This article introduces the current status and development trends of new energy vehicles in Europe, covering the European auto market, the EU’s carbon emission regulations, the new energy promotion policies of EU governments, and the new energy vehicle strategies and technical road-maps of European original equipment manufactures (OEMs). Although the long-term goals of new energy vehicles of major European OEMs are different, because they must comply with the same CO₂ emission regulations, the short-term technical road-maps are similar, that is, pure electric and plug-in hybrid vehicles go hand in hand. In terms of power batteries, European OEMs have all adopted lithium ion battery technology; In terms of pure electric powertrain system, European OEMs basically adopt the configuration of drive motor combined with single speed reducer; In terms of hybrid powertrain systems, the choice of European OEMs is based on the parallel structure, which has not only potential for optimizing energy transmission efficiency, enriching working modes, but also giving full play to the traditional advantages of European OEMs in engine and transmission technology. It is worth mentioning that dedicated hybrid transmission (DHT) technology has been successfully launched in Europe. This technology can give full play to the advantages of electrified powertrains and is forming a development trend. The EU’s strict CO₂ emission regulations are the biggest driving force to ensure the sustainable development of new energy vehicles in the next few decades. Europe’s strength in traditional automotive technology, production, and sales is also becoming a strong advantage in the development of its new energy vehicles. It is expected that in the next ten years, the share of new energy vehicles in Europe will continue to grow steadily, and new energy vehicles will dominate the European market before 2040

The proposed “Double Carbon” policy has brought a broad prospect to the development of hydrogen energy. Fuel cell, as the best way of hydrogen energy utilization, has been embracing a new round of prosperity in research field and industry, and proton exchange membrane fuel cell (PEMFC), which is maturely developed in commercial vehicles, has gained more attention. Membrane electrode assembly (MEA) and bipolar plate (BPP) are two key components of PEMFC stacks, and they directly determine the cost and performance of the stacks. The technologies of water and thermal management and cold start also play vital roles for the realization of stack performance and the promotion of practical application. This article comprehensively illustrates the impact of various technologies above on the performance, lifespan and cost of stacks, and then points out their development trend. In addition, fuel cell vehicles will be applied as buses and heavy duty trucks in near future. And the application as passenger cars put forward higher requirements on power density and cost of stack.

Lightweighting of automotive is an important measure for energy conservation and emissions
reduction with significance for sustainable development of automotive industry. This paper summarizes the
current research and future trends of automotive lightweight technology in China and in the world from three
aspects: the structural optimization, the lightweight materials, and the advanced manufacturing technology.
The review includes the basic principles and research developments of structural size optimization, shape
optimization, to pological optimization, and multidisciplinary design optimization. And it introduces the
applications of high-strength steel, aluminum alloy, magnesium alloy, plastic material, composite material;
as well as the applications of hydroform and laser welding in automotive industry. The authors think that the
lightweight technology future research area are the improvement of automotive structural optimization theory,
the multi-material integration, the lightweight components, and the systematization and integration of lightweight
technology.

The limitation of traction battery systems in performance, cost, life, and safety constitutes the bottleneck for
the diffusion of electric vehicles. This paper analyzes dozens of electric vehicles in the phase of R & D and several major
commercialized electric vehicles, identifies and reviews four key technologies for the traction battery system, the assembly
of cells into the battery, thermal management, electric energy management, and safety. Underlying these key technologies,
two fundamental academic issues are specified: 1) the generation, transfer, and removal of heat in the stacked composite
system comprising cells and heat conduction plates/flow fields; 2) the modeling, identification, and control of the battery
system comprising a multitude of non-linear, time-varying cells connected in parallel and series. Further development
tendency for traction battery systems are viewed, such as the smart cell and the integration with the grid and internet.

Life prediction and performance state estimation online are important in the life stage for each lithium-ion battery in electrical vehicles (EVs). The battery capacity and internal resistance were used to compare the model structure and the chemical meaning of eigen-parameters of two basic approaches; one is the identification of specific parameters based on the Equivalent Circuit Model (ECM) in the time domain and the other is Electrochemical Impedance Spectroscopy (EIS) analysis in the frequency domain. The results show that the common inherent conflict between the nonlinearity of batteries and the linearization of the identification algorithm constrains the development of lithium-ion battery in EV. Therefore, proposals such as aging mechanism, new life modeling approach, hardware structures and algorithm improvement are given to resolve the difficulties encountered in the implementation of battery life estimation online for lithium-ion systems.

PMSM (permanent magnet synchronous motor) drive systems has been having general used in
various industries needed high precision control due to a rapid development of microprocessors. The proper
system configuration is still complex and time consuming. To overcome such a limitation, this paper presents
a FOC (Field Oriented Control) algorithm for PMSM speed control algorithm performed in Matlab/Simulink by
using standard blocks only, which is realized in almost any DSP (Digital Signal Processing) processor by using
auto-coding tool in Matlab. STM32F4 microcontroller was employed. Simple Active Currents Reading Error
Compensator was introduced for appropriate feedback signals filtering. Precision of the signal was set to around
10 mA of current –10 Bit with an Analog-to-Digital Converter operated by three bidirectional 5-A Hall current
sensors. A PMSM sensored motor was tested in 0~2 000 r/min. The experimental step responses to desired
speeds show good dynamic and smooth performance of the entire system.

The state-of-the-art and technical trends of intelligent and connected vehicle (ICV) are illustrated.
The ICV system architecture included the value chain, technology chain and industrial chain. The four stages of
ICVs were the advanced assistance, connected assistance, cooperative automation and highly/fully automated
driving. Some key technologies of ICVs were introduced such as environmental perception, decision making,
dynamical control, human-machine copilot, V2X communication and platform, cyber security. Therefore, China
should develop the ICV industry rely on the top-down design by using the national institutional advantages
because the ICV will be an important direction of the automotive technology in the future, and the ICV
development is a great opportunity for the transformation and upgrading of China's automobile industry.

The performance and life of electric-vehicle battery-systems are affected by the temperature. A
liquid cooling/heating Battery Thermal Management (BTM) with an optimum geometric structure was designed
to keep the average battery-system temperature in the range from 20 ℃ to 45 ℃ and the temperature gradient
within 3 ℃ . According to overall system flow balancing in a BTM, cooling/heating plates with different structure
parameters were simulated to investigate cooling effects of the BTM. An infrared thermal imager monitored the
cooling/heating plate temperature rises in battery-system heating experiments. Experimental and simulation
results were shown to be the same. The results show that the structure with an inlet and an outlet on the
same side has an even flow distribution. By combining the simulation and optimization, the obtained optimum
combination of the inlet velocity and the temperature in the cooling/heating plate reduces the plate-surfacetemperature
standard-deviation to 2.61 ℃ , and makes the battery system uniformly heated.

Several countries’ governments are producing mandatory regulations of automotive engineering
to reduce CO2 emission and fuel consumption. The electric vehicles are one of the results by engineers'
special efforts. This paper gives a wider view of innovation of automotive engineering and a look into the
future. Technology trends include that 1) More Intelligent driver assistance systems can be distinguished
into safety functions, comfort functions, traffic efficiency improvement and environmental effect reduction; 2)
Body technology is determined by the used materials with competition between multi-materials, steel, and
carbon fibres; 3) Chassis technology is improved by integrated vehicle dynamics control, active suspension
components and material application; 4) Drive trains are converted into hybrid-drives with intelligent solutions
on the horizon for these hybrid gearboxes, but also for four-wheel drive systems; 5) Electronic control is
focusing on central control modules, the mobile phone for quite new applications and inventions for car
lighting.

Improving the energy-saving effect of new energy vehicles through vehicle motion planning and control has become a key research focus at home and abroad. This paper summarizes the latest research status of energy-saving planning and control technology for new energy vehicles, and analyzes the eco-routing, eco-driving, eco-charging, energy management and multi-task optimization techniques involving multiple fields above. The study found that although the current energy-saving planning and control technology for new energy vehicles has made considerable research progress, it is difficult to solve the problem in dynamic or random traffic behavior scenarios, and the integrated and collaborative optimization, which considers deeply related behaviors such as path, speed and charging, remains to be explored, and the high-value research results also need to develop from experimental verification to industrial application. This paper proposes that the future development trends of energy-saving planning and control technology for new energy vehicles include: 1) new problems considering the time-varying environment and random behaviors; 2) new algorithms using advanced prediction and efficient solutions; 3) new methods to systematically solve multi-vehicle, multi-task and multi-dimensional problems; 4) new applications that can be replicated and promoted in real scenarios. studying and solving the above problems is of great significance to achieve a higher level of energy-saving control of new energy vehicles.

Field data analyses have shown that the occupant protection in rear seats failed to keep pace with the advances in front seats likely due to their low occupancy and the lack of advanced safety technologies. This study provided a comprehensive literature review on rear seat occupant protection addressing the different needs for a diverse population, ranging from children in harness restraints to adults with a wide range of stature, age, and body shape. Based on the findings from field data analyses, experimental studies, and computational simulations, rear seat safety can be improved by properly using age-appropriate child restraints and introducing
adjustable/advanced/adaptive features into the rear seat restraint systems. However, the lack of biofidelic injury assessment tools for children, older, and/or obese occupants will be one of the major challenges for further improving the rear seat safety. The increased proportion of older and obese populations, the growth of lightweight vehicles, the popularity of smart-phone-based ride service, and the advances in active safety technology and autonomous vehicles will likely increase the significance of rear seat safety but at the same time will pose additional challenges. All these trends suggested that more efforts on optimizing rear seat restraint systems adapting to a wide range of impact conditions, occupant characteristics and sitting postures are necessary in the future.

The current international vehicle technology was introduced. This article described the passive safety technology: automotive collision safety design and vehicle structure design technology, key technology of advanced passenger restraint systems, vehicle safety devices to protect pedestrians, collision safety and security performance evaluation database platform; and the active safety technology: vehicle dynamics stability control technology, integrated chassis control technology, intelligent security auxiliary control technology, pre-warning technology based on people - Vehicle dangerous condition monitoring. A development mode of the advanced automotive security technology, was proposed. The  trends of the car-road coordinate control, intelligent highway and basic research of common technology were also preseuted.

A research progresses on the working principle, development path, application status and regulation of the autonomous emergency braking (AEB) technology were introduced to promote the car’s autonomous emergency braking technology to be safer and more efficient. The key technologies related to the
comprehensive performance of AEB system were summarized, including collision avoidance strategy, braking execution technology and front-end perception technology. The results show that AEB system can effectively avoid or mitigate collision, which can greatly improve the vehicle's active safety performance. However, AEB system can’t avoid any collision at higher vehicle speed and more complex traffic scenarios on account of low level braking execution technology and front-end perception technology. The technology focus for AEB will be comprehensive performance optimization of collision avoidance strategy in more complex traffic scenarios, the development of brake actuators based on shorter response time objectives, and the deep integration and the coordinated control of multiple active safety technologies under dangerous driving conditions.

 Investments of automotive safety technology have substantially been increased due to heavy casualty in traffic accident in China. It thereby accelerates the improvement of R&D capability of safety technology and commercialization process. The fact that the domestic-brand vehicles have achieved C-NCAP 5-star rating marks a great leap forward in terms of passive safety technology in China. The research of passive safety is further performed with regard to pedestrian protection, rear-row passenger’s protection, whiplash protection and cyclist protection. Meanwhile, the research and development of active safety, pre-crash safety and intelligent automotive network system have become the focuses. The perfect combination of high level active and passive safety technology regarding to passenger, vehicle and environment will promote the accomplishment of safety philosophy of zero crash and zero casualty. The subject establishment of state-level development and industrialization, and regulations constitution and perfection thereof will become the driving force of rapid growth of auto safety technology

Development status and trend of connected automated vehicle highway (CAVH) system are presented. The system consists of four key modules: sensing module, fusion and prediction module, planning module, and control module. The system initially starts from a level of “simple vehicle, smart road” or “smart
system” and gradually migrates to a higher-level system of “smart vehicle, smart road”, which can significantly improve transportation efficiency, traffic safety and energy consumption. The development of the CAVH system is very important for China. Accordingly, the roles and functions of government agencies in transportation planning, construction and management need be well defined to develop the CAVH system.

To promote the fuel economy level of passenger vehicle industry in China is beneficial to energy
saving and emission reduction. The Corporate Average Fuel Consumption (CAFC) of passenger vehicles
in China market was investigated based on the authority’s data and standards. The results show that the
passenger-vehicle CAFC in China market in the year of 2011 is 7.5 L/(100 km), which overall meets the target
value of Phase 2 in the "Limits of Fuel Consumption for Passenger Cars" (GB 19578-2004, 2004-09-02) of
China, but does not meet the target of Phase 3 (GB 27999-2011, 2011-12-30). The vehicle companies with
independent-brands have lower real CAFC values than those with the joint-venture-brands, but have a higher
ratio of the real value to the CAFC target standard, so they have more work to do to meet the Phase 3 standard.
Compared with domestic vehicles, imported passenger vehicles have higher CAFC real values and a higher
ratio of the real value to the target standard, which shows a severe challenge to the domestic vehicles.

Ethanol is being promoted as a renewable fuel and as a means of improving energy security. The
blends of gasoline and ethanol from 0 – 100 % ethanol were studied to evaluate their spray characteristics,
combustion performances, and particulate emissions with blends of ethanol and water with up to 40 % water
by volume being tested to research the combustion performances of different water ethanol blends and the
miscibility of water with ethanol/gasoline blends using ternary phase diagrams for gasoline, ethanol, and water.
The results show that presence of water in ethanol/gasoline mixtures is not an impediment to their use as a fuel
in gasoline direct injection (GDI) engines. Adding ethanol to gasoline increases the injected fuel volume and the
persistence of the fuel sprays, especially for a cold engine, leads to reduced mixture homogeneity, a decrease
in the combustion stability, and an increase in particulate matter emissions for a stoichiometric mixture. Adding
water to ethanol further increases injected fuel volume, but the increase in combustion duration and reduction in
combustion stability are not significant with up to 30 % water by volume.

Commercial vehicles are an important force in road transportation and a large carbon emitter. Realizing the green transformation and development of commercial vehicles is an important breakthrough in accelerating the achievement of the “Carbon-Peak and Carbon-Neutrality” target in the automotive industry. However, policy-driven and market demand have posed new challenges and requirements for the development of commercial vehicle technology, especially with the emergence of multiple technological routes for power transmissions. This paper focuses on the application scenarios of medium and heavy trucks, light and pickup trucks, and buses under different power sources such as traditional fuel, hybrid, pure electric, and hydrogen fuel cell, and analyzes the technical characteristics, product spectrum, applicability in different scenarios, and technological development trends of power transmission systems for commercial vehicles. A new prospect is put forward for the development of power transmission technologies for commercial vehicles to provide a reference for the technical path selection and technological innovation and development of commercial vehicle transmissions.

A review on the state-of-art in the world for fuel cell technology was given, which is an efficient,
clean, and new energy technology, including in China, northern America, European Union, Japan, South Korea
and so on. A comparison analysis was made in different aspects, such as the technical specifications of fuel
cell vehicles, the lifetime and the environmental adaptability of fuel cell engines, the hydrogen storage system,
the key materials, the auxiliary system of fuel cells, the demonstration of fuel cell vehicles and the infrastructure
of hydrogen refueling stations. The results show that global automobile companies are prepared for the
industrialization of fuel cell vehicles, and will enter mass production stage in 2015; while fuel cell vehicles are still
in the demonstration stage in China. The future hot points in next generation fuel cell vehicles are the cell life
extension, the system cost reduction, the hydrogen infrastructure construction, and commercial demonstration.

Electromagnetic brakes (EBs) are widely applied in commercial vehicles for their characteristics of
contactless, fast response, and simple controlling. The principle and research situation of EBs were given to
expand their application scopes and functions. The application status, the structure, the working principle and
the control method of EBs were discussed to three main EBs including the eddy current retarder, the rotary eddy
current retarder, and the self-excited retarder for commercial vehicle. The results show that the key technologies
are the external and inner characteristics of the electromagnetic brakes, the matching designs and the design
of control strategy and the controller of united braking system of both electromagnetic and friction. Technology
focus for electromagnetic braking will be the integrated system of electromagnetic brakes and frictional brakes,
and function extension of electromagnetic brake system.

Autonomous driving is one of the three major innovations in automotive industry. Deep learning is a crucial method to improve automotive intelligence due to its outstanding abilities of data fitting, feature representation and model generalization. This paper reviewed the technologies of deep neural network (DNN) for autonomous vehicles, which covered its history, main algorithms and key technical application. The historical timeline of DNN, its “Unit-Layer-Network” architecture, and two types of representative models were introduced. The training algorithms centered on back propagation (BP), labelled datasets and free-source frameworks for deep learning were summarized, followed by the introduction to computing platforms and model optimization technologies. Finally, the applications of DNN in autonomous vehicles were discussed, including object detection and semantic segmentation, hierarchical and end-to-end decision-making, longitudinal and lateral motion control. The applicable methods and future works for different key problems of DNN in autonomous vehicles were pointed out.

Authors reviewed the role of fuel additive in producing quality transportation fuels and fuel additive for optimal vehicle performance, meanwhile studied fuel additive application for advanced hybrid vehicles and direct injection engines. Transportation fuel and vehicle technology are rapidly evolving in response to regulatory and commercial efforts to assure energy supply, improve fuel economy and reduce mobile source emissions.  Along with these changes, the fuels must meet the demands for transportation and storage in a safe and efficient manner and the vehicle performance requirements to ensure acceptable operation in consumer use.  This evaluation looks at the broad class of fuel additives and considers how they can provide fuel producers with a means to readily deliver safe and effective transportation of fuel and to allow for effective operation of changing engine technologies. 

Along with the development of economy and vehicle technology, traffic accidents have some particular characteristics including the high mortality of vulnerable road users and ‘nonstandard groups’ of people, crash incompatibility, high death rate of single-vehicle accidents, and a significant number of accidents caused by drivers’ insufficient perception. The developing trends of active safety technologies and passive safety technologies in terms of each subsystem by analyzing the traffic accident data in China, Europe and the United States since 2000. The main trends of passive safety include protection on vulnerable road users, adaptive passenger protection, crash compatibility and adaptive crashworthiness. The main trends of active safety include vehicle dynamic management and intelligent driving assistant. The comprehensive safety technology integrating the active and passive safety will be an important trend for the development of future vehicles.

Safe-driving was assisted with key parameters calibrated according to the driver’s characteristics
using a developed system of vehicle collision warning and collision avoidance (CW/CA). The system defines the
inverse of time-to-collision (TTC-1) as the evaluation index with the grading warning and braking safe distance
model adopted based on hazardous level ε . A millimeter waveradar obstacle detection method was designed
with adaptive cruise control (ACC). The system configuration and control logic were designed based on a Jetta
car with the collision avoidance test and the manual / automatic interaction test implemented on dry roads. The
real car experiments show that the CW/CA system in accordance with desired TTC-1 index improves vehicle
active safety, and embodies the driver’s priority and cooperation.

Proton exchange membrane (PEM) fuel cell technology has already made tremendous advances. However,
performance, cost, and durability remain the key problems before PEM fuel cells can be successfully commercialized. This
paper is a review of current status in the study of PEM fuel cells and the existing challenges for their use in electrical vehicle
(EV) applications,basedon a survey of the published literature. In reviewing the current status, we introduce presentstate-ofthe-
art PEM fuel cell technology for EV applications and look at key technical achievements. PEM fuel cell research has made
particularly significant progress in improving performance, cost, and durability, primarily focusing on the main components of
the stack and system. Nonetheless, commercialization of fuel cell electrical vehicle (FCEV) applications is still confronted with
performance, cost, and durability hurdles, hindering the achievement of the 2010/2015 US DOE (Department of Energy) targets.
The maintenance of fuel cell vehicles as another component of their future commercializationwas also reviewed.

Durability is one of the challenges for the commercialization of fuel cell vehicles. The mechanisms and solutions
for fuel cell degradation are elucidated from the material and system point of view. In the aspect of fuel cell system, typical
operating processes are analyzed, such as driving cycles, start-stop, low load and idle conditions, in which reactant starvation,
dynamic potential scanning and local high potential have significant impacts on the fuel cell durability. Feasible strategies are also
discussed for mitigating the degradation. The current state and perspective are addressed on the durability of key material in fuel
cells, i.e., catalyst, catalyst support, proton exchange membrane, membrane electrode assembly and bipolar plate. The effective
methods to enhance the fuel cell durability should be based on both the material innovation and system improvement. Currently,
the improvement on system control strategy is a feasible way to prolong fuel cell lifetime although it has been result in a complex
system. Nevertheless, material innovation is a long term task to promote the fuel cell durability. Fuel cells with advanced durable
materials and simply system is a desirable goal for the fuel cell vehicle application.

Hybrid technology is an effective way for passenger cars to meet future regulations. Engine
performance has great influences on power performance, fuel economy and emission of hybrid cars. This paper
reviewed the state-of-art and development process of engines for hybrid passenger cars in the world, compared
and analyzed the engines’ energy-saving technology roadmaps. Four-stroke natural aspirated (NA) highexpansion
ratio gasoline engines and boosted gasoline direct injection (GDI) engines are the two mainstream
technology roadmaps of engines for conventional hybrid passenger cars. In the future, the two pathways will
evolve in parallel. The luxury hybrid passenger cars mainly use the boosted GDI engines while the economic
hybrid passenger cars mainly adopt the NA high-expansion ratio gasoline engines. Small-displacement fourstroke
gasoline engines will be the mainstream range-extender engines. Engines for hybrid passenger cars tend
to become smaller, more fuel-efficient with lower manufacturing cost.

The performances of hybrid propulsions and hybrid brakes of various electric vehicles (EVs)
significantly affect their energy efficiency and their safety. The development statuses were worldwide reviewed
for the hybrid propulsion and hybrid braking technologies from the aspects of the parameter matching and
optimization, the blending energy management, and the dynamical cooperative control to conclude and analyze
the scientific topics and generic technologies. Further researches that need to be carried out in the hybrid
propulsion and the hybrid braking to improve EV performances include the parameter matching and optimization
when vehicle dynamics considered, the construction of cyber-physical system which can provide a platform
for online management of vehicle multi-source and dual-way driving and braking energy, and the investigation
of dynamic characteristics, blended mechanisms, and cooperative control for dynamical-process of the hybrid
propulsion and the braking systems under critical driving situations.

  Contactless power transfer systems are desirable having more compact and lightweight for electric vehicles (EVs) recharging. A transformer of the system was developed according to the criteria of having high efficiency, a large air gap, and good tolerance to misalignment. The transformer uses series and parallel capacitors with rectangular cores and double-sided windings, with the size of 240 mm×300 mm×40 mm, the gap length of (70 ± 20) mm, the misalignment tolerance in the lateral direction of ± 125 mm, and the secondary mass of 4.6 kg. The characteristics of the system were studied with a charge control circuit and lead acid batteries being connected to the secondary winding. The results show that an output power of 1.5 kW and efficiency of 95% was achieved in the normal position and that the system has acompact-structure, light-weight, and satisfies the above criteria.

Diesel engine has advantages of low fuel consumption, high torque output and wide power range,
and has been widely used in transportation and engineering machinery as a power. The diesel powered
vehicles can only meet future stringent emission regulations using aftertreatment devices. This paper compared
the different vehicle emission regulations in Europe, the USA, Japan and China, and analyzed in-cylinder and
aftertreatment technical approaches to meet the emission regulations for light-duty and heavy-duty diesel
vehicles. The research status of the mainstream aftertreatment technologies like diesel oxidation catalyst (DOC),
nitrogen oxides (NOx) selective catalytic reduction (SCR), lean NOx trap (LNT) and diesel particulate filter (DPF)
was described and discussed. The future development of the diesel emission regulations and aftertreatment
technologies was prospected.

In order to explore the working characteristics of the pressure boosting valves and pressure reducing valves of the One Box Electro-Hydraulic Braking System (EHB), multi-field-coupled modeling and simulating methods of pressure boosting valves and pressure reducing valves were proposed, and results were verified by innovative testing bench. The structures and working principals of pressure boosting valves and pressure reducing valves were introduced. Each physical characteristic of pressure boosting valves and pressure reducing valves were precisely modeled. Multi-field coupling simulations, including electromagnetic field, flow field, and motion field, and experimental verifications for the pressure boosting valve were conducted. Simulations and experimental verifications were performed for the pressure reducing valve, including electric circuit, electromagnetic field, and motion field. The results show that the simulation error of the flow rate of pressure boosting valve is lower than 1.5 mL/s, the open delay response error of pressure reducing valve is smaller than 1.3 ms, and the close delay response error smaller than 0.3 ms, indicating that the proposed simulation method has a high accuracy, and providing a guidance for the control of the valves.

With the rapid advancement of China's automobile industry and the swift increase in car ownership, the challenges posed by energy crisis, environmental pollution and traffic safety have become increasingly pronounced. Automobile lightweight technology is one of the most effective solutions to these issues. This paper reviews the current research status of automobile lightweight technology, and explores its future development prospects. Three primary approaches to achieving automotive lightweighting are identified: the utilization of lightweight materials, structural design optimization and advanced manufacturing processes. Lightweight materials mainly encompass ultra-high strength steel, aluminum alloy, magnesium alloy and other metallic materials, as well as polymer materials, composite materials, and other non-metallic materials. Structural design optimization involves topology optimization, shape optimization, size optimization and multidisciplinary design optimization for both whole vehicles and individual components. Advanced manufacturing processes include welding, riveting, similar/dissimilar material joining techniques and integrated die casting and other material forming technologies. The progression of lightweight technology is of great significance to the sustainable development of China's automobile transportation industry.

Based on the National Vehicle Networking Industry Standard System Construction Guide (Intelligent Connected Vehicle) (2023 Edition), and following a thorough analysis of the intelligent connected vehicle standards system, this paper proposes a development pathway for standards in the intelligent networking domain and directions for coordinating international standard regulations, with a focus on automotive intelligence, network connectivity, automotive electronics, and comprehensive safety. Through an in-depth study of the technical system of driving automation, representative products, and application scenarios, a systematic framework and standardization route for advanced driving assistance systems (ADAS) and autonomous driving systems (ADS) have been established. By analyzing the technological and application scenarios of vehicular network communications, a standard system for network functions and applications, along with a standardization route, has been proposed. The current research status of automotive electromagnetic compatibility, electronic environmental and reliability assessment, automotive chips, and automotive electronics subfields has been reviewed, leading to the proposal of a standardization route for automotive electronics. Considering comprehensive safety, a four-dimensional safety concept for intelligent connected vehicles (ICVs) has been introduced, encompassing functional safety, anticipated functional safety, cybersecurity, and data security. The key components of each safety standard system have been discussed, and the standardization routes for these safety standards have been outlined. Finally, the paper explores the development of international standards and regulations for ICVs and the collaborative relationship between Chinese standards and international standards and regulations. In alignment with the developmental requirements of China’s intelligent connected vehicle (ICV) technology and standards, as well as the harmonization trends in international standards and regulations, this paper offers strategic insights and recommendations for the establishment of China’s ICV standard system and the development of key standards.

Accurate understanding of dynamic traffic scenarios is a crucial manifestation of the intelligence in autonomous vehicle (AV). Therefore, it is essential to validate its effectiveness through comprehensive, rational, and efficient testing and evaluation methods. To keep abreast of the research progress in test and evaluation on the cognitive capabilities of autonomous driving, this paper first delves the core issues existing in the field of AV test from macro, meso and micro perspectives. It explores in depth the cognitive correlations between AV and human driver. Secondly, based on the “pyramid” model architecture for AV test, it comprehensively reviews the latest research findings in key test scenario generation, virtual simulation test, hybrid virtual-real test, real-road test and cognitive capability evaluation. Finally, it highlights the challenges faced in the field of test and evaluation for AV cognitive capabilities and outlines future development trends. This comprehensive review will provide an important reference for the iterative evolution and functional validation of AV technology.

Safety has always been a critical concern in road transportation, serving as a fundamental pillar for ensuring traffic efficiency and supporting economic development. Driving risk monitoring and intervention are key technologies for enhancing vehicle safety, particularly with advancements in perception and information technology, which provide a robust data foundation and new avenues for implementation. This paper systematically reviews the research progress of driving risk monitoring and intervention techniques. Firstly, it examines the current state of driving risk monitoring from both of in-vehicle and external perspectives. Secondly, it reviews intervention strategies from both offline and online approaches. Studies have shown that interventions integrating visual, auditory, and haptic feedback can significantly improve driver response times, while haptic warning systems can help reduce the rate of driver errors. Then it is explored that the integration of risk monitoring and intervention technologies into Advanced Driver Assistance Systems (ADAS), autonomous driving systems, connected vehicle systems, and automated driving platforms. Studies have shown that intelligent systems based on vehicle-road-cloud collaboration can improve the real-time performance of risk warnings. The application of ADAS has been proven effective in reducing traffic accident rates and lowering Usage-Based Insurance (UBI) loss ratios. Finally, future research directions are discussed, including model optimization for lightweight deployment, big data applications, cloud-based control platforms, and the role of large-scale autonomous driving models in advancing risk monitoring and intervention technologies.

Facing to the background of global efforts to reduce carbon emissions and transition toward low- and zero-carbon energy systems in response to climate change, ammonia-hydrogen internal combustion engines have emerged as a promising and increasingly studied solution in the transportation sector due to their potential for zero carbon emissions. Ammonia offers several advantages as a fuel, including high hydrogen energy density, ease of storage and transport, and excellent anti-knock properties. However, its inherently slow combustion characteristics and nitrogen-containing nature bring significant challenges, particularly in terms of high nitrogen oxide (NO_x), unburned ammonia (NH₃), and nitrous oxide (N₂O) emissions. Optimizing the combustion process of ammonia-hydrogen fuels and achieving near-zero emissions in internal combustion engines (ICEs) are relatively new research areas that presents formidable technical challenges.

This paper reviews the latest research developments in the emission characteristics and near-zero emission control strategies for ammonia-hydrogen fueled ICEs. First, in terms of emission mechanisms, NO_x formation during ammonia combustion is governed by complex pathways and is highly sensitive to equivalence ratio, pressure, and temperature. Earlier mechanistic studies focused primarily on low-pressure and medium-to-high temperature conditions, which differ significantly from the high-pressure, high-temperature environments of modern engines, highlighting a current gap in research. Second, in-cylinder pollutant formation and control remain key to emission reduction. In-cylinder control techniques, including optimization of fuel injection strategies, ignition timing, and intake conditions can effectively balance the relationship between emissions and thermal efficiency. Studies have shown that hydrogen enrichment can improve combustion efficiency and reduce NH₃ and N₂O emissions, though it may increase NO_x formation. Lastly, aftertreatment technologies are critical to achieving near-zero emissions. Due to the unique characteristic of emissions from ammonia-hydrogen combustion, new dedicated aftertreatment systems are required. These include selective catalytic reduction (SCR) for NO_x, ammonia slip catalysts (ASC), and strategies for addressing high global warming potential gases such as N₂O. Additionally, hydrogen-selective catalytic reduction (H₂-SCR) offers a novel pathway for mitigating hydrogen-related emissions in such engines. Future researches should focus on the synergistic optimization of in-cylinder combustion and specific aftertreatment systems, the development of low-temperature, high-efficiency catalysts, and the exploration of integrated aftertreatment solutions to meet increasingly stringent emission regulations and approach near-zero emissions. While ammonia-hydrogen dual-fuel ICEs hold significant promise in achieving carbon neutrality, their widespread adoption will require overcoming several technical challenges, particularly in emission control.

To investigate the impact of occupant size differences and mutual interactions on the far-side occupant in side pole collisions involving electric vehicles, this study used a 5th percentile female as the near-side occupant and a 50th percentile male as the far-side occupant, and constructed various simulation scenarios by altering the collision angle and position. A linear fitting method was employed to numerically analyze the kinematic responses and head and neck injuries of the far-side occupant under different collision conditions. The results show that with the collision angle increasing, the lateral displacement of the far-side occupant increases, the restraining effect of the seatbelt weakens, and the occupant is more likely to collide with the near-side occupant or themselves. When the collision angle exceeds 45°, the HIC₁₅ predicted AIS 3+ injury risk surpasses 50%. The Head Injury Criteria (HIP) values indicate that, in all cases, the head absorbs a significant amount of energy, suggesting a high risk of AIS 3+ traumatic brain injury for the far-side occupant. Neck anterior longitudinal ligament (ALL) injuries predominantly occur in high-angle collisions and are correlated with the collision angle. Additionally, the posterior longitudinal ligament (PLL), capsular ligament (CL), and interspinous ligament (ISL) show a significant risk of neck ligament injuries in almost all cases.

To address the rapidly growing charging demands of new energy vehicles, mobile charging robots integrated with photovoltaic energy storage and charging systems have emerged as a crucial direction in research and development. This paper outlines the necessity and significance of developing photovoltaic energy storage systems and mobile charging robots for new energy vehicles, along with their fundamental operational modes. It presents the structural framework and core advantages of the photovoltaic energy storage and charging system, as well as the classification and scenario-specific adaptability of mobile charging robots. Furthermore, the economic viability, safety, and reliability of photovoltaic energy storage and charging mobile robots are analyzed. The study reviews the current research status of three key technologies—autonomous charging, path planning, and charging port recognition and insertion—and evaluates their respective strengths and limitations. This paper also summarizes the development of a new system for application-oriented research on photovoltaic energy storage and mobile charging robots, along with its key enabling technologies, and explores various specialized application scenarios. Finally, the paper identifies the challenges faced by photovoltaic energy storage and charging technologies in areas such as energy transmission efficiency, safety and stability, dynamic programming, charging port identification and insertion, advanced energy storage solutions, and the expansion of application domains. It also provides insights into the future development trends of mobile charging robots.

This study explored the variation patterns and mutual influences of the characteristic indicators of drivers' stress responses under different emergency situations. From two aspects, namely physiology and operational behavior, based on road traffic accident data, 12 types of typical road accident emergency scenarios were designed, with 35 drivers being invited to conduct driving simulator experiments. The Kruskal-Wallis non-parametric test method was used to screen out 6 indicators representing drivers' stress response characteristics. The variation patterns of these indicators were analyzed from three dimensions: the scene complexity and the driving speed, the degree of perception of emergency situations, and the scene factors. The results show that drivers are more vigilant, and emergency situations have a greater psychological impact when experiencing complex traffic environments and high driving speeds. Drivers are the most nervous, with the highest psychological load, and they respond more quickly and behave more intensely when an emergency occurs in urban roads with more complex traffic environments. When experiencing a side collision scenario where the degree of perception of the emergency situation is lower, the “heart rate growth rate” of drivers is 9.8%, which is the highest increase, indicating that they are most strongly affected psychologically. The indicator “perception-operation time” is the longest, and the indicators “time of occurrence of maximum braking acceleration” and “half-speed time” occur the earliest. There are also differences in some of the characteristic indicators of drivers' stress responses when experiencing scenarios with similar emergency situations, due to differences in specific factors within the scenarios.

The peak section force (F_max) of battery modules of an electric vehicle exceeded the safety range under side pole collision condition. The parameter optimization of the sill beam section was carried out to improve battery collision safety and achieve weight reduction of the vehicle body. 26 thicknesses or position parameters were selected as optimization variables to reduce F_max and the mass of sill beam. The maximum compression deformation(d_max) and plastic strain(ε_pmax)of battery modules were chosen as constrains. Firstly, the optimal Latin hypercube method was employed to generate samples. A fully connected neural network was established as approximation model based on samples, and the non-dominated sorting genetic algorithms-Ⅱ(NSGA-Ⅱ) was employed for multi-objective optimization. Finally, optimization results were verified through simulation. The results show that the F_max of battery modules is decreased from 21.8 kN to less than 20 kN, indicating safety requirement is eventually satisfied. Meanwhile, the mass of sill beam is reduced by 1.41%~4.02%, which means lightweight design is also achieved. Further analysis shows that d_max and ε_pmax of battery modules are also reduced synchronously in some solutions, which improves battery collision safety comprehensively in the meantime of weight reduction.

Aiming at the problem of poor trajectory tracking accuracy and handling stability of intelligent vehicles under variable speed and variable road adhesion coefficient conditions, an adaptive trajectory tracking control method based on model predictive control (MPC) was designed. Based on the lateral force sliding mode observer and the inverse model of magic tire, the tire equivalent cornering stiffness estimation method was designed to correct the dynamic model parameters in real time. A dynamic predictive time-domain control strategy that took into account the road adhesion coefficient and driving speed was developed, and an adaptive MPC trajectory tracking controller was established. The effectiveness of the adaptive model predictive control method was verified by Simulink-CarSim joint simulation under the conditions of double lane change with variable speed and road adhesion coefficient compared with the traditional MPC control method. The results show that compared with the traditional MPC control method, the control stability of the proposed method is improved at high speed and variable speed on the high adhesion coefficient road, and the average yaw speed is improved by 19.82% at a slight sacrifice of tracking accuracy. The average lateral offset and yaw velocity are reduced by 84.90% and 46.23% respectively when driving at medium and low speed on the road surface with variable adhesion coefficient, which can effectively improve the trajectory tracking control accuracy and handling stability.

To enhance the overall realism of background interactive vehicle trajectories in digital simulation scenarios for autonomous driving, this study approached the problem from both microscopic and macroscopic perspectives. Firstly, vehicle trajectory prediction models were trained on naturalistic driving data. Leveraging the characteristic that model-predicted trajectories more closely resembled real-world vehicle trajectories, the prediction served as the artificial intelligence (AI) driver model for background vehicles in simulation environments, improving the microscopic realism of simulated vehicle trajectory interactions. Building on this foundation, a measurement method for trajectory feature parameter statistical distribution differences and a corresponding optimization algorithm were designed, to re-select a single trajectory with the highest probability from multiple multi-modal prediction outputs, as the final driving trajectory for simulated vehicles, further enhancing the macroscopic realism of the generated trajectory feature parameter statistical distribution. The results show that, based on the proposed measurement metrics, the distribution difference between optimized simulated trajectories and real trajectories is reduced by 56.29% compared to pre-optimization, effectively enhancing the realism of background vehicle trajectories in simulation scenarios.

In order to ensure the safe driving of vehicles at night, the night lane lines were accurately recognized and lane departure warnings were made, a deep generative network EnhanceGAN for nighttime image enhancement and an end-to-end lane line detection network AttentiveLSTR based on Transformer were proposed for nighttime lane line detection, and experiments with real vehicles were conducted. The deep generative network EnhanceGAN used the improved UNet as the generator of the network, adopted a two-layer nested U-shape structure to expand the sensory field, and added a Markov local discriminator and a combined loss function to enhance the detailed information of lane line edges and textures. The lane line detection network AttentiveLSTR used ResNeXt as a feature extraction network to ensure the network depth and reduced the number of model parameters, and introduced feature pyramid networks (FPN) to extract lane line edge and shape information. The results show that compared with the mainstream methodsCycleGAN and Gamma Correction, the pro[osed method is more effective in nighttime image enhancement on the BDD100k dataset, with a high contrast between lane lines and surrounding environment, structural similarity (SSIM) of 0.883 4, natural and realistic images as a whole, peak signal-to-noise ratio (PSNR) of 40.265 4, and natural image quality evaluation index (NIQE) of 3.423 3; the detection accuracy (Acc) on the CULane dataset is 90.12%, and the processing speed is fast, with 82 frames per second (FPS). The research results can provide a reference for nighttime lane line deviation scenarios.

In order to ensure the safety and energy saving performance of the vehicle, it is crucial to improve the machining quality of spiral bevel gear tooth surface. An Iterative Closest Point (ICP) error compensation method optimized by dual quaternion was proposed for the measurement error of the measured and theoretical tooth surface. The error compensation problem was transformed into the matching of two surfaces. Dual quaternions were used to represent the tooth surface matching model, which helped to obtain the error matrix. The error matrix was linearized and a convex relaxation global optimization algorithm was applied to optimize the real part of the matrix. And then the precision matching of the spiral bevel gear tooth surfaces was achieved. The results show that the error compensation for the concave tooth surface reaches up to 77%. Specifically, the maximum error is reduced from 22.11 μm to 5.64 μm and the average error is reduced from 10.34 μm to 2.38 μm. Compared with the traditional Singular Value Decomposition (SVD) method, Quaternion method and Levenberg-Marquardt (L-M) method, the proposed algorithm has higher accuracy and stability, proving that the proposed compensation method is feasible.

To evaluate the potential injury risks of mechanical massage seats during vehicle rear-end collisions, this study employed the Hybrid III 50th percentile male dummy model to conduct comparative crash simulations between conventional automotive seats and mechanical mas-sage seats, with particular focus on analyzing occupant injuries to the head, neck, chest, and lumbar spine. The results showed that when using the 3ms resultant acceleration as the chest injury criterion, the values for mechanical massage seats and conventional seats are 26.6 g and 27.7 g, respectively, both meeting requirements; for the normalized neck injury criterion (N_ij), conventional seat occupants exceedes the threshold of 1, indicating significant injury risk, while mechanical massage seat occupants demonstrates excellent performance across all neck injury metrics with an N_ij value of 0.51, providing better protection; mechanical massage seats show greater advantages in reducing head injury risk, with lower HIC values for occupants; regarding lumbar injuries, the maximum force on conventional seat occupants is 1 670 N compared to 1 800 N for mechanical massage seat occupants, with the maximum LIC values being 4.32 and 3.67, respectively, both meeting safety standards and ensuring passenger safety. This research verifies the safety and reliability of mechanical massage seats in rear-end collisions, providing important reference value for future development and widespread application of mechanical massage seats.

In order to solve the problem of comfort and economy of hybrid electric vehicle (HEV) queues passing through continuous traffic signal intersections in a smart grid environment, a hierarchical control method of ecological driving and energy management based on networked HEV queue was proposed. The upper-level controller developed a target speed planning model for intersections with continuous traffic lights. Based on the defined target speed range, longitudinal constraint limits were established, and an objective function encompassing safety, comfort, following behavior, economy, and passability was formulated. The multi-objective function was solved using a model predictive control (MPC) algorithm to determine the optimal vehicle speed. Meanwhile, the lower controller adopted deep reinforcement learning (DQN) algorithm to optimize the energy management of the HEV, and took the optimal speed solved by the upper layer as the input of the lower layer to obtain the optimal output of the engine motor. The results show that the proposed control strategy can ensure the driving safety of the car queue, and the average fuel consumption of the eco-driving car queue is reduced by 8.51% compared with that of the ordinary queue, improving the ride comfort and fuel economy while avoiding the parking wait.

An automated parking evaluation tool was developed to enhance the functionality of the platform OnSite (Open Naturalistic Simulation and Testing Environment) for autonomous driving. This tool used a scenario construction method based on real vehicle data collection and modeling reconstruction. A more comprehensive test scenario library was established according to industry standards and parking space data. A multidimensional evaluation system was proposed, focusing on completion rate while considering safety, efficiency, and accuracy. The evaluation tool underwent hardware-in-the-loop simulation and was compared with results from the CARLA simulation platform and real vehicle tests. Scores of the top 10 teams in the parking test of the 2nd OnSite Autonomous Driving Algorithm Challenge were analyzed to discuss the future development of the evaluation tool and the OnSite platform. The results show that the tool provides a scientific basis for optimizing automated parking functions and serves as a reference for developing autonomous driving evaluation tools.

A functional safety concept design was conducted for smart vehicles with electronic mechanical braking (EMB) system to improve its safety and robustness. A fault injection simulation method was employed based on the ISO 26262 and combined with the product development status, to obtain the vehicle dynamic characteristics during EMB faulting, which provided the data for the assessment on severity and controllability, and effectively solved the problem of insufficient database in EMB system. The quantified severity and controllability were defined. The hazard analysis and risk assessment (HARA) were carried out, and 10 safety goals and their corresponding automotive safety integrity levels (ASIL) were achieved. The functional safety architecture and requirements of EMB system were developed with the functional safety concept design of the system being completed. Therefore, the concept analysis method can provide references for the functional safety development of other new intelligent driving electronic systems.

A six-degree-of-freedom head model was constructed with prescribed boundary conditions to investigate the impact of head kinematic characteristics on diffuse brain injuries in pole side impacts to refine related brain injury assessment criteria. This study examined the kinematic and biomechanical responses of occupants in 60 pole side impact tests and evaluated the predictive efficacy of existing brain injury metrics for diffuse injuries. The results show that 27% and 35% of the test groups fail to meet the high-performance thresholds for the DAMAGE (Diffuse Axonal Multi-Axis General Evaluation) and for the BrIC (Brain Injury Criteria), respectively. The UBrIC (Universal Brain Injury Criteria) metric shows the coefficient of determination R2 of 0.85 for the 95th percent maximum principal strain, significantly higher than other metrics. The coupling effect of Multi-axis rotational loads leads to concentrated brain tissue strain, posing a high risk for diffuse brain injuries in the pole side impact. The UBrIC is more accurate than that of other metrics in assessing this risk.

In order to improve the cooling performance of the cooling plate and solve the problem of high pressure loss, a composite X-channel liquid cooling plate structure was used to study the heat dissipation performance of lithium-ion battery. By considering the channel inclination angle, channel position, and inlet channel angle as design variables, the comprehensive cooling performance of the liquid-cooled plate was evaluated using an objective function that included average temperature, temperature standard deviation, and pressure drop. Subsequently, the optimal structural parameters of the liquid-cooled plate were determined. The heat production and temperature rise characteristics of the battery under different discharge multipliers were obtained through single-cell experiments. The thermal generation and temperature increase characteristics of the battery at various discharge rates were determined through experiments conducted on a single cell. Latin Hypercube Sampling (LHS) was employed to select 70 sample points within the design space. An approximate model, specifically Response Surface Approximation (RSA), was then utilized to establish the relationship between the design variables and the objective function. The RSA model was subsequently optimized using the Non-Dominated Sorting Genetic Algorithm Ⅱ (NSGA-Ⅱ), and the validity of the optimization outcomes was confirmed via Computational Fluid Dynamics (CFD) simulations. The results show that the pumping power of the liquid cooling plate is effectively improved, the pressure drop is reduced by 37.865%, and the overall cooling performance is increased by 55.3% compared with the initial model.