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With the increase of hotel and ancillary loads and replacement of engine driven mechanical and hydraulic loads with electrical loads, automotive systems are becoming more electric. This is the concept of More Electric Cars (MEC) that necessitates a higher system voltage, such as the proposed 42V, for conventional cars. In this paper, the development of the 42V electric power system for vehicle applications is reviewed. The system architecture and motor drive problems associated with the 42V electric power system are analyzed. Solutions to these problems are also discussed.

The increasing power demand in vehicles has resulted in a need for a higher onboard generation capacity. With the increasing generation requirement, the torque levels of the generator are found to closely converge with that of the starter motor. Hence, integrating the two machines and using a single machine for the two purposes would be technically viable and economically advantageous. This results in a more compact design solution as well. The Integrated Starter/Alternator (ISA) will be integrated directly to the crankshaft of the Internal Combustion Engine (ICE) and deliver 5 kW average and 12-15 kW peak power at 42V.

Electronic Stability Control (ESC) is an important measure to proactively guarantee vehicle safety. In this paper, the method of four-wheel hub-motor torque control is compared with the traditional single-wheel hydraulic brake control in ESC system. The control strategy adopts the hierarchical structure. In upper controller, the stability of the vehicle is identified by threshold method, the additional yaw moment control uses a way to get the moment including feedforward and feedback parts based on the linear quadratic regulator (LQR). The medium controller is tire slip rate control, in order to get the optimal target slip rate from the upper additional yaw moment, a method of quadratic programming to optimize the longitudinal force is proposed for each wheel. The inputs of tire state for the magic tire model is introduced so as to calculate the target slip rate from the target longitudinal force.

In this paper, a high-efficiency and low-cost lithium-ion battery pack active balance system is designed. It adopts a distributed structure and consists of three parts: auxiliary power module, one-way isolated DC/DC conversion module, and a battery group. The battery single cells in the battery pack are layered and divided into m battery groups in total, and each battery group is composed of n battery single cells. Each battery group is connected to an isolated DC/DC conversion module, and all the conversion modules are connected in parallel with the auxiliary power. Taking the SOC average value of the all-single cells in one battery group as the balancing variable, the auxiliary power is controlled to charge the battery group with the lower SOC average value, so that the difference of the SOC average value of all battery groups is within the set threshold range, so as to realize the active balance of each battery group.

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.

A library of macro modules has been written that represent elements common to powertrains of off-highway equipment with diesel powerplants and powershift transmissions. This library allows users to easily and quickly develop complex models of a wide range of vehicle and transmission configurations. These simulation models can be used to evaluate dynamic loadings on the powertrain components, evaluate shift quality, develop control systems and address other powertrain dynamic problems. The library makes use of EASY5 simulation language features to effectively handle such drivetrain nonlinearities as backlash, coulomb friction and hard stops.

Overloading of trucks can easily cause damage to roads, bridges and other transportation facilities, and accelerate the fatigue loss of the vehicles themselves, and accidents are prone to occur under overload conditions. In recent years, various countries have formulated a series of management methods and governance measures for truck overloading. However, the detection method for overload behavior is not efficient and accurate enough. At present, the method of dynamic load identification is not perfect. No matter whether it is the dynamic weight measurement method of reconstructing the road surface or the non-contact dynamic weight measurement method, little attention is paid to the difference of different vehicles. Especially for different vehicles, there should be different load limits, and the current devices are not smart enough.

The vehicle overload seriously jeopardizes traffic safety and affects traffic efficiency. At present, the static weighing station and weigh-in-motion station are both relatively fixed, so the detection efficiency is not high and the traffic efficiency is affected; the on-board dynamic weighing equipment is difficult to be popularized because of the problem of being deliberately damaged or not accepted by the purchaser. This paper proposes an efficient, accurate, non-contact vehicle overload identification method which can keep the road unimpeded. The method can detect the vehicle overload by the relative distance (as the characteristic distance) between the dynamic vehicle's marking line and the road surface. First, the dynamics model of the vehicle suspension is set up. Then, the dynamic characteristic distance of the traffic vehicle is detected from the image acquired by the calibrated camera based on computer vision and image recognition technology.

In order to solve the environmental pollution and energy crisis, Electric Vehicles (EVs) have been developed rapidly. Lithium-ion (Li-ion) battery is the key power supply equipment for EVs, and the scientific and accurate prediction of its Remaining Useful Life (RUL) has become a hot topic in the field of new energy research. The internal resistance and capacity are often used to characterize the Li-ion battery State of Health (SOH) from which RUL is obtained. However, in practical applications, it is difficult to obtain internal resistance and capacity information by using the non-intrusive measurement method. Therefore, it is necessary to extract the measurable parameters to characterize the degradation of Li-ion battery. At present, the methods of extracting health indicators based on measurable parameters have gained preliminary results, but most of them are derived from the Li-ion battery discharging data.

In automatic driving application, the velocity planner can be considered as a key factor to ensure the safety and comfort. One of the most important tasks of the velocity planner is to simulate the velocity characteristics of human drivers. In this paper, two Driver In-the-Loop (DIL) experiments are designed to explain velocity characteristics of human drivers. In the first experiment, static obstacles are placed on both sides of the straight road to shorten the cross range that vehicles can driver across. Moreover, different cross ranges are set to study the influence of the steering wheel error. In the second experiment, velocity characteristics are investigated under the condition of different road widths and curvatures in a U-turn road contour. In both tests, different drivers’ preview behavior is analyzed through the operation of throttle, braking, and steering.

The high gravity center of the concrete mixer truck reduces the truck’s stability while steering. The rolling stirring tank makes the stability even worse than the regular engineering vehicle due to the dynamic variation of the centroid position. Most of the researches on the rollover stability of concrete mixer trucks focus on the rollover model establishment and dynamic simulation module. The change of concrete centroid is ignored when the safety cornering speed is calculated. This paper proposes a pre-warning method for the cornering speed of concrete mixer trucks based on centroid dynamic simulation. In the method, the mixing tank stirring model and the vehicle driving dynamic model are established on the Fluent and TruckSim simulation platforms, respectively. The theoretical speed threshold obtained by simulation is used as the evaluation index of the warning speed in the curve. Firstly, the dynamic simulation of the stirring tank model is carried out by Fluent.

Intelligent sweeper vehicle is gradually applied to human life, in which the accuracy of garbage identification and classification can improve cleaning efficiency and save labor cost. Although Deep Learning has made significant progress in computer vision and the application of semantic network segmentation can improve waste identification rate and classification accuracy. Due to the loss of some spatial information during the convolution process, coupled with the lack of specific datasets for garbage identification, the training of the network and the improvement of recognition and classification accuracy are affected. Based on the Unet algorithm, in this paper we adjust the number of input and output channels in the convolutional layer to improve the speed during the feature extraction part. In addition, manually generated datasets are used to greatly improve the robustness of the model.

At present, the development of autonomous driving technology is still immature, and there is still a long way until fully driverless vehicles. Therefore, the state of the driver is still an important factor affecting traffic safety, and it is of great significance to detect the driver’s distracted behavior. In the task of driver distracted behavior detection, some characteristics of driver behavior in the cockpit can be further utilized to improve the detection performance. Compared with general human behaviors, driving behaviors are confined to enclosed space and are far less diverse. With this in mind, we propose a sparse spatiotemporal transformer which extracts local spatiotemporal features by segmenting the video at the low level of the model, and filters out local key spatiotemporal information associated with larger attention values based on the attention map in the middle layer, so as to enhance the high-level global semantic features.

Recovering the braking energy and reusing it can significantly improve the fuel economy of hybrid electric vehicles (HEVs).The battery ability of recovering electricity limits the improvement of the regenerative braking performance. As one way to solve this problem, the technology of brake-by-wire can be adopted in the HEVs to use the recovery dynamically. The use of high-power electrical equipment, such as electromechanical brake (EMB), is working in the form of brake-by-wire. Due to the nature of EMB, there exists an obvious coupling relationship between the energy flow and brake force distribution. In this paper, a brake force distribution controller is proposed in HEV with EMB, which can maximize braking energy recovery, compared with the conventional distribution control without EMB. Meanwhile, an energy flow strategy working with the distribution controller is designed, which is less limited to the performance of the battery.

With the increasingly serious problems of environmental pollution and energy shortage, electric vehicles have a promising future. As a core component of electric vehicles, the drive motor is developing towards high power density of which remains temperature rise problems, which affects the performance, efficiency and service life of the drive motor. Liquid cooling has high energy consumption and poor reliability. The heat-pipe has excellent heat conduction and temperature uniformity capabilities. Therefore, this paper proposes a heat pipe-based drive motor heat dissipation system to make the heat-pipe act on the inside of the motor to reach a specified range of driving conditions. The drive motor can better dissipate heat through the heat-pipe. Firstly, analysis of the internal heat generation mechanism of the motor, heat transfer characteristics of the heat-pipe and the heat-pipe layout plan was established.

The acquisition of vehicle dimensions in a vehicle’s moving process has a wide application in road monitoring, transportation, vehicle model recognition and non-contact overload recognition. At present, the detection of the vehicle dimensions mostly adopts the methods of human visual inspection and tool detection, which has a low detection efficiency and difficult to replicate on a large scale. Based on the image background subtraction method, this paper proposes a vehicle dimensions detection method, which can realize real-time detection of road vehicle dimensions. This method uses an adaptive Gaussian Mixture Model (GMM) to establish a background model based on the video stream. Initially, the moving target image is obtained by the background subtraction method, and then the edge detection under the Canny operator and Hough transform circle detection are performed on the image to obtain the pixel dimension of the vehicle's outline.

An accurate and timely positioning of the vehicle is required at all times for autonomous driving. The global navigation satellite system (GNSS), even when integrated with costly inertial measurement units (IMUs), would often fail to provide high-accuracy positioning due to GNSS-challenged environments such as urban canyons. As a result, visual odometry is proposed as an effective complimentary approach. Although it’s widely recognized that visual odometry should be developed based on both visible and infrared images to address issues such as frequent changes in ambient lightening conditions, the mechanism of visible-infrared fusion is often poorly designed. This study proposes a Generative Adversarial Network (GAN) based model comprises a generator, which aims to produce a fused image combining infrared intensities and visible gradients, and a discriminator whose target is to force the fused image to retain as many details that exist mostly in visible images as possible.

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.

Adaptive Cycle Engines, where compression and expansion events do not follow a fixed sequence but rather take place depending on demand, are competitive against electric motors because of their higher power density, lower carbon footprint with current energy sources, and predicted ability to use any kind of renewable fuel. The advantage of Adaptive Cycle Engines is greater whenever the powerplant has at least two distinct operating modes: one for high output, and one for high energy economy. This paper compares the well-to-wheels CO2 emissions and pre-tax costs when operating powerplants based on Adaptive Cycle Engines and on electric motors under several scenarios: passenger car, on-road heavy-duty vehicle, and light aircraft.

A more accurate combustion model, based on Fluent simulations including the effect of flame stretching and extinction, has been added to cycle and road simulations of an Adaptive Cycle Engine (ACE), where compressions and expansions do not follow a predefined sequence. Also, engine speed data from the Argonne Downloadable Dynamometer Database is used in the road simulations in lieu of the original constant-speed model. Results show a drop in predicted steady-state brake efficiency and bmep around 15% relative to the model using a standard Wiebe function for heat release rate. Performance on road cycles is not greatly affected by the delayed combustion since the relationship between expansion mass and work is largely unchanged. Even with the refined model, future ACE-equipped vehicles are expected to be competitive with electric powertrains in pre-tax cost and overall emissions.