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There are two shortcomings in the application of traditional A* algorithm in the path planning of autonomous driving. One is that the vehicle environment description method suitable for the A* algorithm is not given; the other is that the vehicle contours and kinematic constraints are not considered. Therefore, according to the characteristics of unstructured environment, this paper presents an environment description method combining global navigation layer and local planning layer, and proposes a local motion planning algorithm based on the improved A* algorithm for autonomous driving vehicles in unstructured environment. In the improved algorithm, profile collision is avoided by setting redundant security space, and the cost of path curvature is considered in heuristic function design. Compared with the original algorithm, it can improve the smoothness of the path, so as to get a path more satisfied with vehicle motion constraints. Simulation results show that the improved algorithm can avoid vehicle contours collision and output a smoother path.

The equivalent static loads method for nonlinear dynamic response structural optimization may be failed in large deformation crash conditions, due to topology optimization with the equivalent static loads mostly beyond the linear range and causing numerical defects such as high compliance of elements. To overcome the above disadvantage, an advanced structural topology optimization method for crashworthiness considering crash-reduced large deformation and plastic buckling is proposed using newly defined equivalent linear static loads. The equivalent linear static loads can adaptively scale to guarantee that the topology optimization is performed within linear range. At each cycle, the crash simulation is performed and the nonlinear nodal displacement vector at the time step with the maximum strain energy is scaled by an adaptive displacement-scaling factor. The equivalent linear static loads that are generated by multiplying the linear stiffness matrix and the scaled nodal displacement vector will be incorporated into topology optimization, which can guarantee the topology optimization to remain in linear range and further solve the numerical instability problems. The process is repeated until the convergence criteria are satisfied. The effectiveness of the proposed method is evaluated by solving a crashworthiness topology optimization of a crash box considering crash-induced plastic buckling to determine the location and profile of crash triggers. The results show that the proposed method can effectively solve the large deformation crashworthiness topology optimization of thin-walled structures and provides a feasible strategy for crash triggers design in crash box.

This paper investigates the effect of using augmented reality (AR) annotations and two different gaze visualizations, head pointer (HP) and eye gaze (EG), in an AR system for remote collaboration on physical tasks. First, we developed a spatial AR remote collaboration platform that supports sharing the remote expert’s HP or EG cues. Then the prototype system was evaluated with a user study comparing three conditions for sharing non-verbal cues: (1) a cursor pointer (CP), (2) HP and (3) EG with respect to task performance, workload assessment and user experience. We found that there was a clear difference between these three conditions in the performance time but no significant difference between the HP and EG conditions. When considering the perceived collaboration quality, the HP/EG interface was statistically significantly higher than the CP interface, but there was no significant difference for workload assessment between these three conditions. We used low-cost head tracking for the HP cue and found that this served as an effective referential pointer. This implies that in some circumstances, HP could be a good proxy for EG in remote collaboration. Head pointing is more accessible and cheaper to use than more expensive eye-tracking hardware and paves the way for multi-modal interaction based on HP and gesture in AR remote collaboration.

Refereed/Peer-reviewed In recent years, the multi-dimensional, multi-faceted, and multi-level applications of augmented reality/mixed reality have attracted more and more attention from researchers in the field of smart manufacturing. However, the disadvantage is that AR interaction cues supporting user cognition have not been fully analyzed and summarized in manual assembly, training and repair. This article reviews the current research status, projects and technical characteristics of manual operation instructions in the past 30 years, and extensively discusses these latest works. It is worth mentioning that this article provides comprehensive academic information for the development of AR-based assembly instructions, thereby providing unique insights for researchers in related fields. In short, it will help researchers designing AR instructions.

In this study, an efficient topology optimization method under crash loads is proposed by combining the equivalent static loads with a model order reduction method, which is referred as the reduced model–based equivalent static loads method for nonlinear dynamic response topology optimization method. Considering that some parts of the vehicle experience large nonlinear deformations, whereas others exhibit only small linear deformations in a vehicle crash scenario, the linear and nonlinear behavior parts are identified and the whole model of the complete structure is divided into nonlinear and linear sub-models. At each cycle, the model order reduction method is used in the linear sub-model during crash analysis to solve the low-density-elements-induced mesh distortion problem and accelerate this process. In the linear static topology optimization, the nonlinear sub-model that was initially used to describe the nonlinear behavior part is linearized by the equivalent static loads method and then reduced by the Guyan reduction method. Then, the reduced equivalent static load model is assembled into the linear sub-model that is defined as the design space to formulate a reduced topology optimization model of the complete structure and the reduced equivalent static loads that only act on master degrees of freedom are calculated. Finally, the linear static topology optimization is performed based on the reduced topology optimization model with the reduced equivalent static loads to enhance the efficiency and improve the numerical stability. The process is repeated until the convergence criterion is satisfied. The effectiveness of the proposed method is demonstrated by investing a numerical example. The results show that the proposed method provides a feasible strategy for the topology optimization under crash loads, which can effectively improve the numerical stability and convergence.

A low temperature environment will lead to the decrease of chemistry reaction rate and increase of the internal resistance of the lithium battery. In addition, the excessive charging current will cause the lithium to separate out and even the permanent attenuation of battery capacity. In order to solve these problems, this paper proposes a low-temperature charging heating combined control strategy, which takes the temperature acceptable charging current of the battery at low temperature as the charging current constraint and the maximum output power of the system as the power constraint. Firstly, a scheme of combined charging and heating control system is put forward. Secondly, the low temperature charging control strategy based on adaptive fuzzy control is established and then the model is simulated and analyzed in MATLAB software. At last, a Chroma 72,001 charge and discharge tester is used to conduct a low temperature test on 18,650 lithium iron phosphate battery monomers. The results show that the low-temperature charging control strategy proposed in this paper has a more stable temperature control effect on the battery, the constant current charging time of the battery is reduced by 14% compared with the traditional threshold control method, and the overall charging energy consumption is reduced by 5.6%.

This paper presents an efficient structural topology optimization strategy for crashworthiness. Although equivalent static loads (ESLs) provides a relatively effective way to solve nonlinear dynamic response topology optimization problems, efficiency is still required to improve. To overcome above disadvantage, the proposed method combines the ESLs with model reduction (MR) method. The ESLs method transforms crashworthiness topology optimization problems into a series of linear static topology problems. The model reduction scheme is applicable to reduce the linear matrix equation and ESLs to interface degrees of freedom (DOFs), by selecting interface DOFs between design and non-design space as master DOFs. Therefore, the linear static topology optimization is performed in a reduction manner and the efficiency is improved. The topology optimization results are filtered into a black-white design for the crash simulation to avoid the element distortion problem. The process is repeated until the convergence criteria is satisfied. A numerical example is used to verify the reliability of the proposed method. The results are compared with the dynamic response optimization method using ESLs (ESLSO). The results show that, the proposed method can efficiently solve structural topology optimization problems for crashworthiness.

In this paper, we describe a new Mixed Reality (MR) remote collaborative platform making use of 3D CAD models for training in the manufacturing industry. It enables a remote expert in Virtual Reality (VR) to train a local worker in a physical assembly task. For the local site, we use Spatial Augmented Reality (SAR) to enable the local worker see virtual cues without wearing any AR devices, leaving their user hands free to easily manipulate the physical parts. For the remote expert, we construct a 3D virtual environment using virtual replicas of the physical parts. We also report on the results of a usability study of the prototype. Refereed/Peer-reviewed

With the popularity of electric vehicles (EV), the charging technology has become one of the bottleneck problems that limit the large-scale deployment of EVs. In this paper, a charging method using multi-stage constant current based on SOC (MCCS) is proposed, and then the charging time, charging capacity and temperature increase of the battery are optimized by multi-objective particle swarm optimization (MOPSO) algorithm. The influence of the number of charging stages, the cut-off voltage, the combination of different target weight factors and the ambient temperature on the charging strategy is further compared and discussed. Finally, according to the ambient temperature and users’ requirements of charging time, a charging strategy suitable for the specific situation is obtained by adjusting the weight factors, and the results are analyzed and justified on the basis of the experiments. The results show that the proposed strategy can intelligently make more reasonable adjustments according to the ambient temperature on the basis of meeting the charging demands of users.

Lithium-ion batteries will produce a lot of heat during working, which can increase the temperature and temperature difference in the battery module, and seriously impair the capacity, life, and safety of the batteries. Therefore, an effective thermal management system should be implemented to control the temperature of the batteries, while reducing the energy consumption and cost of the system as much as possible. In this paper, a passive, low-cost battery thermal management system consisting of pouch lithium-ion cells and phase change material units is designed. Based on the heat production process of the battery and heat transfer theory of the phase change material, a numerical model is built and the performance of the thermal management system is analyzed. It is found that the phase change material units can effectively decrease the maximum temperature and the maximum temperature difference on the cell at the end of discharge, and thus have good heat storage capability and temperature equalization capability. After insulating heat dissipation from the surfaces of the phase change material units, a temperature retaining function can also be achieved. The effects of the design parameters of the phase change material unit on its thermal management performance are further investigated, including the thermal conductivity, viscosity, and latent heat of the phase change material, the thickness of the phase change material unit, and the thermal conductivity of the unit shell. It could provide theoretical references and technical means for the design and implementation of passive, low-cost battery thermal management systems applied with phase change materials.

This paper presents a new method for battery degradation estimation using a power-energy (PE) function in a battery/ultracapacitor hybrid energy storage system (HESS), and the integrated optimization which concerns both parameters matching and control for HESS has been done as well. A semiactive topology of HESS with double-layer capacitor (EDLC) coupled directly with DC-link is adopted for a hybrid electric city bus (HECB). In the purpose of presenting the quantitative relationship between system parameters and battery serving life, the data during a 37-minute driving cycle has been collected and decomposed into discharging/charging fragments firstly, and then the optimal control strategy which is supposed to maximally use the available EDLC energy is presented to decompose the power between battery and EDLC. Furthermore, based on a battery degradation model, the conversion of power demand by PE function and PE matrix is applied to evaluate the relationship between the available energy stored in HESS and the serving life of battery pack. Therefore, according to the approach which could decouple parameters matching and optimal control of the HESS, the process of battery degradation and its serving life estimation for HESS has been summed up.

Electric vehicle thermal management system (EVTMS) is essential for electric vehicles (EVs) to guarantee cabin thermal comfort and battery appropriate temperature. However, under low-temperature driving conditions, heating power demand for EVTMS affects system performance as well as battery lifetime. In this context, an effective EVTMS solution both for cabin thermal comfort and battery heating is investigated in this paper. The EVTMS performance and battery-lifetime loss characteristics under low temperatures are analyzed. Moreover, a battery-lifetime extension method which reduces battery-lifetime loss by the peak shifting and valley filling effect for battery current is advanced. A novel EVTMS fuzzy control strategy based on this method is proposed to maintain the proper temperature range of cabin and battery, which also regulate the heater load considering battery current. Using an EV and EVTMS model validated by low-temperature driving experiments, the proposed strategy was investigated by simulation and the control effect was carried out. The results showed that compared with the original strategy, battery-lifetime loss with the proposed strategy was reduced by 3.11–3.76% while the average temperatures for the cabin and battery were almost unaffected. Therefore, the strategy can reduce battery degradation while meeting the cabin and battery heating requirements.

A hybrid energy storage system (HESS) composed of a battery and super-capacitor (SC) can make full use of advantages in energy and power density, which can further improve the performance of hybrid electric vehicles (HEV). Most studies have been limited to configuration and control to reduce the burden and prolong the life cycle of the battery, seldom focusing on the redesign of energy management strategies (EMS) for HEVs when passing from battery-only energy storage to HESS. In this paper, the equivalent fuel consumption during parallel-charging of a semi-active HESS in a series-parallel HEV under rule-based EMS was deduced, and a fuzzy-logic based (FLB) CS for the HESS was established using the state of energy (SOE) of the battery and SC as inputs. This allows the requirement of energy balance for HESS to be clearly expressed. Furthermore, the regulated EMS for HEV, which could dynamically manage the high efficiency of the engine and maintain the real-time charging/discharging capability of HESS, was proposed and verified under hardware in a loop test (HIL). Quantitative comparison of results between HESS and battery-only indicated that velocity-based SC'SOE-adjustable EMS presented in this paper could make better use of SC in filtering than SOE-constant EMS, and improve the fuel economy of HEVs from 22.76 l/100 km (battery-only) to 21.18 l/100 km. In order to emphasize the advantages of management, the electric energy usage/loss and efficiency under city driving-cycle were also presented.

This paper presents an optimized energy management strategy for Li-ion power batteries used on electric vehicles (EVs) at low temperatures. Under low-temperature environments, EVs suffer a sharp driving range loss resulted from the energy and power capability reduction of the battery. Simultaneously, because of Li plating, battery degradation becomes an increasing concern as temperature drops. All these factors could greatly increase the total vehicle operation cost. Prior to battery charging and vehicle operating, preheating battery to a battery-friendly temperature is an approach to promote energy utilization and reduce total cost. Based on the proposed LiFePO4 battery model, the total vehicle operation cost under certain driving cycles is quantified in the present paper. Then given a certain ambient temperature, a target temperature of preheating is optimized under the principle of minimizing total cost. As for the preheating method, a liquid heating system is also implemented on an electric bus. Simulation results show that the preheating process becomes increasingly necessary with a decreasing ambient temperature; however, the preheating demand declines as driving range grows. Vehicle tests verify that the preheating management strategy proposed in this paper is able to save total vehicle operation cost.

Range anxiety and battery cycle life are two major factors which restrict the development of electric vehicles. Battery degradation can be reduced by adding supercapacitors to create a Hybrid Energy Storage System. This paper proposes a systematic approach to configure the hybrid energy storage system and quantifies the battery degradation for electric vehicles when using supercapacitors. A continuous power-energy function is proposed to establish supercapacitor size based on national household travel survey statistics. By analyzing continuous driving action in standard driving cycles and special driving phases (start up and acceleration), the supercapacitor size is calculated to provide a compromise between the capacitor size and battery degradation. Estimating the battery degradation after 10 years, the battery capacity loss value decreases 17.55% and 21.6%, respectively, under the urban dynamometer driving schedule and the US06. Furthermore, the battery lifespan of the continuous power-energy configured system is prolonged 28.62% and 31.39%, respectively, compared with the battery alone system.

Dynamic Programming (DP) algorithm is used to find the optimal trajectories under Beijing cycle for the power management of synergic electric system (SES) which is composed of battery and super capacitor. Feasible rules are derived from analyzing the optimal trajectories, and it has the highest contribution to Hybrid Electric Vehicle (HEV). The methods of how to get the best performance is also educed. Using the new Rule-based power management strategy adopted from the optimal results, it is easy to demonstrate the effectiveness of the new strategy in further improvement of the fuel economy by the synergic hybrid system.

Although both battery and super-capacitor are important power sources for hybrid electric vehicles, there is no accurate configuration theory to match the above two kinds of power sources which have significantly different characteristics on energy and power storage for the goal of making good use of their individual features without size wasting. In this paper, a new performance is presented that is used for analysis and optimal design method of battery and super-capacitor for hybrid energy storage system of a parallel hybrid electrical vehicle. In order to achieve optimal design with less consumption, the power-energy function is applied to establish direct mathematical relationship between demand power and the performance. During matching process, firstly, three typical operating conditions are chosen as the basis of design; secondly, the energy and power capacity evaluation methods for the parameters of battery and super-capacitor in hybrid energy storage system are proposed; thirdly, the mass, volume, and cost of the system are optimized simultaneously by using power-energy function. As a result, there are significant advantages on mass, volume, and cost for the hybrid energy storage system with the matching method. Simulation results fit well with the results of analysis, which confirms that the optimized design can meet the demand of hybrid electric vehicle well.

Both the battery/supercapacitor (SC) and SC/battery are two common semi-active configurations of hybrid energy storage systems (HESSs) in hybrid electric vehicles, which can take advantage of the battery’s and supercapacitor’s respective characteristics, including the energy ability, power ability and the long lifetime. To explore in depth the characteristics and applicability of the two kinds of HESS, an analysis and comparison study is proposed in this paper. Based on the data collected from public transit hybrid electric bus (PTHEB) with battery-only on-board energy storage, the range and distribution probability of electric power/energy demand is comprehensively statistically analyzed with the decomposing and normalizing methods. Accordingly, the performance of each topology under different parameter matching conditions but same mass, volume and cost values with battery-only energy storage, are presented and compared quantitatively. The results show that both HESS configurations can meet the electric power demand of the hybrid electric vehicle (HEV) through reasonable design. In particular, the SC/battery can make better use of the SC features resulting in high efficiency and long life cycles compared with the battery/SC. Equally, it proves that the SC/battery topology is a better choice for the HEV. Finally, an experimental validation of a real HEV is carried out, which indicated that a 7% fuel economy improvement can be achieved by a SC/battery system compared with battery-only topology.

Under the current technical circumstances, the high cost of the electrical vehicle is the biggest problem of the mass marketization. So how to reduce the cost on the promise that the dynamic performance requirements be meet becomes more and more important. The purpose of this study is to develop a methodology to optimize an Extended-Range Electric Vehicle’s parameters taking minimum drivetrain cost and the best fuel economy as objectives. Design parameters, including electric motor peak power, engine rated power, and battery capacity are reasonably set. The simulation result illustrates that a set of vehicle dynamic performance constraints are met, in the same time the production cost is reduced and the fuel economy is improved. Further study should optimize the control strategy variables, too. Thus, the fuel economy can be optimized in a further extent. What’s new of this study in the field is taking production cost as one of the objectives to optimize the powertrain parameters. According to the ADVISOR simulation result, the fuel economy have been significantly improved and production cost has been reduced.

Reliability is an important vehicle performance. It mainly depends on the fatigue life of the key auto parts. As the key component in transmission system of the truck, gearbox's efficiency and reliability are closely linked with the truck's power and safety. In the paper, by using multi-body dynamics and finite element method, the fatigue life of the gearbox housing with static method is presented.

The quasiphase matched PPMgLN crystal is used intracavity frequency doubled to obtain high power green laser, with a pump power of 20W at 808nm, 6.67W output power at 532nm is achieved.

Efficient and compact green lasers with intracavity frequency doubling were developed. Over 52% optical-optical efficiency with 1.3 W output power was obtained, and the lifetime was over 10000 hours. Compact digital laser powers were also developed.