs of different drive torques and steer angles of four wheels. The dynamic characteristics of drive motors and steer motors are considered, and also it can reflect the vehicle longitudinal dynamics change due to the increase of the mass and inertia of the four wheels. Besides, drive mode selection function that is unique to this type vehicle is involved. Simulations and analyses of crab, oblique driving and zero radius turning which are the special conditions of 4WID/4WIS EV are conducted. The results show that the model can reflect the dynamic response characteristics. The model can be used to the simulation analyses of handling, stability, energy saving and control strategies verification of 4WID/4WIS EVs.

Transport vehicles consume a large amount of fuel with low efficiency, which is significantly affected by drivers' behaviors. An assessment system of eco-driving pattern for buses could identify the deficiencies of driver operation as well as assist transportation enterprises in driver management. This paper proposes an assessment method regarding drivers' economic efficiency, considering driving conditions. To this end, assessment indexes are extracted from driving economy theories and ranked according to their effect on fuel consumption, derived from a database of 135 buses using multiple regression. A layered structure of assessment indexes is developed with application of AHP, and the weight of each index is estimated. The driving pattern score could be calculated with these weights.

The earth's fossil energy is not limitless, and we should be taking advantage of the highly developed fields of science and technology to utilize it more efficiently and to create a fully environmentally friendly life. Considering the prodigious amount of vehicles in the world today, even a small improvement in their energy-saving performance could have a significant impact. In this paper, a new type of electro-hydraulic power steering (EHPS) system is described. It has two main advantages. First, it can significantly decrease the demand on the motor so that it can be used for a wider range of vehicles. Second, its pressure-flow characteristic can be programmed and is more flexible than hydraulic power steering (HPS) system. A prototype with a 500 W motor was applied to a truck with a front load of 2,700 kg, and static steer sweep tests were conducted to validate its feasibility.

Lane detection is one of the most important part in ADAS because various modules (i.e., LKAS, LDWS, etc.) need robust and precise lane position for ego vehicle and traffic participants localization to plan an optimal routine or make proper driving decisions. While most of the lane detection approaches heavily depend on tedious pre-processing and great amount of assumptions to get reasonable result, the robustness and efficiency are deteriorated. To address this problem, a novel framework is proposed in this paper to realize robust and real-time lane detection. This framework consists of two branches, where canny edge detection and Progressive Probabilistic Hough Transform (PPHT) are introduced in the first branch for efficient detection.

Velocity prediction on hilly road can be applied to the energy-saving predictive control of intelligent vehicles. However, the existing methods do not deeply analyze the difference and diversity of road slope driving characteristics, which affects prediction performance of some prediction method. To further improve the prediction performance on road slope, and different road slope driving features are fully exploited and integrated with the common prediction method. A rolling prediction-based multi-scale fusion prediction considering road slope transition driving characteristics is proposed in this study. Amounts of driving data in hilly sections were collected by the advanced technology and equipment. The Markov chain model was used to construct the velocity and acceleration joint state transition characteristics under each road slope transition pair, which expresses the obvious driving difference characteristics when the road slope changes.

Internal combustion engines, as the dominant power source in the transportation sector and the primary contributor to carbon emissions, face both significant challenges and opportunities in the context of achieving carbon neutral goal. Biofuels, such as biodiesel produced from biomass, and zero-carbon fuel ammonia, can serve as alternative fuels for achieving cleaner combustion in internal combustion engines. The dual-fuel combustion of ammonia-biodiesel not only effectively reduces carbon emissions but also exhibits promising combustion performance, offering a favorable avenue for future applications. However, challenges arise in the form of unburned ammonia (NH3) and N2O emissions. This study, based on a ammonia-biodiesel duel-fuel engine modified from a heavy-duty diesel engine, delves into the impact of adjustments in the two-stage injection strategy on the combustion and emission characteristics.

A feasible method was developed to reconstruct the three-dimensional flame surface of SI combustion based on 2D images. A double-window constant volume vessel was designed to simultaneously obtain the side and bottom images of the flame. The flame front was reconstructed based on 2D images with a slicing model, in which the flame characteristics were derived by slicing flame contour modeling and flame-piston collision area analysis. The flame irregularity and anisotropy were also analyzed. Two different principles were used to build the slicing model, the ellipse hypothesis modeling and deep learning modeling, in which the ellipse hypothesis modeling was applied to reconstruct the flame in the optical SI engine. And the reconstruction results were analyzed and discussed. The reconstruction results show that part of the wrinkled and folded structure of the flame front in SI engines can be revealed based on the bottom view image.

Polyoxymethylene dimethyl ethers (PODEn) have high cetane number, high oxygen content and high volatility, therefore can be added to gasoline to optimize the performance and soot emission of Gasoline Compression Ignition (GCI) combustion. High speed imaging was used to investigate the spray and combustion process of gasoline/PODEn blends (PODEn volume fraction 0%-30%) under various ambient conditions and injection strategies in a constant volume vessel. Results showed that with an increase of PODEn proportion from 10% to 30%, liquid-phase penetration of the spray increased slightly, ignition delay decreased from 3.8 ms to 2.0 ms and flame lift off length decreased 29.4%, causing a significant increase of the flame luminance. For blends with 20% PODEn, when ambient temperature decreased from 893 K to 823 K, the ignition delay increased 1.3 ms and the flame luminance got lower.

Intelligent driving, aimed for collision avoidance and self-navigation, is mainly based on environmental sensing via radar, lidar and/or camera. While each of the sensors has its own unique pros and cons, camera is especially good at object detection, recognition and tracking. However, unpredictable environmental illumination can potentially cause misdetection or false detection. To investigate the influence of illumination conditions on detection algorithms, we reproduced various illumination intensities in a photo-realistic virtual world, which leverages recent progress in computer graphics, and verified vehicle detection effect there. In the virtual world, the environmental illumination is controlled precisely from low to high to simulate different illumination conditions in the driving scenarios (with relative luminous intensity from 0.01 to 400). Sedan cars with different colors are modelled in the virtual world and used for detection task.

Advanced compression ignition combustion system which reduces simultaneously both nitride oxides (NOx) and particulate matter (PM) is a promising approach to meet future emission regulations. In order to achieve advanced compression ignition, flexible fuel injection is required for ultra-early and post-TDC injections, which conventional injector fails to accomplish due to wall-wetting effect. In this work, special injectors with the spray angle of 60 degree are applied on a 4 cylinder mass-production diesel engine without modification of the engine configuration. For application-oriented study, sweep experiments of injection timings and durations, fuel injection pressure and the boost pressure are carried out to investigate the relationships between the control parameters and the engine performance. Model based calibration and real application tests validate the maximum applicable operation range of maximum speed of 2200 RPM and IMEP of 8.0 bar.

In quasi-static tension and compression tests of thermoplastics, full-field strain distribution on the gage section of the specimen can be captured using the two-dimensional digital image correlation method. By loading the test specimens made of a talc-filled and impact-modified polypropylene up to tensile failure and large compressive strains, this study has revealed that inhomogeneous deformation within the gage section occurs quite early for both test types. This leads to the challenge of characterizing the mechanical properties - some mechanical properties such as stress-strain relationship and fracture strain could depend on the measured section length and location. To study this problem, the true stress versus true strain curves determined locally in different regions within the gage length are compared.

For the purpose of lessening fuel consumption, engine noise, shift jerk and clutch friction work in the shift process of Automatic Mechanical Transmission (AMT), a fuzzy-bang bang dual mode control strategy for engine rotate speed is put forward in this paper, which takes the advantages of time optimal control and fuzzy control. The combined control strategy is applied to the shift process control of AMT test minibus named SC6350 and proved to be successful by the experimental results.

The automotive industry is aggressively pursuing fuel efficiency improvements through hybridization of production vehicles, and there are an increasing number of racing series adopting similar architectures to maintain relevance with current passenger car trends. Hybrid powertrains offer both performance and fuel economy benefits in a motorsport setting, but they greatly increase control complexity and add additional degrees of freedom to the design optimization process. The increased complexity creates opportunity for performance gains, but simulation based tools are necessary since hybrid powertrain design and control strategies are closely coupled and their optimal interactions are not straightforward to predict. One optimization-related advantage that motorsports applications have over production vehicles is that the power demand of circuit racing has strong repeatability due to the nature of the track and the professional skill-level of the driver.

Hybrid Electric Vehicles with a power split system provide a variety of possibilities to promote the fuel economy of vehicles and better adapt to various driving conditions. In this paper, a new power split system of a hybrid electric bus which consists of double planetary gear sets and a clutch is introduced. The system is able to decouple both the torque and speed of the engine from the road load, which makes it possible for the engine to operate on its optimal operation line (OOL). Considering the features of the system configuration and bus driving cycle, the driving mode of the bus is divided into Electric Vehicle (EV) mode, Electric Variable Transmission (EVT) mode and Parallel mode. By controlling the engagement of the clutch at high vehicle speed (after the mechanical point), the system operates in the parallel mode rather than EVT mode. This avoids the problem that the system efficiency sharply declines in high speed region which EVT configurations are generally faced with.

The platooning of connected automated vehicles (CAV) possesses the significant potential of reducing energy consumption in the Intelligent Transportation System (ITS). Moreover, with the rapid development of eco-driving technology, vehicle platooning can further enhance the fuel efficiency by optimizing the efficiency of the powertrain. Since road grade is a main factor that affects the energy consumption of a vehicle, the estimation of the road grade with high accuracy is the key factor for a connected vehicle platoon to optimize energy consumption using vehicle-to-vehicle (V2V) communication. Commonly, the road grade is quantified by single consumer grade global positioning system (GPS) with the geodetic height data which is rough and in the meter-level, increasing the difficulty of precisely estimating the road grade.

The lightweight technology takes an important role in electric vehicle(EV) energy conservation domain, as lighter vehicle means less energy consumed under same condition. In this paper, the typical energy requirement in an NEDC cycle is investigated, and the relationship between lightweight rate and energy consumption reduction effectiveness is given. The benefit of lightweight to EV come from the less battery cost because of less energy requirement. For EVs, with less battery cost, a certain lightweight rate can be obtained with less total cost. On the other hand, if lightweight rate is very high, the battery cost won't be able to cover the lightweight cost. Besides, the relationship between driving range and battery capacity is discussed in this paper. It is found that there is a limitation of EV driving range, which is determined by the battery energy density.

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.

Many types of brake by wire systems have been developed in past years, such as EMB (Electro-mechanical Brake) [1, 2], DEHB (Distributed Electro-hydraulic Braking System) [3] and EWB (Electric Wedge Brake) [4]. When the vehicle need braking in long period such as waiting for traffic light or downhill braking in those brake systems, the current will sustain very long time with very high level. This current will result in high temperature in motor, and will damage the power supplier. When a new DEHB is developing, a holding function is added in this DEHB. The holding function is self-energized when holding the brake, and automatic released after the brake. Advantageously, after activation of the holding function, the current delivered to the motor for braking is substantially decreased, especially, will be zero when the brake torque is not need to adjust.

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.

This paper summarized the first phase research work to develop hybrid transit buses for China, including driving cycle analysis, performance requirements setting, key components first dimensioning, configuration choosing, saving potential estimate and parametric study. Through these fundamental works, we realize that (1) the Chinese urban bus cycle has some specialties compared with foreign ones, and these specialties cause differences on the design criteria and design results of the hybrid buses; (2) the parallel configuration is better than the series one for the Chinese cycle from both fuel consumption and cost points of view.