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In this research, the interior noise of a passenger car was measured, and the sound quality metrics including sound pressure level, loudness, sharpness, and roughness were calculated. An artificial neural network was designed to successfully apply on automotive interior noise as well as numerous different fields of technology which aim to overcome difficulties of experimentations and save cost, time and workforce. Sound pressure level, loudness, sharpness, and roughness were estimated by using the artificial neural network designed by using the experiment values. The predicted values and experiment results are compared. The comparison results show that the realized artificial intelligence model is an appropriate model to estimate the sound quality of the automotive interior noise. The reliability value is calculated as 0.9995 by using statistical analysis.

Based on the traditional heavy commercial vehicle, hydraulic hub-motor drive vehicle (HHMDV) is equipped with a hydraulic hub-motor auxiliary drive system, which makes the vehicle change from the rear-wheel drive to the four-wheel drive to improve the traction performance on low-adhesion road. In the typical operating mode of the vehicle, the leakage of the hydraulic system increases because of the oil temperature rising, this makes the control precision of the hydraulic system drop. Therefore, a temperature compensation control strategy for the assist mode is proposed in this paper. According to the principle of flow continuity, considering the loss of the system and the expected wheel speed, the control strategy of multifactor target pump displacement based on temperature compensation is derived. The control strategy is verified by the co-simulation platform of MATLAB/Simulink and AMESim.

The new control algorithm for parallel hybrid electric vehicle is presented systematically, in which engine operation points are limited within higher efficient area by the control algorithm and the state of charge (SOC) is limited in a range in order to enhance the batteries' charging and discharging efficiency. In order to determine the ideal operating point of the vehicle's engine, the control strategy uses a lookup table to determine the torque output of the engine. The off-line simulation model of parallel HEV power train is developed which includes the control system and controlled objective (such as engine, electric motor, battery pack and so on). The results show that the control algorithm can effectively limite engine and battery operation points and much more fuel economy can be achieved than that of conventional one.

As a new brake-by-wire solution, the electro-booster (Ebooster) brake system can work with the electronic stability program (ESP) equipped in the real vehicle to realize various excellent functions such as basic force boosting (BFB), active braking and energy recovery, which is promoting the development of smart vehicles. Among them, the BFB is the function of Ebooster's servo force to assist the driver's brake pedal force establishing high-intensity braking pressure. After the BFB function failure of the Ebooster, it was not possible to provide sufficient brake pressure for the driver's normal braking, and eventually led to traffic accidents. In this paper, a compensation redundancy control strategy based on ESP is proposed for the BFB failure of the self-designed Ebooster.

Supercapacitor has the merits of low resistance and long lifecycle ability. When combined with battery, they can alleviate the burthen of battery, increase the battery's working efficiency and prolong its lifecycle. This paper introduces a control architecture based on balancing of SOC and algorithm based on fuzzy logic, Aiming at the two different cycles that have sufficient and insufficient energy that can be recovered from braking unlikely, this paper puts forward the methods of on-line adjusting fuzzy control parameters. Consequently, simulation was performed,and the results validate the effective adapting capacity of the control logic under different driving cycles.

Numerous modern military and commercial vehicles rely on portable, battery-powered sources for electric energy. Due to their highly specialized functions these vehicles are typically custom-designed, produced in limited numbers, and expensive. To mitigate the power system's contribution to these undesirable characteristics, this paper proposes a modular power system architecture consisting of “smart” power battery units (SPUs) that can be readily interconnected in numerous ways to provide distributed and coordinated system power management. The proposed SPUs contain a battery power source and a power electronics converter. They are compatible with multiple battery chemistries (or any energy storage device that can produce a terminal voltage), allowing them to be used with both existing and future energy storage technologies.

Using the nonlinear finite element analysis, three nonlinear characteristics of the rubber gasbag of the air spring on the bus are thoroughly analyzed, including the nonlinear characteristic of the rubber gasbag with multi layers of composite materials, the nonlinear large displacement geometry characteristic of the rubber gasbag on working, and the nonlinear contact characteristic of the rubber gasbag when contacts the pedestal and the top cover plate. A model is build and the nonlinear characteristic of the air spring on the bus is analyzed using the ABAQUS software. At last, the article discusses parameters that influence on the characteristic of the air spring for the bus.

A multitude of recent studies are suggestive of the EV as a paramount representative of the NEV, its development direction is transformed from “individuals adapt to vehicles” to “vehicles serve for occupants”. The multi-mode drive control technology is relatively mature in traditional auto control sphere, however, a host of EV continues to use a single control strategy, which lacks of flexibility and diversity, little if nothing interprets the vehicle performances. Furthermore, due to the complex road environment and peculiarity of vehicle occupants that different requirement has been made for vehicle performance. To solve above problems, this paper uses the key technology of mathematical statistics process in MATLAB, such as the mean, linear fitting and discrete algorithms to clean up, screening and classification the original data in general rules, and based on short trips in the segments of kinematics analysis method to establish a representative of quintessential driving cycle.

In order to study the static and dynamic characteristics of the thrust rod. Based on the multi-body dynamics theory, the dynamic model of the thrust rod and the vehicle system is established by using ADAMS software. The limit braking condition is simulated, and the limit braking load of the thrust rod is obtained. Thrust rod finite element model is established, the load calculation value and rubber test data as a finite element analysis of input conditions, using ABAQUS software to carry on the stiffness and strength analysis, analysis results show that the strength meets the requirement, and the stiffness and strength calculation result is in good agreement with the sample test, accurately describes the finite element model. The analytical method used can be used to predict the stiffness of the thrust rod.

Since road electric vehicles typically require a significantly variable and random load power demand in response to traffic conditions, such as frequent sequences of acceleration and deceleration and uphill followed by downhill runs. In this context, the energy management system of electric vehicle must ensure an effective power distribution between battery and supercapacitor to satisfy load demand. In this paper, the power management control strategy of hybrid energy storage system is developed by introducing terrain information to optimize system efficiency and battery lifetime. In this presented research, we aim at developing a power management control strategy considering the influence of the terrain information on system efficiency and battery lifetime.

The poor low-temperature behavior of Li-ion batteries has limited its application in the field of electric vehicles and hybrid electric vehicles. Many previous studies concentrate on developing new type of electrolyte to solve this problem. However, according to recent research, the key limitation at low temperature is the low diffusivity of lithium ion in the anode electrodes. Hence, it is potential to study anode materials to improve low-temperature behavior of LIBs. ZnFe2O4 with higher theoretical capacity is low toxicity and abundance, contributing to its commercial application. Different ZnFe2O4 crystalline shapes have different particle sizes. Among them, the cubic ZnFe2O4 with smaller particle size will increase its own electronic and ionic conductance at lower temperature. In this regard, we evaluated low-temperature performance of LIBs with ZnFe2O4 cubes as anode materials at -25°C.

This paper describes a novel power management control strategy of battery and supercapacitor hybrid energy storage system to improve system efficiency and battery lifetime. In the presented research, the high and low frequency power demand in the load is separated by a Haar wavelet transform algorithm to overcome the problem of battery overload work and associated degeneration in battery lifetime resulting from an ineffective distribution between battery and supercapacitor. The purpose of frequency distribution is that the supercapacitor is used to share high frequency power components of load power demand to smooth the power demand applied to battery. However, the sole frequency control often fails to realize the optimal utilization of supercapacitor because of the uncertain variation in the driving cycle.

For the purpose of predicting the interior noise of a passenger automobile at middle and high frequency, an energy finite element analysis (EFEA) model of the automobile was created using EFEA method. The excitations including engine mount excitation and road excitation were measured by road experiment at a speed of 120 km/h. The sound excitation was measured in a semi-anechoic chamber. And the wind excitation was calculated utilizing numeric computation method of computational fluid dynamics (CFD). The sound pressure level (SPL) and energy density contours of the interior acoustic cavity of the automobile were presented at 2000 Hz. Meanwhile, the flexural energy density and flexural velocity of body plates were calculated. The SPL of interior noise was predicted and compared with the corresponding value of experiment.

In order to solve the abnormal vibration of a light bus, order tracking analysis of finite element simulation and road test was made to identify the vibration source, finding that the rotation angular frequency of the wheels and the first two natural frequency of the body structure overlaps, resonance occurring which lead to increased vibration. To stagger the first two natural frequency and excitation frequency of the body, thickness of sheet metal and skeleton of the body-in-white were chosen as the design variables, rise of the first two natural frequency of the body-in-white as the optimization objective, optimal design and sensitivity analysis of the body-in-white was carried out with the modal analysis theory. Combining with the modal sensitivity and mass sensitivity of sheet metal and skeleton, the optimum design was achieved and tests analysis was conducted.

Hyperelastic model constants of rubber material are predicted based on test date. The fluid-structure interaction model of light vehicle cab's hydraulic mount is established. Static characteristics of the hydraulic mount are analyzed by quasi-static method. In dynamic characteristics analysis, the flow model of fluid is set to turbulent K-Epsilon RNG. The dynamic stiffness and loss angle of the hydraulic mount are presented via the finite element model. The simulations of static and dynamic characteristics agree well with corresponding test results. The effects of main structure parameters to the dynamic characteristics of the hydraulic mount are analyzed based on the finite element model.

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.

The slip ratio of vehicle driving wheels is easily beyond a reasonable range in the complex and changeable driving conditions. In order to achieve the adaptive acceleration slip regulation of four-wheel driving (4WD) vehicle, a fuzzy control strategy of Automatic Drive Train Management (ADM) system based on road situation identification was proposed in this paper. Firstly, the influence on the control strategy of ADM system was analyzed from two aspects, which included the different road adhesion coefficients and the vehicle’s ramp driving state. In the meantime several quantitative expressions of relevant control parameters were derived. Secondly, the fuzzy logic control algorithm was adopted to design a road situation identification subsystem and a ramp driving state identification subsystem respectively. The former was based on the μ-S curve model, and the latter was based on the vehicle driving equilibrium equation.

The traditional energy management algorithm is mainly based on a single driving cycle, it is obvious that many factors might be often neglected by designer, such as different driving cycles would suit for different control strategies. But they tend to make decisions on the balance of torque distribution and battery power that based on a single driving cycle. Therefore, it is very difficult to achieve the optimal control in each case. In this paper we introduce a new design concept of Multi-operating mode energy management, a mathematical model of the energy management applied to a hybrid vehicle system is presented. Results of simulations using the model with the Multi-operating mode energy management were compared with results of simulations using a model with the single mode energy management, allowing the energy efficiency evaluation of the proposed energy management system.

In order to improve drivers’ acceptance to advanced driver assistance systems (ADAS) with better adaptation, drivers’ driving behavior should play key role in the design of control strategy. Adaptive cruise control systems (ACC) have many factors that can be influenced by different driving behavior. It is important to recognize drivers’ driving behavior and take human-like parameters to the adaptive cruise control systems to assist different drivers effectively via their driving characteristics. The paper proposed a method to recognize drivers’ behavior and intention based on Gaussian Mixture Model. By means of a fuzzy PID control method, a personalized ACC control strategy was designed for different kinds of drivers to improve the adaptabilities of the systems. Several typical testing scenarios of longitudinal case were created with a host vehicle and a traffic vehicle.

The RON(Research Octane Number) is the most important indicator of motor petrol, and the petrol refining process is one of the important links in petrol production. However, RON is often lost during petrol refining and RON Loss means the value of RON lost during petrol refining. The prediction of the RON loss of petrol during the refining process is helpful to the improvement of petrol refining process and the processing of petrol. The traditional RON prediction method relied on physical and chemical properties, and did not fully consider the high nonlinearity and strong coupling relationship of the petrol refining process. There is a lack of data-driven RON loss models. This paper studies the construction of the RON loss model in the petrol refining process.