Wireless power transfer (WPT) offers a viable means of charging electric vehicles (EVs) whilst in a dynamic state (DWPT), mitigating issues concerning vehicle range, the size of on-board energy storage and the network distribution of static based charging systems. Such charge while driving technology has the capability to accelerate EV market penetration through increasing user convenience, reducing EV costs and increasing driving range indefinitely, dependent upon sufficient charging infrastructure. This study reviews current traction battery technologies, conductive and inductive charging processes, influential parameters specific to the dynamic charging state as well as highlighting notable work within the field of WPT charging systems. DWPT system requirements, specific to the driver, vehicle and infrastructure interaction environment are summarised and international standards highlighted to acknowledge the work that must be done within this area. It is important to recognise that the gap is not currently technological; instead, it is an implementation issue. Without necessary standardisation, system architectures cannot be developed and implemented without fear of interoperability issues between systems. For successful deployment, the technologies impact should be maximised with the minimum quantity of infrastructure and technology use, deployment scenarios and locations are discussed that have the potential to bring this to fruition.

Energy conservation has always been crucial issues faced by the development of academia and the automotive industry. Traditional cellular networks cannot meet people's needs for video, large files and entertainment. To tackle these problems, cooperatively vehicles-to-everything (V2X) downloading is a typical solution of driving experience, which is in line with the trend of automobile development. Cooperative downloading can better achieve energy efficiency, low-emission and resource sharing. Using cooperative vehicles to download files for intelligent transport systems (ITS) is attracting increasing attention. The future commercial potential is unlimited and a win-win situation is achieved. One of the key challenges in building cooperative downloading today is the provisioning of multimedia services requiring actuator algorithm, intermittent connectivity and real-time computation. This survey summarises recent efforts of the key technologies, routing protocols and incentives mechanism in cooperative downloading.

Driving style recognition plays a crucial role in eco-driving, road safety, and intelligent vehicle control. This study proposes a statistical-based recognition method to deal with driver behaviour uncertainty in driving style recognition. First, the authors extract discriminative features using the conditional kernel density function to characterise path-following behaviour. Meanwhile, the posterior probability of each selected feature is computed based on the full Bayesian theory. Second, they develop an efficient Euclidean distance-based method to recognise the path-following style for new input datasets at a low computational cost. By comparing the Euclidean distance of each pair of elements in the feature vector, then they classify driving styles into seven levels from normal to aggressive. Finally, they employ a cross-validation method to evaluate the utility of their proposed approach by comparing with a fuzzy logic (FL) method. The experiment results show that the proposed statistical-based recognition method integrating with the kernel density is more efficient and robust than the FL method.

To evaluate the performance of the advanced driver assistant systems, such as lane departure warning systems (LDWs) and lane keeping assist systems (LKAs), a deep learning model is proposed to estimate the lateral distance between the vehicle and lane boundaries. The training of a deep learning model requires a large number of label images, but the generation of label images is time consuming and boring. Therefore, an improved image quilting algorithm based on a convolutional neural network is proposed. A lot of lane and asphalt pavement images can be synthesised using fewer images of a real road scene. Moreover, an algorithm that aims to automatically generate label images using lane and asphalt pavement images to satisfy the distribution of real scenes is proposed. Experimental results showed that the generated label images can be used to train a deep learning model, and the lateral distance can be estimated with a sub-centimetre precision, which can provide an effective benchmark for the road test of LDWs, LKAs and other driving assistant systems.

This study proposes an applicable driver identification method using machine learning algorithms with driving information. The driving data are collected by a 3-axis accelerometer, which records the lateral, longitudinal and vertical accelerations. In this research, a data transformation way is developed to extract interpretable statistics features from raw 3-axis sensor data and utilise machine learning algorithms to identify drivers. To eliminate the bias caused by the sensor installation and ensure the applicability of their approach, they present a data calibration method which proves to be necessary for a comparative test. Four basic supervised classification algorithms are used to perform on the data set for comparison. To improve classification performance, they propose a multiple classifier system, which combines the outputs of several classifiers. Experimental results based on real-world data show that the proposed algorithm is effective on solving driver identification problem. Among the four basic algorithms, random forests (RFs) algorithm has the greatest performance on accuracy, recall and precision. With the proposed multiple classifier system, a greater performance can be achieved in small number of drivers’ groups. RFs algorithm takes the lead in running speed. In their experiment, ten drivers are involved and over 5,500,000 driving records per driver are collected.

A gated branch neural network (GBNN) is proposed for modelling mandatory lane changing (MLC) behaviour at the on-ramps of highways. It provides a core algorithm for an MLC suggestion system for advanced driver assistance systems (ADAS), where the main challenge is the trade-off between computational speed and prediction accuracy for both non-merge and merge events. The GBNN algorithm employs a gated branch based on correlation analysis, scaled exponential linear units activation function, and adaptive moment estimation optimiser. The algorithm has been evaluated using the real-world dataset of U.S. Highway 101 and Interstate 80 from Federal Highway Administration's Next Generation Simulation (NGSIM). Input features are extracted from NGSIM and pre-processed by standardisation and principal component analysis. TensorFlow framework and Python are used as the development platform. Results show that the proposed GBNN algorithm with the Pearson correlation method has values of 97.7%, 96.3%, and 0.990 for non-merge accuracy, merge accuracy, and receiver operating characteristic score, respectively. It outperforms other traditional binary classifiers for MLC applications, and is more light-weight than a convolutional neural network (AlexNet) of deep learning algorithm. Owing to its compact architecture, the GBNN provides high accuracy and efficiency, demonstrating promising usage as an MLC suggestion system in ADAS.

Lane detection techniques have been widely studied in the last two decades and applied in many advance driver assistance systems. However, the development of a robust lane detection system, which can deal with various road conditions and efficiently evaluate its detection results in real time, is still of great challenge. In this study, a vision-based lane detection system with dynamic integration and online evaluation is proposed. To increase the robustness of the lane detection system, the integration system dynamically processes two lane detection modules. First, a primary lane detection module is designed based on the steerable filter and Hough transform algorithm. Then, a secondary algorithm, which combines the Gaussian mixture model for image segmentation and random sample consensus for lane model fitting, will be activated when the primary algorithm encounters a low detection confidence. To detect the colour and line style of the ego lanes and evaluate the lane detection system in real time, a lane sampling and voting technique is proposed. By combining the sampling and voting system system with prior lane geometry knowledge, the evaluation system can efficiently recognise the false detections. The system works robustly in various complex situations (e.g. shadows, night, and lane missing scenarios) with a monocular camera.

In this work, a human-centred steering assist controller based on dynamic allocation of control authority between driver and automatic e-copilot has been proposed for lane keeping systems. Cooperative control between driver and steering assist controller is addressed taking into consideration human driving behaviour. The vehicle steering controller for lane keeping is designed using a driver model for representation of the conflict between the driver and the controller. The steering controller is designed employing the integrated driver-vehicle model using Takagi–Sugeno control technique coupled with Lyapunov stability tools. The proposed design is robust to longitudinal speed variations and involves a trade-off between the lane following performance and ratio of negative system interference. The proposed approach was implemented on dynamic vehicle simulator SHERPA and the results presented in this study demonstrate the effectiveness of the proposed structure for cooperative control action between human driver and the steering assistance system. Based on indices such as energies spent by driver, driver satisfaction level and contradiction level between driver and autonomous controller the proposed optimal approach shows 93.48% and 89.30% reductions in expended driver energy and contradiction levels. Further, the satisfaction level of driver increased by 67.80% while performing a lane change manoeuvre.

This study proposes a method to investigate the perceived experiences of the appearances of smart vehicles using a combination of eye-movement tracking and semantics. The experiences of 89 participants regarding smart vehicle design were explored, targeting users’ perceived experiences of smart vehicles. The semantic results demonstrate that users felt ‘dynamic’, ‘fashionable’ and ‘sophisticated’, while designers felt ‘dependable’, ‘sophisticated’ and ‘dynamic’. Additionally, the eye-movement data reveals that designers were more attracted to the grille, waistline, and hood, while users are more interested in the rear window. This indicates a clear inconsistency between designers and users in terms of the perceived styling semantics and interest areas for smart vehicles, which helps to provide the industry with valuable insight. This method can be used by smart vehicle researchers and development teams to identify the styling desires of target users in order to accurately convey findings to designers.

The problem of cooperative driving for the connected and automated vehicles (CAVs) at the non-signalised intersection is addressed in this study. The conceptions of conflict point and intersection layout are used to formulate the mathematical model of the non-signalised intersection. Based on this model, a novel cooperative control algorithm is proposed for the CAVs driving at the non-signalised intersection. In the cooperative algorithm, the high-dimensional problem of multi-CAV cooperating at multi-conflict points is expediently converted into the single-dimensional problem of searching the optimal time for current CAV to enter the intersection. Then the analytical solution based on Pontryagin's minimum principle considering constraints of vehicles is used to control the CAVs at the intersection area. In addition, the modification of the switching input is used to reduce the CAVs’ jerk. With the cooperative control algorithm, the multi-objectives are considered including guaranteeing CAV safety, alleviating traffic congestion, and improving the performance of fuel consumption. In particular, the low-computational characteristic of the proposed algorithm guarantees that each CAV can get the optimal solution quickly and effectively. Simulation results verify that the proposed algorithm is capable of achieving coordination of CAVs with the various speeds at the non-signalised intersection.

Various studies have been conducted on resisting network attacks on autonomous vehicles and vehicular networks. Invaded vehicles may cause severe damage and casualties; thus, it is essential to stop these vehicles to prevent traffic accidents and terrorist attacks. To improve traffic safety, an active collision algorithm based on trajectory planning is therefore proposed. The algorithm can be used to cause autonomous vehicles to collide with invaded vehicles at intersections. Several types of trajectory planning algorithms have been proposed for autonomous vehicles in recent years. However, a few of these algorithms consider active collisions with other vehicles. The main advantage and novelty of the proposed method are that it can be utilised to plan a suitable trajectory for active collision with invaded vehicles at intersections. This capability has rarely been discussed in the literature to date. The main contributions of this study are that the problem of active collision of autonomous vehicles at intersections is discussed and an effective active collision algorithm based on trajectory planning is proposed. The performance of the algorithm is demonstrated using simulation. The results show that the proposed algorithm is effective in enabling autonomous electric vehicles to collide with invaded vehicles at intersections.

In this study, an integrated path tracking control framework is proposed for the independent-driven autonomous electric vehicles. The proposed control scheme includes three parts: the non-linear model predictive path tracking controller, the lateral stability controller, and the optimal torque vectoring controller. Firstly, the upper bound speed limit is regulated based on the known curvature and adhesion coefficient of the road to prevent the tyre saturation. The model predictive controller generates the steering angle and the desired longitudinal force for path tracking. Simultaneously, the lateral stability controller calculates the desired yaw moment to balance the vehicle stability and motility under different situations. Finally, the optimal torque vectoring controller distributes the wheel torques to generate the desired longitudinal force and yaw moment. Three test cases are designed and verified based on a Carsim/Simulink platform to evaluate the control performance. The test results illustrate that the proposed control framework has satisfactory path tracking performance, and the desired balance between vehicle mobility and stability is achieved under different road conditions.

As a rapidly developing technology, dedicated short-range communications (DSRC) can facilitate the solution to increasingly outstanding traffic safety problems, especially for collision warning. The conventional collision warning system using DSRC is based on the kinematic model, which depends on accurate velocity and position information. However, the reliability of the system may be reduced due to low DSRC penetration rate, communication range constraint, and vehicle positioning error. In this study, a DSRC-based end of queue collision warning system is proposed without concerning position information. The warning criterion is defined according to the real traffic data rather than experimental data. According to the warning strategy, warning evaluation proves that the strategy is effective under low DSRC penetration rate condition. Despite the DSRC penetration rate, traffic and communication influential factors are also taken into consideration in the evaluation model. The warning performance under different weather conditions is analysed based on the simulation result. Comparison studies demonstrate that the proposed algorithm outperforms the variable speed limits warning strategy based on the loop detector.

To study the overall performance of the distributed drive intelligent electric vehicle (EV), a in-wheel motor drive (IWMD) vehicle is developed in this study. The configuration and 11-degrees of freedom model of IWMD EV is introduced firstly. Then, the co-simulation model of IWMD EV based on Carsim and Matlab/Simulink is established. The block design is employed for the co-simulation modelling, including the in-wheel motor model, driver model, tyre model, steering model, braking model, suspension model, aerodynamic model, and road surface model. The effectiveness and the reasonableness of the co-simulation model of IWMD EV are verified by the snake testing with on the campus road. The co-simulation model provides accuracy and reliable simulation method for the path-tracking and self-driving study of IWMD intelligent vehicle in the future.

This study presents a novel direct yaw-moment control scheme to improve the lateral stability for a four-wheel-independently actuated electric vehicle. The proposed scheme adopts a cascaded structure that consists in an upper and a lower controller. A novel sliding mode prediction controller is proposed and used in the upper controller for deriving the desired additional yaw moment for lateral stability enhancement. By using the historic and current sliding mode information for the futuristic sliding mode dynamic prediction, the synthesised control law exhibits better robustness to matched/unmatched uncertainties and significantly mitigates the chattering phenomenon. An optimisation-based torque allocation algorithm is presented in the lower controller to optimally appropriate the driving torques to each in-wheel motor based on selected criteria. The effectiveness of the proposed method is verified through simulation and hardware-in-the-loop tests, which yields better performance compared to the rule-based method.

This study describes a cooperative chassis control system that controls longitudinal motion in accordance with the yaw movement for electric vehicles. This system can be used to improve vehicle agility and stability using the integration of torque distribution unit and electronic stability control (ESC). Moreover, this system can assist drivers smoothly navigate through a curve before ESC intervention. The structure of the proposed control system is fundamentally a model following controller, thereby making the vehicle follow the desired instantaneous handling characteristics by regulating the feedforward of the cornering stiffness, state feedback of longitudinal acceleration, and front and rear drive ratios. Experiments are performed to demonstrate the effectiveness of the proposed control system. The maximum steering angle during cornering is confirmed to be significantly reduced with proper deceleration/acceleration control and adjustment of the drive torques of the front and rear axles. Moreover, trajectory tracking can be significantly improved. The proposed control strategy can be used to assist intelligent vehicles to plan a reasonable trajectory, thereby enabling these vehicles to safely and rapidly pass corners or avoid obstacles while ensuring safety.

Electric drive system with directly coupled traction motor and two-speed transmission is a good choice for electric vehicles because of the potential in energy efficiency improvement and motor size reduction. Here, a control hierarchy of the electric powertrain has been applied, which comprises an optimal shift schedule at entire driving cycles as well as torque and speed control of the traction motor at the certain shift process. Firstly, the energy-saving shift schedule is investigated by dynamic programming methods. Then, bench tests of the integrated motor-transmission system are conducted. The oscillation of driveline and unsatisfied speed regulation of the traction motor are observed in the experiment results, which will deteriorate the shift performance. Therefore, an anti-jerking robust controller is designed to attenuate the vibration of the powertrain and a robust speed controller is proposed to enhance the speed synchronisation capability of the traction motor. Simulation results indicate that the obtained shift schedule is effective to improve the energy efficiency and the robust shift process control can enhance the drive ability.

For intelligent four-wheel-drive (4WD) electric vehicle (EV), the vehicle speed can be planned and controlled for energy saving based on the slope information of road ahead. To reduce the calculation load of the optimisation algorithm, the model predictive control (MPC) method is formulated based on the time horizon in this study. Furthermore, a fast gradient method based control tool-GARMPC is used to solve the optimisation problem. First, the longitudinal dynamics model of 4WD EV based on time horizon and distance horizon is established based on the road slope information, respectively. Second, the MPC problem based on the time-discrete model is formulated and solved by GARMPC tool. For comparison, a dynamic program (DP) control method is introduced based on the distance-discrete model. Finally, the simulation is conducted under a designed road condition and a real measured road condition. The results show that the time-horizon based MPC method can significantly reduce the energy consumption compared with the proportion integration differentiation control method, which is similar to the driver's operation. Compared with the DP optimisation method, the time-based MPC method reduces the calculation time to smaller than 1 ms, which is essential for real-time application in a road vehicle.

The suddenly released torque that accumulated in the elastic drive shaft will bring torsional vibration and jerking feel at the shifting moment. A novel sliding mode observer is proposed to estimate the torque in drive shaft for a motor-transmission integrated powertrain system. Non-linear external characteristics of a driving motor and non-linear drag torque are considered in the electric powertrain system. In order to attenuate the chatting problem, the second-order super twisting sliding mode algorithm with an adaptive gain is adopted. Furthermore, a term ‘system damping’ is introduced to accelerate the estimation error convergence. The proposed estimation algorithm is tested on test rig for typical operating conditions. The results show that the torque in drive shaft can be estimated satisfactorily and the tracking error converges to 0 in a short time.

This study presents a new hybrid and electric vehicle (HEV/EV) traction motor sizing strategy, an overcurrent-tolerant prediction model is used to estimate the dynamic and thermal characteristics of a motor operating in the overcurrent region. This can be used to determine if a prospective traction motor and powertrain configuration is able to fulfil the HEV/EVs target dynamic objectives. Since the prediction model only requires minimal motor torque–speed characteristics, it can be a useful tool during the early development stages of an HEV/EV when the detailed motor parameters used in analytical models cannot be obtained. Allowing the motor to operate in the overcurrent region could downsize the traction motor used in the final HEV/EV design to one that is smaller, easier to package and likely to run in a higher efficiency region. A case study is explored where this sizing strategy is used to convert an aeroplane pushback vehicle into a series HEV and tasked with following a rigorous duty cycle. The feasibility of two HEV configurations is then analysed further. The final HEV design reduces the fuel consumption and engine emissions by up to 52% from the original internal combustion engine powered vehicle.

To improve the mechanical performance of the In-wheel switched reluctance motor (SRM) used in electric vehicles (EVs), structure design and geometrical multi-objective optimisation strategy for the In-wheel SRM were developed in this study. The design method for major parameters of the In-wheel SRM was presented by means of design specifications of the EVs. According to requirements of the EVs, four indicators of the SRM were defined to evaluate the development of the SRM and perform the optimisation. To simultaneously improve the static performance of the SRM, a novel multi-objective simultaneous optimisation function was proposed by using four weighted factors and considering sensitivity analysis of the design variables on indicators. A four-phase 16/20 In-wheel SRM with an outer rotor was designed and optimised based on the proposed multi-objective optimisation method. The influence of design variables on average torque, torque ripple, efficiency, and torque density was analysed based on a combination of finite element analysis and orthogonal experiment design method. The static and dynamic torque performances of the optimised SRM were evaluated and compared with those of the initial motor. The comparison results showed that the proposed multi-objective simultaneous optimisation strategy can greatly improve the static and dynamic torque performances of the SRM.

The electro–hydraulic composite braking system of the electric vehicle can effectively collect the wasted energy by the regenerative braking to improve the endurance mileage. In this study, according to the characteristic of the electro–hydraulic composite braking system, the energy flow processes are analysed, which includes the energy recovery generated by motor regenerative braking and energy consumption of the hydraulic braking system, such as hydraulic pump, brake line and brake valve. Based on this, the brake sense, energy recovery and loss are proposed as the evaluation index, and their quantitative formula are derived. Taking the brake sense and energy as the optimisation objectives, and ECE regulations as the constraints, the parameters of electro–hydraulic composite braking system are optimised-based on a multi-objective evolutionary algorithm based on decomposition (MOEA/D). The simulation results show that the electro–hydraulic composite braking system optimised by the MOEA/D algorithm can decrease the energy loss and make the driver obtain a better brake sense, which improves the comprehensive performance of the system. The research of this study can provide a certain basis for the design and optimisation of electro–hydraulic composite braking system.

Unmanned mining is one of the essential fields of mining technology at present. Therefore, optimisation with known driving speed cycle for fuel economy, emissions, driveability, and more is no longer exclusive, the optimal velocity trajectory based on road information has been studied. Given that both fuel consumption and transport time influence transport costs of mining trucks, an energy management strategy (EMS) based on velocity optimisation is proposed and illustrated on a series hybrid electric mining truck in this study. The vehicle speed and SOC are adopted as state variables. Then two-scale dynamic programming is applied to calculate optimum velocity trajectory and power distribution. Simulation results reveal that the weighting coefficients of transport time and fuel economy can be optimally distributed for the different design requirements. Compared to the results under the known driving speed cycle, the proposed EMS can enhance fuel economy by 26.59% under the guarantee of same transport time, or transport time can be reduced by 42.4% without sacrificing the fuel consumption. Therefore, the proposed velocity optimisation strategy can reduce transport costs for mining enterprises significantly.

The mining trucks with heavy loads are widely used in open-pit mines, which are usually under working conditions where the recoverable potential energy accounts for more than 1/3 of the traction energy. Therefore, it is important to study the existing energy recovery technologies suitable for mining trucks. This work presents a comparative study on the economy of a mining truck by integrating four different energy storage systems (ESS): battery, supercapacitor, hydraulic accumulator, and air tank combining with the existing internal combustion engine to form a hybrid mining truck. First, recent applications and features of the four ESSs in vehicles are discussed. Next, the configurations of four types of partial hybrid mining trucks are presented. In addition, this project compares different configurations by the total benefit over 10 years of operation. The compressed-air ESS with full capacity obtained the best benefit under medium and heavy load scenarios, which is more applicable to the actual situations. Therefore, it will be beneficial to turn the conventional electric-driving mining truck into a compressed-air hybrid.

This study researches an improved scheme of membership function optimisation (MFO) for fuzzy air–fuel ratio (AFR) control of gasoline direct injection (GDI) engines based on correspondence analysis (CA). This proportional–integral-like fuzzy knowledge-based controller (FKBC) optimised by the proposed scheme can further optimise AFR control performance while maximising conversion efficiency of the three-way catalyst to eliminate the exhaust emissions in real time. Different from the conventional experience-based membership function (MF) design method for an FKBC, the proposed MFO scheme uses CA approach and can visualise the relationship between engine step gain scenarios and designed MF patterns to precisely determine its scalar parameters for AFR regulation of GDI engines. Within this context: (i) specialised MFs for self-adaptive AFR control system of a GDI engine are designed with weight distribution; (ii) based on designed scalar parameters, the CA model with taxonomic dimensions is built for acquiring a customised MF to counter transient scenario changes more effectively; (iii) the engine controller with the proposed scheme is real time validated in a production V6 GDI engine, and its advantage in terms of engine transient control performance is further demonstrated by comparing with a benchmark controller designed based on experience.

Clean fuels are recommended for ships at berth to reduce air pollutant emissions. This study aimed to evaluate the feasibility of waste cooking oil (WCO) biodiesel application on board with regard to particle matter (PM) and polycyclic aromatic hydrocarbon (PAH) emissions. An experiment was conducted on a marine auxiliary engine for three different fuels: WCO biodiesel, formulation blends with marine gas oil (MGO) and neat MGO. Results revealed that WCO biodiesel could reduce PM and PAHs emissions. WCO exhaust also exhibited differences in PAH profile and phase distribution as compared to MGO, depending on the operation modes and the proportion of biodiesel in the formulation blends. Consequently, WCO biodiesel could dramatically reduce the total carcinogenic potencies related to PAHs of exhausts. Moreover, PAH source recognition pair ratios of tested fuels were observed to deviate from the widely accepted values. This study highlights that WCO biodiesel is a cleaner fuel for operating ship auxiliary engines with respect to PM and PAHs emissions and has the potential to moderate the severe effects of PM and PAHs on an air of coastal areas.

Vehicle delay and traffic emissions have serious influences on traffic flow operations on an arterial street. The emission factors during green and red are calibrated based on vehicle-specific power (VSP), then three optimisation models are formulated by minimising vehicle delay or traffic emissions and by considering left-turn bays and coordinated signals. The field data from Dalian city of China are used to validate these models. Vehicle compositions include car, medium-size vehicle (MSV) and bus. Carbon monoxide (CO), hydrocarbons (HC) and nitrogen oxides (NO _x ) emissions are estimated. Three coordinated signal control scenarios are proposed and four simulation experiments are carried out. The results indicate that the emission factor during green is greater than that during red for CO, HC or NO _x produced by car, MSV or bus in each lane group on an arterial street; the emissions estimated by the motor vehicle emission simulator model are greater than those calculated by the VSP-based model; and the signal control scenario obtained by minimising total emissions performs better than that obtained by minimising total delay or both of them.