Proceedings of the Institution of Mechanical Engineers,Part D: Journal of Automobile Engineering

Lithium-ion batteries are facing difficulties in an aspect of protection towards battery thermal safety issues which leads to performance degradation or thermal runaway. To negate these issues an effective battery thermal management system is absolute pre-requisite to safeguard the lithium-ion batteries. In this context to support the future endeavours and to improvise battery thermal management system (BTMS) design and its operation the article reveals on three aspects through the analysis of scientific literatures. First, this paper collates the present research progress and status of various battery management strategies employed to lithium-ion batteries. Further, to promote stable and efficient BTMS operation as an initiation the extensive attention is paid towards roles of BTMS electronic control unit and also presented the essential functionality need to consider for designing best BTMS control strategy. Finally, elucidates the various unconventional assessment tools can be employed to recognize the suitable thermal management technique and also for establish optimum BTMS operation based on requirements. From the experience of this article additionally delivers some of the research gaps identified and the essential areas need to focus for the development of superior lithium-ion BTMS technology. All the contents reveal in this article will hopefully assist to the design commercially suitable effective BTMS technology especially for electro-mobility application.

This paper describes a dynamic trajectory planning method for lane-changing maneuver of connected and automated vehicles (CAVs). The proposed dynamic lane-changing trajectory planning (DLTP) model adopts vehicle-to-vehicle (V2V) communication to generate an automated lane-changing maneuver with avoiding potential collisions and rollovers during the lane-changing process. The novelty of this method is that the DLTP model combines a detailed velocity planning strategy and considers more complete driving environment information. Besides, a lane-changing safety monitoring algorithm and a lane-changing starting-point determination algorithm are presented to guarantee the lane-changing safety, efficiency and stability of automated vehicles. Moreover, a trajectory-tracking controller based on model predictive control (MPC) is introduced to make the automated vehicle travel along the reference trajectory. The field traffic data from NGSIM are selected as the target dataset to simulate a real-world lane-changing driving environment. The simulations are performed in CarSim-Simulink platform and the experimental results show that the proposed method is effective for lane-changing maneuver.

This paper proposes a new approach of using reinforcement learning (RL) to train an agent to perform the task of vehicle following with human driving characteristics. We refer to the ideal of inverse reinforcement learning to design the reward function of the RL model. The factors that need to be weighed in vehicle following were vectorized into reward vectors, and the reward function was defined as the inner product of the reward vector and weights. Driving data of human drivers was collected and analyzed to obtain the true reward function. The RL model was trained with the deterministic policy gradient algorithm because the state and action spaces are continuous. We adjusted the weight vector of the reward function so that the value vector of the RL model could continuously approach that of a human driver. After dozens of rounds of training, we selected the policy with the nearest value vector to that of a human driver and tested it in the PanoSim simulation environment. The results showed the desired performance for the task of an agent following the preceding vehicle safely and smoothly.

Frontal vehicle structure is of high importance through crash energy managements and crash boxes are the fundamental structural component for vehicle safety as well as after sales issues. Similar to many other vehicle components, the detail design of crash box is usually part of manufacture knowhow. However, some guide lines are always available. In this article a general procedure is introduced for designing of crash box with the aid of novel thin walled structures and according to conventional crash scenarios. The problem is followed through some basic steps. Firstly, the crash box idea is selected through a wide range of previous investigated elements and is packaged in a real bench vehicle. Then thanks to the protection provided by the new crash box on the other more expensive components (e.g. headlamp, cooling pack, etc.), the effectiveness of this element are acknowledged through the low speed offset crash. Further on the robustness of new proposed crash box is approved by high speed crash simulations. The quasi-static simulations implemented during the analyses are carried out by finite element explicit code (Abaqus) and the FE modeling and dynamic simulation through the next steps are also performed in ANSA and PAM CRASH respectively. Finally in addition to the general crash box design proposed procedure, the achieved results demonstrated that the corrugated conical thin walled tubes deforms in regular and rather stable shape under both axial and oblique loadings. They also produced a reasonable reaction force versus deformations which leads to stiff and crashworthy energy absorber in comparison to traditional rectangular and even some special models like as origami shapes, and so they could be a valuable selection for crash box implementations in passenger cars.

This work presents a numerical investigation of turbulent flow over a simplified vehicle model called the Square Back Ahmed Body (SBAB). The simulations are performed at Reynolds number 1 × 10⁵ using the k −  ω SSTSAS turbulence model. The numerical results for the standard reference model, that is, SBAB, have been validated against available experimental data and numerical simulations. The performance of a passive flow controller with four variants on the mean wake topology and the resultant drag reduction is evaluated. The four variants are; (i) straight cavity, (ii) straight cavity with rounded edges, (iii) C-shaped cavity, and (iv) tapered cavity. For a straight cavity with a depth equal to 33.33% of the body height, drag is lowered by 5.63%. With the same cavity depth, rounding the straight cavity edges reduces the drag by 10.67% owing to the streamlining and shortening of the recirculation region. For a C-shaped cavity, the amount of drag reduction increased slightly by 1% more than that off straight cavity with rounded edges, due to improvement in the base pressure distribution compared to that of the latter case. Tapering the cavity edges at an angle of 6° gave a significant drag reduction of 22.55% primarily due to a tremendous decrease in wake size. The drag reduction achieved in all the cases results from the modification in the mean wake topology induced by afterbody shape remodeling. The power spectra of the evolution of the lift coefficient over time reveal a noticeable decrement in the flow randomness with the inclusion of a cavity and its modifications, which interprets the mitigation of the chaotic nature in the wake. The cavity presence has also increased the vorticity spreading rate in the mixing layer and produced significant attenuation of Turbulent Kinetic Energy (TKE).

The mechanism of vehicle dynamics steering bifurcation has almost been confirmed. But the present steering bifurcation mechanism cannot explain the bifurcation phenomena caused by the driving torque. As a result, the vehicle coupled bifurcation analysis of the steering angle and driving torque has not been studied. Based on the five degrees of freedom (5DOF) vehicle system dynamics model with driving torque involved, the vehicle dynamics equilibriums under different driving torque and driving mode were searched by a hybrid method in this paper. The hybrid method combined the real-coded Genetic Algorithm with Quasi-Newton gradient method. According to the definition of static bifurcation of nonlinear systems, the equilibrium bifurcation of 5DOF vehicle system was confirmed. Then, the 5DOF vehicle system model was transformed into autonomous equation with the front wheel steering angle as intermediate variable. From the two aspects of constant steering angle amplitude and constant driving torque, the bifurcation diagrams of different driving mode were calculated. The vehicle coupled bifurcation characteristics of steering angle and driving torque were analyzed. The results show that the values of the driving torque will directly affect the bifurcation characteristics of vehicle dynamics system. The coupled feature of the front wheel steering angle and driving torque effect on vehicle bifurcation is obvious.

Knowledge of tire–road friction coefficient (TRFC) is valuable for autonomous vehicle control and design of active safety systems. This paper investigates TRFC estimation on the basis of longitudinal vehicle dynamics. A two-stage TRFC estimation scheme is proposed that limits the disturbances to the vehicle motion. A sequence of braking pressure pulses is designed in the first stage to identify desired minimal pulse pressure for reliable estimation of TRFC with minimal interference with the vehicle motion. This stage also provides a qualitative estimate of TRFC. In the second stage, tire normal force and slip ratio are directly calculated from the measured signals, a modified force observer based on the wheel rotational dynamics is developed for estimating the tire braking force. A constrained unscented Kalman filter (CUKF) algorithm is subsequently proposed to identify the TRFC for achieving rapid convergence and enhanced estimation accuracy. The effectiveness of the proposed methodology is evaluated through CarSim™-MATLAB/Simulink™ co-simulations considering vehicle motions on high-, medium-, and low-friction roads at different speeds. The results suggest that the proposed two-stage methodology can yield an accurate estimation of the road friction with a relatively lower effect on the vehicle speed.

A hybrid coupling scheme of parallel connection and contralateral cross-linking was proposed based on the characteristics of high centroid position and low rollover threshold on hydro-pneumatic suspension (HPS) for a seven-axle elevated platform vehicle. A dynamic model with 20 DOFs which can comprehensively reflect vehicle coupling vibration including vertical-lateral-longitudinal-transverse and the joint simulation model of Simulink and AMESim were established based on the structural characteristics of the vehicle. Furthermore, the random road surface model linking the wheels was constructed by filter shaping white noise. Based on the model of hybrid coupling HPS, the study of joint simulation and test on the whole vehicle were conducted, and improved ant colony algorithm (IACA) was applied to the multi-objective optimisation study of HPS parameter matching. The comparison between simulation and test data shows that the weighted acceleration PSD curve is in good agreement, and the relative error of weighted acceleration RMS is small, which indicated that the theoretical model was accurate. The results of simulation and testing show that the optimal parameter matching scheme output by IACA can improve vehicle ride comfort and stability to a significant extent.

In gasoline engines, the spark timing is often advanced to increase fuel economy under certain heavy load engine operating conditions. As a compromise between the risk of knock and the power output, spark timing is regulated at the boundary where a low knock probability is tolerated. Due to the stochasticity of binary knock events, it is necessary to have a large number of engine cycles for probability estimations, which can slow down the response speed of a controller to operating condition changes. To speed up the spark timing regulation and to reduce the spark timing variance, in this article, a knock probability feedforward map learning method and a spark timing control method are proposed under a unified framework. A learning method that applies the beta distribution is the key contribution of this work. The beta distribution in the map learning part is used to describe knock probabilities with uncertainties and to determine the next engine operating condition for sampling and map learning. In the spark timing method, the beta distribution is applied in the conventional control method to adjust the control gains. The proposed methods are experimentally validated on a test bench equipped with a production Toyota 1.8 L, 4-cylinder SI engine.

The use of optimization methods in engineering is growing, allowing the best possible way to fulfill the requirements of the project. For vehicle suspensions, there are various conditions, which involve comfort, safety, stability, maneuverability, among others. A safety and stability evaluation is carried out by several tests, including Double Lane Change. In this maneuver, the vehicle must change lanes quickly twice, allowing it to be assessed for stability in sudden movements. For ride comfort, it is common for the design to be based on the vehicle’s natural vibration frequencies. In this context, this work aims to present a methodology for optimizing the suspension parameters of a vehicle, based on the natural frequencies of vibration and the simulation of a Double Lane Change maneuver. For that, it is employed vertical and lateral dynamics mathematical models, with hypotheses that allow the adequate adaptation to the represented phenomena. Finally, Particle Swarm Optimization (PSO) is used, which is a stochastic algorithm, based on nature. It has low computational cost, with reasonable results, allowing the parameters to be estimated and comprising the two objectives simultaneously.

A novel integrated multi-physics assessment of the piston top compression ring of an internal combustion engine under normal operation mode, as well as subjected to cylinder deactivation is carried out. The methodology comprises ring-liner thermo-mixed hydrodynamics, elastodynamics of ring, as well as combustion gas blow-by and emissions. Therefore, the analysis provides prediction of ring-liner contact’s energy losses and gas power leakage across the piston and ring crevices, as well as the resulting emissions. Cylinder deactivation (CDA) technology reduces the unburnt fuel entering the ring-pack crevices, as well as the emissions. It is also shown that the frictional and gas leakage power losses are exacerbated under CDA by as much as 20%. Although this is much lower than the potential gains in fuel usage when using CDA. The optimisation of the piston compression ring would provide further fuel efficiency and improved emissions, an issue which has not hitherto received the attention which it deserves. The in-depth analysis has also shown that CDA reduces the predicted CO, NOx and HC emissions by nearly as much as 8.5%, 10% and 8.7%, respectively.

This paper is a study of the dynamic path planning problem of the pull-type multiple Automated Guided Vehicle (multi-AGV) complex system. First, based on research status at home and abroad, the conflict types, common planning algorithms, and task scheduling methods of different AGV complex systems are compared and analyzed. After comparing the different algorithms, the Dijkstra algorithm was selected as the path planning algorithm. Secondly, a mathematical model is set up for the shortest path of the total driving path, and a general algorithm for multi-AGV collision-free path planning based on a time window is proposed. After a thorough study of the shortcomings of traditional single-car planning and conflict resolution algorithms, a time window improvement algorithm for the planning path and the solution of the path conflict covariance is established. Experiments on VC++ software showed that the improved algorithm reduces the time of path planning and improves the punctual delivery rate of tasks. Finally, the algorithm is applied to material distribution in the OSIS workshop of a C enterprise company. It can be determined that the method is feasible in the actual production and has a certain application value by the improvement of the data before and after the comparison.

The paper presents novel analytical droplet breakup criteria, on Weber number (We)-relative turbulence intensity and We-Ohnesorge (Oh) representations, based on the critical Weber number. The We-Oh analytical criterion stressed the importance of turbulence effects on We-Oh breakup criterion at high We-Oh applications. In developing the analytical models the energy criterion for a disturbed parent droplet to disintegrate and the dual-timescale for turbulent shear in droplet dispersion were considered. The present model has an advantage over to two popular droplet breakup criterion models (Pilch-Erdman and Kolev) because the present model has the ability to support a parametric investigation of droplet breakup characteristics in turbulent flow fields. The Weber-relative turbulence intensity and We-Oh analytical breakup criteria are in good agreement with published experimental data. It is envisaged that the analytical model will be incorporated into larger CFD codes for spray simulation. The continuous research in this important area will present the next generation fuel injectors for improved fuel economy of internal combustion engines, especially high-pressure direct injection engines. The model has the potential to be applied in other natural and engineering systems.

Heavy duty vehicles, especially special vehicles, including wheel loaders and sprinklers, generally work with drastic changes in load. With the usage of a conventional hydraulic mechanical transmission, they face with these problems such as low efficiency, high fuel consumption and so forth. Some scholars focus on the research to solve these issues. However, few of them take into optimal strategies the fluctuation of speed ratio change, which can also cause a lot of problems. In this study, a novel speed regulation is proposed which cannot only solve problems above but also overcome impact caused by speed ratio change. Initially, based on the former research of the Compound Coupled Hydro-mechanical Transmission (CCHMT), the basic characteristics of CCHMT are analyzed. Besides, to solve these problems, dynamic programming algorithm is utilized to formulate basic speed regulation strategy under specific operating condition. In order to reduce the problem caused by speed ratio change, a new optimization is applied. The results indicate that the proposed DP optimal speed regulation strategy has better performance on reducing fuel consumption by up to 1.16% and 6.66% in driving cycle JN1015 and in ECE R15 working condition individually, as well as smoothing the fluctuation of speed ratio by up to 12.65% and 19.01% in those two driving cycles respectively. The processes determining the speed regulation strategy can provide a new method to formulate the control strategies of CCHMT under different operating conditions particularlly under real-world conditions.

As a part of Intelligent Transportation System (ITS), the vehicle traffic sign detection and recognition system have been paid more attention by Intelligent transportation researchers, the traffic sign detection and recognition algorithm based on convolution neural network has great advantages in expansibility and robustness, but it still has great optimization space inaccuracy, computation and storage space. In this paper, we design a multiscale feature fusion algorithm for traffic sign detection and recognition. In order to improve the accuracy of the network, the gaussian distribution characteristics are used in the loss function. The training and analysis of two neural networks with different feature scales and YOLOv3-tiny were carried out on the Tsinghua-Tencent open traffic sign dataset. The experimental results show that the detection and recognition of the targets by networks with multiple feature scales have improved significantly, and the recall and accuracy are 95.32% and 93.13% respectively. Finally, the algorithm of traffic sign detection and recognition is verified on the NVIDIA Jetson Tx2 platform and delivers 28 fps outstanding performances.

A new method for supporting ground vehicle wind tunnel models is proposed. The technique employs a centrally mounted sting connecting the front face of the vehicle, adjacent to the floor, to a fixed point further upstream. Experiments were conducted on a 1/24^th-scale model, representative of a Heavy Goods Vehicle, at a width-based Reynolds number of 2.3 × 10⁵, with detailed comparisons made to more established support methodologies. Changes to mean drag coefficients, base pressures and wake velocities are all evaluated and assessed from both time-independent and time-dependent perspectives, with a particular focus within the wake region. Results show subtle changes in drag coefficient, together with discrete modifications to the flow-field, dependent on the method adopted. Subtle differences in base pressures and wake formation are also identified, with mounting the model upstream found to demonstrate retention of many of the beneficial effects of other techniques without suffering their deficiencies. Overall, these results identify the upstream mounting methodology as a viable alternative to currently available and more well-established techniques used to facilitate wind tunnel aerodynamic interrogation.

A human-machine shared steering control is presented in this paper for tracking large-curvature path, considering uncertainties of driver’s steering characteristics. A driver-vehicle-road (DVR) model is proposed in which uncertain characteristic parameters are defined to describe the human driver’s steering behaviors in tracking large-curvature path. Then the radial basis function neural network (RBF) is used to estimate parameters of different drivers’ characteristics and to obtain the boundaries of these parameters. Parameter uncertainties of the driver’s steering characteristics and time-varying vehicle speed of the DVR model are handled with the Takagi-Sugeno (T-S) fuzzy logic. And these parameter uncertainties are considered in the design of the shared steering controller. Then based on the DVR model, a T-S fuzzy full-order dynamic compensator with D-pole assignment is designed to assist driver’s steering for tracking path with large curvature. Simulation results show that the proposed controller can provide individual levels of steering assistance in path following according to driver’s proficiency, and can improve driving comfort significantly.

Vehicle-to-TW accidents are one of the largest components of traffic accidents system in China, which always leads to severe accident consequences because the cyclists are vulnerable road users and drive at a high collision velocity. With the development of information and communication technology, automated Emergency Braking (AEB) system has been applied to modern vehicles, which can perform emergency braking automatically in dangerous situations and mitigate the consequence of accident. The purpose of this study is to propose a method to identify the mechanism that affects the effectiveness of AEB functions under real-life vehicle-to-TW traffic accident scenarios with Chinese county characteristics. Through the analysis and reconstruction for the sampling accident case-by-case, the whole process of the accident has been reproduced. The trajectory analysis program determines the accident triggering conditions and then generates the virtual physical parameters of the triggering conditions through the virtual sample generation method based on the initial sample. In parallel, the AEB effectiveness simulation program has been established. Plug in parameters of AEB system generated by the Latin hypercube sampling to the AEB effectiveness simulation program for getting the mechanism that affect the effectiveness of AEB functions. The AEB system parameters generated by Latin hypercube sampling are inserted into the AEB effectiveness simulation program to obtain the mechanism that affects the effectiveness of the AEB function. Under the multi-objective optimization problem of accident avoidance rate, technical cost and occupant comfort, the optimal parameters and multi-objective values of AEB are obtained by the NSGA-II algorithm. These results can adapt to Chinese actual traffic conditions to a certain extent, and provide a reliable basis for the research and development of AEB system in China.

Lightweight and high strength of titanium alloys makes them suitable for automotive industry. Automobiles are in high demand in the future and new materials and technologies are continuously enhancing the performance of automobiles. This research work addresses the potential use and barrier faced by the automobile industry in using titanium alloy. In this experimental study, we tried to overcome the machining challenges faced by industries to machine widely used grade–5 titanium alloy. The machining is performed under a dry machining environment due to ecological and economic concerns. The controllable machining variables and their influence on the tool wear are determined. Statistical analysis is performed to assess the impact of controllable variables. As a result, the optimal parameters setting obtained using the desirability approach, and confirmation runs carried out at optimal conditions. Moreover, a mathematical relation developed using the RSM technique to predict the response. Later on, using better modeling techniques based on artificial intelligence, a better predictive model is developed which forms the basis of cyber physical system.