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With the increasingly serious global environmental and energy problems, as well as the increasing number of vehicles, pure electric vehicles with its advantages of environmental protection, low noise and renewable energy, become an effective way to alleviate environmental pollution and energy crisis. Due to the current pure electric vehicle power battery technology is not perfect, the range of pure electric vehicle has a great limit. Through the braking energy recovery, the energy can be reused, the energy utilization rate can be improved, and the battery life of pure electric vehicles can be improved. In this paper, a pure electric vehicle is taken as the analysis object, and the whole vehicle analysis model is built. Through the comparative analysis, based on the driver's braking intention and vehicle running state, the braking energy recovery control strategy of double fuzzy control is proposed.

In the field of Electric Vehicle (EV), what the driver is most concerned with is that whether the value of the battery's capacity is less than the failure threshold because of the degradation. And the failure threshold means instability of the battery, which is of great danger for drives and passengers. So the capacity is an important indicator to monitor the state of health (SOH) of the battery. In laboratory environment, standard performance tests can be carried out to collect a number of related data, which are available for regression prediction in practical application, such as the on-board battery pack. Firstly, we make use of the NASA battery data set to form the observed data sequence for regression prediction. And a practical method is proposed to determine the minimum embedding dimension and get the recurrence formula, with which a capacity model is built.

Discretionary lane changing is commonly seen in highway driving. Intelligent vehicles are expected to change lanes discretionarily for better driving experience and higher traffic efficiency. This study proposed to optimize the decision-making and trajectory-planning process so that intelligent vehicles made lane changes not only with driving safety taken into account, but also with the goal to improve driving comfort as well as to meet the driver’ s expectation. The mechanism of how various factors contribute to the driver’s intention to change lanes was studied by carrying out a series of driving simulation experiments, and a Lane-Changing Intention Generation (LCIG) model based on Bi-directional Long Short-Term Memory (Bi-LSTM) was proposed.

The thermal management system is essential for the safe and long-term operation of the power battery. The temperature difference between the individual cells exceeds the acceleration of the battery performance, which leads to battery out of use and affects the performance of the vehicle. Compared with the low heat transfer coefficient of the air-cooling system, the complex structure of the liquid-cooling system and the large quality of phase change material system, the heat pipe has high thermal conductivity, strong isothermal performance and light weight, it’s an efficient cooling element that can be used for thermal management. In this study, the flat plate heat pipe(FPHP) is used to manage the temperature of the battery, through experiments, the optimized placement of the flat heat pipe is obtained.

This paper presents a method of calculating temperature field of the piston by using a wavelet neural network (WNN) to identify the unknown boundary conditions. Because of the complexity of the heat transfer and limitations of experimental conditions of heat transfer analysis of the piston in a diesel engine, boundary conditions of the piston temperature field were usually obtained empirically, and thus the result itself was uncertain. By employing the capability of resolution analysis from a wavelet neural network, the method obtains improved boundary heat transfer coefficients with a limited number of measured temperatures. Using FEA software iteratively, results show the proposed wavelet neural network analysis method improves the prediction of unknown boundary conditions and temperature distribution consistent with the experimental data with an acceptable error.

With the increase of car ownership and the complex and crowded parking environment, it is difficult for drivers to complete the parking operation quickly and accurately, which may cause traffic accidents such as vehicle collisions and road jams because of poor parking skills. The emergence of an automatic parking system can help drivers park safely and reduce the occurrence of safety accidents. In this paper, the neural network identifier on the control method of an adaptive integral derivative of a neural network is proposed for an automatic parallel parking system with front-wheel steering is studied by using MATLAB/Simulink environment, and the simulation is carried out. Firstly, according to vehicle parameters and obstacle avoidance constraints, the minimum parking space, and parking starting position are calculated. Meanwhile, the path planning of parallel parking spaces is carried out by quintic polynomial.

Magnetorheological fluid (MRF) is used as the transmission medium of the hydraulic retarder. The rheological properties are regulated by changing the magnetic field to achieve accurate control of the retarder's braking torque. Under the action of the external magnetic field, the flow structure and performance of the MRF retarder will be changed in a short time. The apparent viscosity coefficient increases by several orders of magnitude, the fluidity deteriorates and the heat generated by the brake cannot be transferred through the liquid circulation, which will affect the braking torque of the retarder. Changing the surface area of the stator also has an influence on the braking torque of the retarder and the wall heat dissipation. In this study, the relationship between the braking torque of the MRF retarder and the stator surface area of the retarder was analyzed.

Driver distraction detection is crucial to driving safety when autonomous vehicles are co-piloted. Recognizing drivers’ behaviors that are highly related with distraction from real-time video stream is widely acknowledged as an effective approach mainly due to its non-intrusiveness. In recently years, deep learning neural networks have been adopted to by-pass the procedure of designing features artificially, which used to be the major downside of computer-vision based approaches. However, the detection accuracy and generalization ability is still not satisfying since most deep learning models extracts only spatial information contained in images. This research develops a driver distraction model based on a two-stream, spatial and temporal, convolutional neural network (CNN).

In order to avoid the braking recession on heavy commercial vehicles caused by the long-distance continuous braking of the main brake, the hydraulic retarder is widely used as an important brake auxiliary device in various heavy commercial vehicles to improve the vehicle safety. However, the hydraulic retarder not only has the advantages of large braking torque and good stability, but also has the disadvantages of poor retarding ability at low rotating speed, braking lag and difficulty in accurately controlling the braking torque. This paper introduces a new type of hydraulic retarder. The new retarder replaces the oil in the retarder with magnetorheological fluid and applies a magnetic field in the retarder arrangement space, so that slows down the vehicle by using the rheological properties of the magnetorheological fluid under the magnetic field.

On-road testing of driving performance of the urban bus was carried out, and a representative urban bus driving cycle was developed after on-road testing, according to the test results. Then, the vehicle simulation software AVL CRUISE was used to simulate the dynamic behavior of the urban bus. It involves the simulation of complete drive train system and the driver behavior. The model is validated by comparing the results of the simulation to the results of the field test. Then the developed driving cycle is evaluated by fuel consumption resulted from the simulation and engine bench test on fuel economy.

Dual-Clutch-Transmission (DCT) is one kind new automatic transmission which has double clutch structure. The most important unit of DCT is Transmission-Control-Module (TCM).In the development process of TCM, simulation is an important research tools. We have analyzed the DCT principle of work, established its mathematical model, created the charge and discharge oil models of typical wet dual clutch transmission, established the control logic to unify and separate double clutch in turn, and also designed out the shift control using fuzzy control using MATLAB/Simulink software. Utilizing engine model, driver model, the DCT model, the TCM model, the vehicle model, established the vehicle simulation model, and implemented simulation; Result indicated that, the established model can correctly reflect the torque and speed change when shifted gears and can correctly realize the automatic shift gears.

On road simulation, both the traditional iterative method based on frequency response function (FRF) and adaptive control method based on the CARMA model are realized by using linear model to identify the target test system. However the real test system is very complicated because of various nonlinear factors. Linear models approximately describe the system only in a small range. Therefore, system simulation methods can not be used to validate the developed control algorithm and the uncertainty of test accordingly increases. As mentioned above, this paper presents a model to identify the nonlinear test system using NARMA dynamic neural network and discusses how to make the model parameters in detail. Using the test input-output series data, this network was trained by Levenberg-Marquardt method. Results of verification simulation show the validation of the nonlinear model.

In this paper a new pressure control method of a modified accumulator-type Electro-hydraulic Braking System (EHB) is proposed. The system is composed of a hydraulic motor pump, an accumulator, an integrated master cylinder, a pedal feel simulator, valves and pipelines. Two pressurizing modes are switched between by-motor and by-accumulator to adapt different pressure boost demands. A differentiator filtering raw sensor signal and calculating pedal speed is designed. By using the pedal feel simulator, the relationship between wheel pressures and brake force is decoupled. The relationships among pedal displacement, pedal force and wheel pressure are calibrated by experiments. A model-based PI controller with predictor is designed to lower the influences caused by delay. Moreover, a self-tuning regulator is introduced to deal with the parameter’s time-varying caused by temperature, brake pads wearing and delay variation.

Vehicle hydraulic retarders are applied in heavy-duty trucks and buses as an auxiliary braking device. In traditional cooling systems of hydraulic retarders, the working fluid is introduced into the heat exchanger to transfer heat to the cooling liquid in circulation, whose heat is then dissipated by the engine cooling system. This prevents the waste heat of the working fluid from being used effectively. In hydraulic retarder cooling system based on the Organic Rankine Cycle, the organic working fluid first transfers heat with the hydraulic retarder working fluid in Rankine cycle, and then outputs power through expansion machine. It can both reduce heat load of the engine cooling system, and enhance thermal stability of the hydraulic retarder while recovering and utilizing braking energy. First of all, according to the target vehicle model, hydraulic retarder cooling system model based on Rankine cycle is established.

With the continuous increasing requirements of commercial vehicle weight and speed on highway transportation, conventional friction brake is difficult to meet the braking performance. To ensure the driving safety of the vehicle in the hilly region, the eddy current retarder (ECR) has been widely used due to its fast response, lower prices and convenient installation. ECR brakes the vehicle through the electromagnetic force generated by the current, and converted vehicle mechanical energy into heat through magnetic field. Air cooling structure is often used in the traditional ECR and cooling performance is limited, which causes low braking torque, thermal recession, and low reliability and so on. The water jacket has been equipped outside the eddy current region in this study, and the electric ECR is cooled through the water circulating in the circuit, which prolongs its working time.

This research is based on the Controller Area Network (CAN) bus, and briefly analyzed its communication protocol with reference to the layered model of Open System Interconnect Reference Model (OSI). Subsequently, a data acquisition system was designed and developed including a Vehicle Communication Interface (VCI) and a laptop. After the overall architecture was built, the communication mechanism of the VCI was studied. Furthermore, the lap top app was built using the layered design followed by the implementation of a scheme for data collection and experimentation involving the test driving of a real car on road. Finally, the driving style was identified by means of fuzzy reasoning and solving ambiguity based on fuzzy theory; via training the acceleration sample and forecast using the excellent learning and generalization ability of Support Vector Machine (SVM) for high-dimensional, finite samples.

Lithium-ion battery plays a key role in electric vehicles, which is critical to the system availability. One of the most important aspects in battery managements systems(BMS) in electric vehicles is the stage of health(SOH) estimation. The state of health (SOH) estimation is very critical to battery management system to ensure the safety and reliability of EV battery operation. The classical approach of current integration(coulomb counting) can't get the accurate values because of accumulative error. In order to provide timely maintenance and replacements of electric vehicles, several estimation approaches have been proposed to develop a reliable and accurate battery state of health estimation. A common drawback of previous algorithm is that the computation quantity is huge and not quite accurate, that is updated partially in this study.

Whether the available energy of the on-board battery pack is enough for the driver’s next trip is a major contributor in slowing the growth rate of Electric Vehicles (EVs). What’s more, the actual capacity of the battery pack depend on so many factors that a real-time estimation of the state of charge of the battery pack is often difficult. We proposed a big-data based algorithm to build a battery pack dynamic model for the online state of charge estimation and a stochastic model for the energy consumption prediction. And the good performance of sensors, high-bandwidth communication systems and cloud servers make it convenient to measure and collect the related data, which are grouped into three categories: standard, historical and real-time data. First a resistance-capacitance ( RC )-equivalent circuit is taken consideration to simplify the battery dynamics.

Traction batteries are operated in severe working conditions of wide temperature range as the vehicles run in different seasons and regions, which effects battery performance deeply. Investigation on the effect of temperature under such circumstances on battery performance is very significant to promote the application of traction battery. In this paper, some tests are conducted on a ternary-material lithium-ion battery at various temperatures. The cycling performance and some significant parameters are evaluated at the whole temperature range, especially at the extreme temperatures (below -10°C or above 45°C). The results show that the battery performance becomes poor obviously at low temperatures, which is reflected in the decreased terminal voltage and the faded discharge capacity, and at too high temperatures (above 45°C), power and capacity also decrease, which happens in the later period of discharge process.

In these years, the advantages of using phase change material (PCM) in the thermal management of electric power battery has been wide spread. Because of the thermal conductivity of most phase change material (eg.wax) is low, many researchers choose to add high conductivity materials (such as black lead). However, the solid-liquid change material has large mass, poor flow-ability and corrosively. Therefore, it still stays on experiential stage. In this paper, the Thermal characteristics of power battery firstly be invested and the requirements of thermal management system also be discussed. Then a new PCM thermal management has been designed which uses pure water as liquid phase change material, adopts PCM with a reflux device for thermal management.