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Vehicle lateral velocity is critical for the high-level automatic driving technology, andyyfusing Global Position System (GPS) in the lateral velocity estimation method can greatly improve the estimation accuracy. However, the method fusing GPS is seldom reported, since the problem of GPS-outage often appears. Accordingly, this article proposes a novel lateral velocity estimation method (VLVEM) based on SBI-LSTM in GPS-outage environment. Additionally, VLVEM integrating Inertial Navigation System-aided GPS (INS-aided GPS) is derived from Federated Kalman Filter (FKF) algorithm. Furthermore, during GPS-outage, induced from the Stack Bidirectional Long Short-Term Memory Recurrent Neural Network (SBI-LSTM RNN), an INS-aided GPS fault reconstructor (IGFR) is designed to reconstruct INS-aided GPS model. Finally, the simulation results show that compared with most of the existing methods which only consider the in-vehicle sensors’ signals, the proposed method fusing GPS has higher lateral velocity estimation accuracy. Besides, when GPS-outage causes INS-aided GPS failure, IGFR can reconstruct INS-aided GPS model, and VLVEM still has high estimation accuracy. Combining the in-vehicle sensors and GPS, VLVEM exhibits great robustness and fault tolerance.

Recently, inferring lane change intention has received considerable attention. Due to the high nonlinearity and complexity of traffic contexts, traditional methods cannot satisfy the requirements of long-term prediction tasks and lack the ability of capturing nonlinear temporal dependencies. This paper proposes an intention inference model based on Recurrent Neural Networks (RNN), to tackle time series prediction problems. Considering dynamic interaction among surrounding vehicles, our model takes the sequence motion information of surrounding vehicles as inputs and calculates the congestion of different lanes, integrated with vehicle states of the object vehicle. To illustrate the availability of the proposed RNN intention inference model, a motion planning controller considering intention was developed. A Nonlinear Model Predictive Control (NMPC) was established to planning a safe, sub-optimal path for autonomous driving vehicle under collision avoidance constraints. The experiments on the proposed model were conducted, based on two RNN structure Long-Short Term Memory (LSTM) and Generalized Recurrent Unit (GRU), by Tensorflow with NGSIM data. The motion planning controller is modeled and simulated by Carsim with Simulink for some typical scenarios. Subsequently, experimental results demonstrate that RNN achieves best performance, inferring intention with 96% accuracy, compared with other approaches.

In order to improve the real-time and computational efficiency of autonomous vehicles’ decision-making process, this paper draws on the decision-making behavior of human drivers with the motivation as the core and proposes a decision-making and planning method based on motivation and risk assessment. On the one hand, it analyzes and determines the motivations that cause the driving state to change for decision-making and planning. On the other hand, on the basis of the lateral trajectory prediction of surrounding vehicles, the longitudinal trajectory propensity prediction of different drivers is added to construct a risk assessment model that can reflect risk of the future time domain. Based on this, the motivation-based decision method is mapped into the risk assessment model, and a cost function is established to decouple the path and speed, so that the geometry and speed can be flexibly adjusted according to environmental risks. The simulation results show that the proposed method can effectively make driving behavior decisions and plan the trajectory in real time according to the current environment, which can improve the computational efficiency of the decision-making process and guarantee the safety at the same time.

Random network delay will introduce uncertainty into trajectory tracking model of the autonomous vehicle, which seriously deteriorates the vehicle's control system stability and trajectory tracking accuracy. In this paper, considering steering angle oscillation caused by random network delay, trajectory tracking system robustness and stability is analyzed and a linear uncertain time-delay system is established. Comprehensively considering control system accuracy, robustness, and computational efficiency in the rolling optimization of Model Predictive Control (MPC), Adaptive Model Predictive Control for Uncertain model (UM-AMPC) algorithm is proposed to predict control variables for the next sampling time and alleviate the target angle discontinuity. This is achieved by operating target angle signal and augmented state variables, which are received by the lower nodes during the period from the current sampling time to network delay upper bound. The hardware-in-the-loop simulation results show that the proposed algorithm can effectively guarantee system stability and tracking accuracy of the autonomous vehicle under random network delay.

To improve vehicle side crashworthiness, this paper first introduces the negative Poisson’s ratio structure to the traditional B-pillar and proposes a negative Poisson’s ratio B-pillar. Then, the performance of the negative Poisson’s ratio B-pillar is studied in detail by comparison with a traditional B-pillar and honeycomb B-pillar. Aiming at the problem that the side crashworthiness is also significantly affected by the side structure parameters of vehicle body, the factorial analysis theory is adopted to screen out the side structure parameters with significant effect. Based on this, by combining the optimal Latin hypercube design and response surface model, a multi-objective optimization design is conducted for those structure parameters based on non-dominated sorting genetic algorithm II. Finally, the normal boundary intersection method is adopted to seek the Pareto optimal solution, and the simulation results show that compared with the traditional B-pillar, the negative Poisson’s ratio B-pillar optimized by non-dominated sorting genetic algorithm II has better comprehensive crashworthiness. The results of this paper can provide some basis for the design and optimization of vehicle side crashworthiness.

Traditional crash box is unable to efficiently solve the problem of bending deformation during the collision process, which limits the energy absorption performance and crashworthiness. This paper introduces the structure of cactus into the design of crash box and attempts to redesign a new one with stable folding deformation. By imitating the cactus characteristic, the bionic crash box consists of two parts: one is the corrugated angular structure, and the other is its thickness functionally gradient distribution along the axial direction. Based on the sensitivity analysis, the parameters which have great influences on the energy absorption performance are selected as the design variables. The multi-objective optimization design is conducted based on response surface model and Latin hypercube design of experiment. Simulation results show that the bionic crash box can effectively weaken the damage to the autobody and improve the energy absorption performance through stable folding deformation.

The aim of this paper is to explore a novel bumper system consisting of a negative Poisson's ratio (NPR) beam and a NPR absorber. The NPR beam is designed to improve the frontal crashworthiness of the vehicle, which is composed of a large number of inner hexagonal cellular structures through periodic arrangement. Meanwhile, the NPR absorber, filled in the space between the bumper beam and the skin, is contrived to protect the pedestrian's lower leg in the collision accident. Based on this, an optimization model is constructed and the multi-objective optimization algorithms are used to search the optimal structural parameters. The simulation results show that the comprehensive performance index of the NPR bumper system optimized by electronic-search algorithm (ESA) algorithm has been effectively improved, and it performs excellent performances in both pedestrian protection and vehicle crashworthiness.

Comparing with traditional honeycomb structures, Negative Poisson's Ratio (NPR) structures had better mechanical performances in some certain respects, especially the shear modulus and fracture toughness. However, few publications focused on the cylinder-shape NPR structure, which influence the diversity and possibility of NPR structure applications. In this paper, a cylindrical NPR structure was introduced and applied as a suspension jounce bumper in order to solve the issue that the ideal uniaxial compression load-displacement curve sometimes cannot be realized by traditional Polyurethane (PU) jounce bumper. The load-displacement curve of NPR jounce bumper was proved to be smoother and more ideal than that of traditional jounce bumper. Nevertheless, the influences of NPR jounce bumper on the suspension mechanical performance and vehicle ride comfort were not comprehended yet. In this study, the traditional and NPR jounce bumpers were both assembled into virtual prototypes of Macpherson, double wishbone and multi-link suspensions to conduct single wheel travel virtual tests. The results indicated that NPR jounce bumper can achieve more ideal wheel force vs. jounce height curve without adjusting free travel, which is beneficial to spare precise suspension space. Furthermore, a jounce bumper evaluation method using pulse ride comfort was proposed in this paper. The virtual ride comfort tests of travelling through bump and pothole were conducted using established vehicle virtual prototype. The maximum vertical accelerations and weighted root mean square (RMS) of acceleration of vehicle centroid at most speeds were reduced applying NPR jounce bumper. Thus, the NPR jounce bumper can apparently improve vehicle ride comfort.

Blast shock wave is a big threat to the military vehicle and may cause vehicle damages and passenger casualties. It is a big issue to balance the protective performance and lightweight property of military vehicle. In these days, many types of sandwich panels with different cores were applied as protective plates in military vehicles. In view of the excellent energy absorption capability of auxetic structure, applying auxetic structure as sandwich panel core for blast protection purpose is a promising field. However, existing in-plane configuration of two-dimensional auxetic honeycomb core always represent anisotropic behaviour. In this paper, a novel sandwich panel with three-dimensional double-V Auxetic (DVA) structure core, which can produce isotropic mechanical behavior, was proposed for air blast protection purpose. Its primary structural parameters and their relations were discussed, and a parametric numerical model was established. In order to reduce both the dynamic response under air blast loading and mass, the design variables and objective function of the DVA core optimization problem were confirmed, then parametric optimization was conducted based on Latin Hypercube Sampling (LHD) method, Gaussian process metamodel (GPM) and multi-objective particle swarm optimization algorithm (MOPSO). Finally, it is proved that the applied optimization process is efficient and can obtain accurate optimum points. The proposed DAV core sandwich panel performed much better both in lightweight and protection than solid plate and has an apparent merit in lightweight aspect compared with square honeycomb core sandwich panel.

A rollover dynamic model that merges the active front steering model and differential braking model is established in this paper. After analyzing and improving the existing rollover evaluation method, a new evaluation method that takes both sprung mass and under-sprung mass into consideration is proposed. The reliability of the improved LTR (lateral load transfer rate) is confirmed by simulation results obtained from MATLAB and CARSIM where, all of three evaluation methods are taken under the same condition. The accuracy of the rollover evaluation index depends on the centroid height of under-sprung mass and the ratio of under-sprung mass and under-sprung mass. In order to achieve the desired tracking effect and anti-jamming capability, an integrated rollover control system based on active steering and differential braking is designed where a H∞ controller is adopted. The results of simulation under J-turn condition indicate that the control system has strong stability and robustness. When the vehicle is under the risk of rollover and reaches the setting threshold, the designed H∞ controller will actively keep the vehicle under the critical state.

A displacement and force coupling control design for active front steering (AFS) system of vehicle is proposed in this paper. In order to investigate the displacement and force characteristics of the AFS system of the vehicle, the models of AFS system, vehicle, tire as well as the driver model are introduced. Then, considering the nonlinear characteristics of the tire force and external disturbance, a robust yaw rate control method is designed by applying a steering motor to generate an active steering angle to adjust the yaw stability of the vehicle. Based on mixed H2/H∞ control, the system robustness and yaw rate tracking performance are enforced by H∞ norm constraint and the control effort is captured through H2 norm. In addition, based on the AFS system, a planetary gear set and an assist motor are both added to realize the road feeling control in this paper to dismiss the influence of extra steering angle through a compensating method. Evaluation of the overall system is accomplished by simulations and experiments under various driving condition. The simulation and experiment results show the proposed control system has excellent tracking performance and road feeling performance, which can improve the cornering stability and maneuverability of vehicle.

In order to improve the crashworthiness and energy absorption performance, this paper introduces the structural bionics to the structural design of crash box and proposes a novel structure. Taking the human tibia as a bionic object, the novel crash box is composed of a concave structure shell and an inner core filled with negative Poisson's ratio (NPR) structure material. In view of the gradient characteristics of the cancellous bone structure of the tibia, the NPR inner core is designed as a functional gradient distribution structure along the longitudinal direction of the crash box. Based on this, by combining the optimal Latin hypercube design and response surface methodology, a multi-objective optimization design is conducted for the novel crash box based on archive-based micro genetic algorithm (AMGA) and improved non-dominated sorting genetic algorithm (NSGA-II). Simulation results show that the novel crash box optimized by NSGA-II algorithm can improve the energy absorption characteristics and comprehensive crashworthiness more effectively, and make the collision process controllable and stable. The results of this paper can provide some reference for the design and optimization of the crash box.

A cylindrical negative Poisson’s ratio (CNPR) structure based on two-dimensional double-arrow negative Poisson’s ratio (NPR) structure was introduced in this paper. The CNPR structure has excellent stiffness, damping and energy absorption performances, and can be applied as spring, damper and energy absorbing components. In this study, the CNPR structure was used as a jounce bumper in vehicle suspension, and the load-displacement curve of NPR jounce bumper was discussed. Moreover, the influences of structural parameters and materials on the load-displacement curve of NPR jounce bumper were specifically researched. It came to the conclusion that only the numbers of cells and layers impact the hardening displacement of NPR jounce bumper. And all parameters significantly affect the structure stiffness at different displacement periods. On the other hand, the load-displacement curve of NPR jounce bumper should be in an ideal region which is difficult to be achieved applying mathematical optimization method. Therefore, a parametric design strategy of NPR jounce bumper was proposed according to the parametric analysis results. The design strategy had two main steps: design of hardening displacement and design of stiffness. The analysis results proved that the proposed method is reliable and is also meaningful for relevant structure design problem.

To improve the maneuverability and stability of in-wheel-motor-drive electric vehicle, a control strategy based on nonlinear decoupling control method is proposed in this paper, realizing the coordinated control of the steering and driving system. At first, the nonlinear models of the in-wheel-motor-drive electric vehicle and its sub-system are constructed. Then the inverse system decoupling theory is applied to decompose the nonlinear system into several independent subsystems, which makes it possible to realize the coordinated control of each subsystem. Next, the μ-Synthesis theory is applied to eliminate the influence of model uncertainty, improving the stability, robustness and tracking performance of in-wheel-motor-drive electric vehicle. Simulation and experiment results and numerical analyses, based on the electric vehicle actuated by in-wheel-motors, prove that the proposed control method is effective to accomplish the decoupling control of the steering and driving system in both simulation and real practice.

The crash box can absorb energy from the beam as much as possible, so as to reduce the collision damage to the front part of the car body and protect the safety of passengers. This work proposes a novel crash box filled with a three-dimensional negative Poisson’s ratio (NPR) inner core based on an inner hexagonal cellular structure. In order to optimize and improve the crash box’s energy absorption performance, the multi-objective optimization model of the NPR crash box is established, which combines the optimal Latin hypercube design method and response surface methodology. Then, the microstructure parameters are further optimized by the multi-objective particle swarm optimization algorithm to obtain an excellent energy absorption effect. The simulation results show that the proposed NPR crash box can generate smooth and controllable deformation to absorb the total energy, and it can further enhance the crashworthiness through the designed optimization algorithm.

By possessing a good capacity for energy absorption and a lightweight structure, the negative Poisson’s ratio (NPR) structure has very fine prospects for application in vehicle engineering. By combining the traditional side door impact beam and a NPR structure, a novel side door NPR impact beam is first proposed in this work to improve the side impact crashworthiness in automobiles. The performance of the side door NPR impact beam is first studied in detail by comparison with a traditional side door impact beam and aluminum-foam-filled impact beam. To make further improvement on the performance of the side door NPR impact beam, the multi-objective design optimization while considering reliability is also investigated in this work. The parametric model of the NPR structure is established to improve modeling efficiency when the shape and topology are changed. A Latin hypercube sampling technique, orthogonal design, and a response surface model are then combined to construct the surrogate models. A radial-based importance sampling technique (RBIS) and multi-objective particle swarm optimization algorithm (MOPSO) are applied in the inner and outer loop respectively to find the optimal multi-objective reliability solutions. The results indicate that the side impact crashworthiness is improved remarkably by the side door NPR impact beam and the structure is further improved by the multi-objective reliability optimization. The studies in this work also serve as a good example for other improvements in automobile performance.

The optimization design of chassis integrated system mainly involves steering, suspension and brake subsystems, which is essentially a multidisciplinary design optimization. This paper mainly researches the multidisciplinary optimization of the chassis integrated system for the electric wheel vehicle, from the view of ensuring a favorable feel for the driver. The dynamic models of differential steering system, brake system, active suspension system and vehicle are established. Then, taking the coupling relationship of the chassis subsystems into account, this paper proposes an evaluating index of driver’s ride comfort (Drc), which is composed of the steering road feel, brake feel and suspension ride comfort. In order to determine the weight coefficient in the quantization formula of Drc, the technique for order preference by similarity to ideal solution (TOPSIS) method is used to overcome the subjectivity in the selection. Based on these, a multidisciplinary hybrid hierarchical collaborative optimization (HHCO) method is proposed on the basis of the collaborative optimization (CO), which consists of a system level coordinator and a coupling analyzer to solve the problem of poor convergence and the low efficiency of CO method. The optimization results show that the proposed HHCO method has excellent computational efficiency and better convergence compared with the CO method, which can further improve the steering road feel and the drive ride comfort, on the premise of ensuring the brake feel and suspension ride comfort.

The bumper system always directly causes the injury of the pedestrian leg in the car–pedestrian accidents. Therefore, it is of practical significance to optimize the bumper structure to enhance the protective effect of the pedestrian leg. Based on the original bumper system, this work proposes a novel negative Poisson’s ratio energy-absorbing forepart with inner hexagonal cellular structure, which is designed at the lower leg impact zone between the bumper beam and the cover. The parametric model of the negative Poisson’s ratio forepart, the lower legform impactor model, and the car–pedestrian collision model are firstly established. Then, through integrating the response surface method, second-order reliability method, and archive-based micro genetic algorithm, a reliability optimization design is further conducted for the negative Poisson’s ratio forepart based on the deterministic optimization results. Simulation results show that the optimized negative Poisson’s ratio forepart not only can significantly enhance the reliability and robustness, but also improve the protective effect of the pedestrian leg.

The unique structure of negative Poisson’s ratio (NPR) materials provides a good energy absorption capacity that has found wide application in automotive and aeronautics industries. The present work proposes a novel NPR energy absorption structure installed between the bumper and anti-collision beam of automobiles for improving pedestrian lower leg protection. The performance of the proposed NPR structure based on tibia acceleration, knee bending angle, and knee shear displacement is evaluated by comparison with the performance of a conventional energy absorption structure. The performance of the proposed structure is further improved by conducting multi-objective design optimization with consideration for the perturbation induced by parameter uncertainties. For optimization, a parametric model of the NPR structure is first established based on the relationships between parameters. To promote computational efficiency, an optimal Latin hypercube sampling technique and the dual response surface method are combined to build surrogate models between responses and inputs. A multi-objective particle swarm optimization algorithm and six sigma criteria are then applied to obtain the optimal design parameters of the structure. The optimization results are validated by comparisons with the results of multi-objective deterministic optimization. The comparison results show that the proposed NPR energy absorption structure improves pedestrian lower leg protection significantly, and its performance is further improved by the proposed multi-objective robust design optimization. This study serves as a good example of the safety promotion provided by applying NPR structures with enhanced energy absorption capacity in vehicles.

Four wheel steering (4WS) technology can effectively improve the vehicle handling stability and driving safety. In order to fully consider the influence of the rear wheel steering, the vehicle dynamics model of 4WS vehicle, including the rear wheel steering by wire and two degrees of freedom vehicle model of 4WS vehicle, is established in this paper. The desired yaw rate is obtained according to the variable transmission ratio strategy. The yaw rate tracking strategy is applied to 4WS vehicle and rear wheel steering resistance moment is taken into account. Based on the robust control theory, H2/H∞ mixed robust controller design is carried to research the stability control of 4WS vehicle. Finally, the closed-loop simulation added driver model based on preview theory is carried out. The simulation results indicate that the designed H2/H∞ mixed robust controller can achieve the stability control.

Vehicle collision avoidance system is a kind of auxiliary driving system based on vehicle active safety, which can assist the driver to take the initiative to avoid obstacles under certain conditions, so as to effectively improve the driving safety of vehicle. This paper presents a collision avoidance system for an autonomous vehicle based on an active front steering, which mainly consists of a path planner and a robust tracking controller. A path planner is designed based on polynomial parameterization optimized by simulated annealing algorithm, which plans an evasive trajectory to bypass the obstacle and avoid crashes. The dynamic models of the AFS system, vehicle as well as the driver model are established, and based on these, a robust tracking controller is proposed, which controls the system to resist external disturbances and work in accordance with the planning trajectory. The proposed collision avoidance system is testified through CarSim and Simulink combined simulation platform. The simulation results show that it can effectively track the planning trajectory, and improve the steering stability and anti-interference performance of the vehicle.

Negative Poisson’s ratio (NPR) structure has outstanding performances in lightweight and energy absorption, and it can be widely applied in automotive industries. By combining the front anti-collision beam, crash box and NPR structure, a novel NPR bumper system for improving the crashworthiness is first proposed in the work. The performances of the NPR bumper system are detailed studied by comparing to traditional bumper system and aluminum foam filled bumper system. To achieve the rapid design while considering perturbation induced by parameter uncertainties, a multi-objective robust design optimization method of the NPR bumper system is also proposed. The parametric model of the bumper system is constructed by combining the full parametric model of the traditional bumper system and the parametric model of the NPR structure. Optimal Latin hypercube sampling technique and dual response surface method are combined to construct the surrogate models. The multi-objective robust optimization results of the NPR bumper system are then obtained by applying the multi-objective particle swarm optimization algorithm and six sigma criteria. The results yielded from the optimizations indicate that the energy absorption capacity is improved significantly by the NPR bumper system and its performances are further optimized efficiently by the multi-objective robust design optimization method.

A control strategy based on LQG/LTR theory for steer-by-wire (SBW) system is proposed in this paper. Firstly, the models of the SBW system and the whole vehicle are constructed. Secondly, the control strategy of LQG for SBW system is proposed, in which the LTR is utilized to eliminate the effect from the Kalman filter. Thirdly, simulations based on the co-simulation platform of MATLAB/Simulink and Carsim are performed with the proposed control strategy to identify its performance. At last, field experiments are conducted to further verify the feasibility of the proposed control strategy in real application. The simulation and experiment results indicate that the proposed control strategy has good stability, robustness and feasibility in real application, and is more effective in practical application of SBW system.

The uncertainty influences may result in performance deterioration and instability to the steer by wire (SBW) system. Thus, it must make the control system keep robust stability from uncertainty, and have good robustness. In order to effectively restrain the interference and improve steering stability, this paper presents a μ synthesis robust controller based on SBW system, which considers the effect of model uncertainty and external disturbance on the system dynamics. Taking the ideal yaw rate tracking, interference suppression and excellent robustness as the control objectives, the μ synthesis robust controller is designed using linear fractional transformation theory to deal with the uncertainty. Then, it is testified through time domain and robustness simulation analysis. Simulation results show that the proposed controller can not only ensure robustness and robust stability of the system quite well, but improve handling stability of the vehicle effectively. The results of this study provide certain theoretical basis for the research and application of SBW system.

To minimize the auto body’s posture change caused by steering and uneven road, and improve the vehicle’s riding comfort and handling stability, this paper presents an H ∞ robust controller of the active suspension system, which considers the effects of different steering conditions on its dynamic performance. The vehicle’s vibration in the yaw, roll, pitch and vertical direction and the suspension’s dynamic deflection in the steering process are taken into account for the designed H ∞ robust controller, and it introduces the frequency weight function to improve the riding comfort in the specific sensitive frequency bands to human body. The proposed robust controller is testified through simulation and steering wheel angle step test. The results show that the active suspension with the designed robust controller can enhance the anti-roll capability of the vehicle, inhibit the changes of the body, and improve the riding comfort of the vehicle under steering condition. The results of this study can provide certain theoretical basis for the research and application of active suspension system.

To improve the maneuverability, stability, and reliability of the steer-by-wire system, a two-way H∞ control method with a fault-tolerant module is proposed in this paper. First, a two-way H∞ control scheme is proposed. Two controllers are designed in this scheme: one is used as a feedback controller as a general practice to stabilize the system and detect the tracking error; the other is used as a feed forward controller to make the output of the system follow the driver’s steering intention rapidly and precisely. Second, a fault-tolerant module aiming at front wheel angle sensor which is an important feedback signal to the system is added to improve the reliability of the system. A revised Kalman filter is applied in the fault-tolerant module to reconfigure the front wheel angle as a reference value and a substitute when the sensor fails, thus replacing hardware redundancy by software redundancy in a cost-effective way. Lastly, simulations by Matlab/Simulink and CarSim software and hardware-in-the-loop experiments are conducted and effectiveness of the proposed control method is demonstrated by simulation and experimental results and numerical analyses.

Beam model has been used widely in computer aided engineering for mechanical systems and the simulation results largely depend on the beam model, there are great demand for beam model with high accuracy and efficiency in simulating the mechanical system with large deformation. In this work, a new model of Timoshenko beam for large deformations is proposed to improve the accuracy and efficiency. Absolute nodal coordinate formulation and Geometrically-exact beam formulation, which make the model very suitable to simulate large deformation mechanical system, are first combined in the new beam model, the governing equations are then discrete by finite element methods and numerical difficulties are well solved by applying the shape function strategy. The new beam model is then applied to simulate the mechanical systems with large deformation and the simulations based on the traditional beam model and hexahedron element were also carried out to verify the results. The results show that good precision and efficiency in simulating the mechanical systems with large deformation can be obtained by the new Timoshenko beam model at the same time. The presented new method also serves as a good foundation for other simulation of mechanical systems.

The motion trajectory optimization method for an intelligent vehicle in the planning and control of the trajectory of an intelligent vehicle under high-speed driving working conditions is difficult to research. In order to ensure that the intelligent vehicle runs steadily under high-speed driving working conditions, an improved-genetic-algorithm-based motion trajectory-planning method was introduced to optimize the generated trajectory. The trajectory optimization method considers the influence of the dynamic constraints to ensure that the vehicle runs steadily under high-speed driving working conditions. First, a B-spline curve is used to parameterize the generated trajectory. Then, the improved genetic algorithm is used to optimize the parameters of the B-spline curve so that the parameterized trajectory satisfies the dynamic constraints and so that the optimized trajectory is feasible. The simulation results showed that the optimized trajectory satisfies the dynamic constraints better, that the optimized effect is effective and that the vehicle steers steadily under high-speed driving working conditions.

Possessing the unique properties of lower mass and higher performances, the structure with Negative Poisson's Ratio (NPR) can be widely used in aerospace and vehicle industry. By combing the NPR structure filled core and the traditional crash box, a novel NPR crash box is first proposed in this work to improve the performances of the crash box. The performances of the novel NPR crash box are fully studied by comparing to the traditional crash box and the aluminum foam filled crash box. A parameterized model of the NPR crash box, which integrates the design parameters of the basic NPR cell structure, is built to improve the analysis and optimization efficiency, the accuracy of the parameterized model is also verified by comparing to traditional FEM model. Multi-objective optimization model of the NPR crash box is established by combining the parameterized model, optimal Latin square design method and response surface model approach. Non-dominated sorting genetic algorithm-II (NSGA-II) is then applied to optimize the design parameters of the basic NPR cell structure to improve the performances of the NPR crash box. The results indicate that the novel NPR crash box can improve the performances of the crash box remarkably and the combination of parameterized model and multi-objective genetic algorithms optimize the NPR crash box efficiently. The presented new method also serves as a good example for other application and optimization of NPR structure.

A new straightforward method for residual stress measurement is presented. A specimen is cut in half along its diagonal by using electric discharge machining, and the deformation benchmarks are measured to determine the displacements caused by release of the residual stresses. Analytically, to verify calculation accuracy of the new method, the known stresses are applied in finite element model as an initial condition. By using the series expansion approach, the inverse calculation based on displacements gives the original longitudinal residual stress of the specimen. This “wedge method” is more straightforward than other relaxation methods because it measures fewer data. Its data processing is simpler, yet its result is found to be in good agreement with other methods. The new method is first outlined for a three-dimensional (3-D) body and the accuracy of the methodology is demonstrated for a representative case using a numerical experiment. Then measurements are performed on a block specimen of AA6061-T6 to show that the approach is valid under real experimental conditions.

Autonomous driving has been one of the key factors behind the various technology innovation initiatives in the automotive industry in recent years. A unified driver model controller, which is essential to the design and development of autonomous driving technology, based on fractional-order PIλDμ and internal model control is proposed and studied in this paper. The unified driver model control algorithm utilizes and integrates fractional-order PIλDμ control and internal model control to ensure path tracking capability and driving stability of the vehicle. Matlab/Simulink simulation of the proposed controller indicates that it can effectively improve path tracking capabilities and driving stability.

Anti-collision beam of front door has significant influence on occupant protection and side crashworthiness. In order to improve the comprehensive side impact performance of vehicle, this work proposes a novel negative Poisson's ratio (NPR) door anti-collision beam based on star-shaped cellular structural material, and optimizes it by a reliability-based optimization scheme. First, the parametric model of NPR anti-collision beam, vehicle model assembling NPR beam, occupant restraint system model and side collision model are established. Then, the basic mechanical properties of the NPR structure are investigated under considering the axial normal force, transverse shear force and bending moment of each rod, and the macro performance of NPR beam are analyzed. Next, a deterministic optimization integrating optimal Latin hypercube sampling (OLHS) method, response surface model (RSM) and NSGA-II algorithm is conducted to improve the performance of novel NPR beam. Finally, considering the uncertainties of design variables, a reliability-based optimization is further executed to enhance the robustness of the deterministic optimization by using Design for Six Sigma (DFSS) method. Simulation results show that the ultimate optimized NPR anti-collision beam can not only ameliorate driver's protection and side crashworthiness, but also ensure the reliability and lightweight effect.

Experimental investigations were conducted on a diesel engine to evaluate the effects of hydrogen addition on engine combustion, unregulated gaseous emissions and particulate emissions based on the Japanese 13-mode testing cycle. A diesel–hydrogen co-operated combustion strategy is proposed in which hydrogen is naturally aspirated into the diesel engine to substitute 10, 20 and 30% of the total fuel energy at 10 selected modes and diesel is used as the sole fuel for the other 3 modes. Gradual increase of peak in-cylinder pressure and heat release rate is observed at medium and high engine loads. Pre-ignition might occur at high engine load and speed. Cyclic variation of the indicated mean effective pressure and maximum pressure derivative increased with hydrogen addition. The brake specific emissions of acetaldehyde (CH3CHO), alkenes (C2H4, C3H6 and C4H6), arenes (C6H6, C7H8 and C8H10) and particulate mass can be evidently reduced. However, with H30 operation, the weighted total HC and formaldehyde (HCHO) increased by around 27% and 8%, respectively, while NOx increased by around 17%. The simultaneous decrease of particle size and number are believed to be associated with the inhibition effect of hydrogen on soot particle formation based on the HACA (H2 abstraction and C2H2 addition) mechanism.

The dynamic models of an integrated system based on active steering and an active suspension are established; these include a vehicle model, an active steering system model, an active suspension system model, a tyre model and a road input model. The performance evaluation indices of the integrated system are proposed, including the steering road feel, the steering sensitivity and the ride comfort. On the basis of the target cascading theory, an analytical target cascading method is researched. Considering the coupling factors of the integrated system, the optimization model of the integrated system is established with the target cascading method. Then, the parameters are optimized based on a multi-island genetic algorithm, and the optimized results are compared with those of the collaborative optimization method. Thus, a theoretical foundation and technical support for the integration design of the automotive chassis can be provided.

The differential steering system (DSS) of electric wheel vehicle gets rid of the restrictions of traditional steering system completely. As an ideal steering technology, it not only realizes the perfect combination of the road feel and the steering portability, but also realizes the harmony and unification between the steering maneuverability and safety. The structure and basic theory of the DSS of electric wheel vehicle are discussed in this paper. Based on these, the dynamic model of the steering system is built. Considering of the uncertainties and disturbances existing in the model, the H∞ mixed sensitivity control theory is applied to achieve better tracking performance and road feel in the process of steering. Then, a H∞ mixed sensitivity controller is designed to restrain the effect of the road disturbance and model uncertainties. The simulation results indicate that the DSS with the designed controller can effectively restrain the effect of noises and disturbances caused by random motivation from road, torque sensor measurement and model parameter uncertainty, and enable the driver to obtain satisfactory road feel.

The vehicle model of the recirculating ball-type electric power steering (EPS) system for the pure electric bus was built. According to the features of constrained optimization for multi-variable function, a multi-objective genetic algorithm (GA) was designed. Based on the model of system, the quantitative formula of the road feel, sensitivity, and operation stability of the steering were induced. Considering the road feel and sensitivity of steering as optimization objectives, and the operation stability of steering as constraint, the multi-objective GA was proposed and the system parameters were optimized. The simulation results show that the system optimized by multi-objective genetic algorithm has better road feel, steering sensibility and steering stability. The energy of steering road feel after optimization is 1.44 times larger than the one before optimization, and the energy of portability after optimization is 0.4 times larger than the one before optimization. The ground test was conducted in order to verify the feasibility of simulation results, and it is shown that the pure electric bus equipped with the recirculating ball-type EPS system can provide better road feel and better steering portability for the drivers, thus the optimization methods can provide a theoretical basis for the design and optimization of the recirculating ball-type EPS system.

Based on the multidiscipline design optimization theory, a multidiscipline collaborative optimization model of the differential steering system of electric vehicle with motorized wheels is built, with the steering economy as the main system and the steering road feel, the steering flexibility and the mechanic character of the steering sensors as the subsystems. Considering the coupled relationship of each discipline, the main system is optimized by the multi-island algorithm and the subsystems are optimized by the sequential quadratic programming algorithm. The simulation results show that the steering economy can be optimized by the collaborative optimization, and that the system can get good steering road feel, good steering flexibility and good mechanic character of the steering sensors.

A novel active steering system with force and displacement coupled control (the novel AFS system) was introduced, which has functions of both the active steering and electric power steering. Based on the model of the novel AFS system and the vehicle three-degree of freedom system, the concept and quantitative formulas of the novel AFS system steering performance were proposed. The steering road feel and steering portability were set as the optimizing targets with the steering stability and steering portability as the constraint conditions. According to the features of constrained optimization of multi-variable function, a multi-variable genetic algorithm for the system parameter optimization was designed. The simulation results show that based on parametric optimization of the multi-objective genetic algorithm, the novel AFS system can improve the steering road feel, steering portability and steering stability, thus the optimization method can provide a theoretical basis for the design and optimization of the novel AFS system.

In view of the existence of uncertainties such as system model and disturbance signal in the electric power steering (EPS) system, and the demand for system dynamic performance, the mixed H2/H∞ controller based on genetic algorithm is proposed. In order to obtain satisfactory steering feel, robust performance and steering stability, models of EPS system and a two-degree-of-freedom car are set up, then the state space model and the augmented matrixes are built. The H∞ method is introduced to minimize the effect of disturbances on the outputs, and the H2 method is applied to optimizing the system performance based on genetic algorithm. The simulation results show that the modified mixed H2/H∞ controller, which synthesizes the advantage of H2 control and H∞ control, has better robust performance and robust stability. The designed controller can attenuate the noises and disturbances caused by road random motivation, torque sensor measurement and model parameter uncertainty, enabling the driver to obtain satisfactory road feel.

The model of the differential steering system (DSS) of electric vehicle with motorized wheels and the three-degree-of-freedom dynamic model of vehicle are built. Based on these models, the concepts and quantitative expressions of steering road feel, steering portability and steering stability are proposed. Through integrating the Monte Carlo descriptive sampling, elitist non-dominated sorting genetic algorithm (NSGA-II) and Taguchi robust design method, the system parameters are optimized with steering road feel and steering portability as optimization targets, and steering stability and steering portability as constraints. The simulation results show that the system optimized based on quality engineering can improve the steering road feel, guarantee steering stability and steering portability and thus provide a theoretical basis for the design and optimization of the electric vehicle with motorized wheels system.

A novel electric power steering system (EPS) integrated with active front steering (AFS) is developed. It has functions of both AFS system and EPS system with two actuator units: the AFS actuator unit and the EPS actuator unit. The AFS actuator unit controls the displacement transfer behavior of the steering system, and improves the handling stability under adverse road conditions by varying the steering ratio directly related to the speed and road conditions. The EPS actuator unit controls the force transfer behavior of the steering system, and improves the steering portability and road feel of the vehicle. Based on a dynamic model, the mixed H2/H∞ control strategy of the EPS actuator and the active steering intervention control strategy of the AFS actuator are designed. The simulation indicates that the novel EPS system with the designed control strategies has obvious advantages in vehicle handling stability and the driver’s road feel over the traditional EPS system, and extends the vehicle’s steering performance.

Owing to the limitation of production cost, the idle shake problem is very common among minivans. To improve the performance of the steering system while keeping the cost not increased, an enhanced hybrid and adaptive meta-model based global optimisation (E-HAM) is applied in this work. Based on the HAM algorithm, the new important region update method (IRU) and different sampling size strategies are integrated in the E-HAM. The resulting precision, convergence rate and search efficiency are improved remarkably. The minivan steering system is then optimised by another six optimisation algorithms to perform the comparisons. Finite element method (FEM) analysis and experiment are also conducted to verify the effectiveness. The results show that the structures are optimised successfully by the E-HAM optimisation method and the idle shake problem is well solved. The research in this work also serves a good example for other structure optimisation problems.

To solve the problem of trajectory tracking for 4WS autonomous vehicles under high speed emergency obstacle avoidance, an improved trajectory tracking controller combining four-wheel steering (4WS) feed-forward control and linear quadratic regulator (LQR) feed-back control is proposed. First, a polynomial obstacle avoidance trajectory of small radius and sharp curve is established and its several variables are analysed. Second, in order to make actual yaw rate follow steady-state value based on reference model, the steering gain of the improved 4WS feed-forward control can change with steering angular frequency and vehicle speed. Third, combined with the LQR feed-back control, the deviations between actual lateral displacement and desired lateral displacement are controlled under 0.05 m. Finally, CarSim® simulation results show that 4WS autonomous vehicles based on the controller can not only avoid obstacles in time, but also improve active safety and handling stability under high speed emergency obstacle avoidance.

According to the operating mode of pure electric power bus, the recirculating ball-type EPS (Electric Power Steering System) is designed and its dynamic model is established by the optimum design. Because of the existence of system uncertainty model, disturbance signals, sensor noises and the demand of system dynamic performance, the mixed H∞/H2 controller is designed for the EPS. The simulation results show that the EPS with the mixed H∞/H2 controller could attenuate the random noises and disturbances and track the boost curve well, so it has improved the system's robust performance and dynamic performance. At last, the bench test is experimented to verify the simulation results and it shows that the mixed H2/H∞ controller for EPS is feasible, and it could improve the steering and handling performance and provide satisfied road feeling for the drivers.

A soft computing arithmetic based on Adaptive Neuro-Fuzzy Inference System (ANFIS) is proposed to estimate vehicle yaw rate in a driver-vehicle closed loop system, and vehicle yaw rate is considered as a nonlinear mapping of time series of rack displacement and lateral acceleration. Simulation study on soft computing of vehicle yaw rate in the driver-vehicle closed loop system is conducted, and the performance of the soft computing arithmetic based on ANFIS is evaluated with the help of actual vehicle test data. Results indicate that the generalisation of the soft computing arithmetic based on ANFIS is better than that based on Radial Basis Function (RBF) neural network.