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The fusion of multi-modal perception in autonomous driving plays a pivotal role in vehicle behavior decision-making. However, much of the previous research has predominantly focused on the fusion of Lidar and cameras. Although Lidar offers an ample supply of point cloud data, its high cost and the substantial volume of point cloud data can lead to computational delays. Consequently, investigating perception fusion under the context of 4D millimeter-wave radar is of paramount importance for cost reduction and enhanced safety. Nevertheless, 4D millimeter-wave radar faces challenges including sparse point clouds, limited information content, and a lack of fusion strategies. In this paper, we introduce, for the first time, an approach that leverages Graph Neural Networks to assist in expressing features from 4D millimeter-wave radar point clouds. This approach effectively extracts unstructured point cloud features, addressing the loss of object detection due to sparsity.

In this paper, a newly developed Active Noise Control (ANC) system is introduced, that effectively reduces road noise, which becomes a major issue with electrified vehicles, and that enhances vehicle interior sound levels matching seamless acceleration by electric drive. Conventionally, reducing road noise using ANC requires numerous sensors and speakers, as well as a processor with high computing power. Therefore, the increase in system cost and the complexity of the system are obstacles to its spread. To overcome these issues, this system is developed based on four concepts. The first is a modular system configuration with unified interface to apply to various vehicle types and grades. The second is the integration and optimal placement of noise source reference sensors to achieve both reduction in number of parts and noise reduction performance.

To reduce the energy consumption level of electric vehicles, the working range of the regenerative braking system will gradually expand to the high state of charge of the battery. The time delay in the control signal transmission path of the high state of charge regenerative braking control process will affect the regenerative braking. At the same time, regenerative braking under a high state of charge puts forward higher requirements for the control accuracy of regenerative current. In the research of this paper, the motor model, battery model, and vehicle dynamics model are firstly established by using MATLAB/Simulink, and the dynamic relationship between regenerative current and regenerative braking torque is analyzed at the same time. Considering the system time delay, this paper proposes a high-charge regenerative braking control strategy (SPPC) that combines Smith prediction and prescribed performance control.

Electrification is the well-accepted solution to address carbon emissions and modernize vehicle controls. Batteries play a critical in the journey of electrification and modernization with battery voltage prediction as the foundation for safe and efficient operation. Due to its strong dependency on prior information, battery voltage was estimated with recurrent neural network methods in the recent literatures exploring a variety of deep learning techniques to estimate battery behaviors. In these studies, standard recurrent neural networks, gated recurrent units, and long-short term memory are popular neural network architectures under review. However, in most cases, each neural network architecture is individually assessed and therefore the knowledge about comparative study among three neural network architecture is limited. In addition, many literatures only studied either the dynamic voltage response or the voltage relaxation.

Due to the poor ability of millimeter wave radar in recognizing distant static objects, target loss and incomplete information will occur when it recognizes the static target in front, thus increasing the false alarm rate and missing alarm rate of the radar-dependent driving assistant system, which will reduce the driving safety and the acceptability of the assistant system. Aiming at the radar's poor ability to recognize static targets, this paper uses a model based on machine learning algorithm to recognize and track targets. The radar signals are collected and processed in different conditions, and the results show that the radar has a poor recognition effect when the distance is more than 100 meters and the speed is more than 19m/s.

Today’s progress in electronic technologies is advancing the process of making vehicles more intelligent, and this is making driving safer and more comfortable. In recent years, numerous vehicles equipped with high-level Advance Driving Assist System (ADAS) have been put on the market. High-level ADAS can detect impending lane deviation, and control the vehicle so that the driver does not deviate from the lane. Lane departure prevention systems are able to detect imminent departure from the road, allowing the driver to apply control to prevent lane departure. These systems possess enormous potential to reduce the number of accidents resulting from road departure, but their effectiveness is highly reliant on their level of acceptance by drivers.

In this research, a material model was developed that has orthotropic properties with respect to in-plane damage to support finite element strength analysis of components manufactured from a randomly oriented long-fiber thermoplastic composite. This is a composite material with randomly oriented bundles of carbon fibers that are approximately one inch in length. A macroscopic characteristic of the material is isotropic in in-plane terms, but there are differences in the tension and compression damage properties. In consideration of these characteristics, a material model was developed in which the damage evolution rate is correlated with thermodynamic force and stress triaxiality. In-plane damage was assumed to be isotropic with respect to the elements. In order to validate this material model, the results from simulation and three-point bending tests of closed-hat-section beams were compared and found to present a close correlation.

A rubber sealing for a water-cooled battery pack plays a significant role to prevent water immersion into the inside of the pack. The appropriate design including the adjacent parts achieves a weight reduction of the battery pack by reducing the battery tray thickness and the quantity of bolts used in the whole battery pack. Generally, finite element analysis (FEA) is effective for the design optimization before proto-typing. However, the application to the sealing for a battery pack requires a large scale analysis, including the complicated contacts and large deformation of the rubber sealing, and results in unpractically long computation time and frequent computation errors due to the finite element distortion. A multi-scale structural analysis and the process on the rubber sealing for the battery pack has been developed to solve the above issues. This approach consists of 3 steps, which are single-unit, entire-scale and detailed structural analysis.

HTL is a kind of biodiesel converted from wet biowaste via hydrothermal liquefaction (HTL), which has drawn increasing attention in recent years due to its wide range of raw materials (algae, swine manure, and food processing waste). However, from the previous experiments done in a constant volume chamber, it was observed that the presence of 20% of HTL in the blend produced as much soot as pure diesel at in chamber environment oxygen ratio of 21%, and even more soot at low oxygen ratios. It was also observed that n-butanol addition could reduce the soot emission of diesel significantly under all tested conditions. In this work, the spray and combustion characteristics of HTL and diesel blends with n-butanol added were investigated in a constant volume chamber. The in-chamber temperature and oxygen ranged from 800 to 1200 K and 21% to 13%, respectively, covering both conventional and low-temperature combustion (LTC) regimes.

The dynamic performance of battery electric vehicles (BEV) is affected by battery output power, which depends on state of charge (SOC) and the temperature of battery cells. The temperature of the batteries varies in particular with the environment, in which the user stores the vehicle, and the battery output power. It is therefore necessary to employ thermal management systems that can control the battery temperature within the optimal range under severely hot and cold conditions in BEVs. A highly sophisticated thermal management system and its operation strategy were developed to fulfill the above requirements. The powertrain components to be thermo-controlled were located into two coolant circuits having different temperature range. The compact and efficient front-end heat exchangers were designed to optimally balance the cooling performance of powertrain, cabin comfort, vehicle aerodynamics and the vehicle design.

Controllability is defined in ISO 26262 as a driver’s ability to avoid a specified harm caused by a malfunction of electrical and electronic systems installed in road vehicles. According to Annex C of Part 12 of ISO 26262, simulation is one of the techniques that the Controllability Classification Panel (CCP) can use to evaluate comprehensively the controllability class (C class) of motorcycles. With outputs of (i) an index for the success of harm avoidance and (ii) the magnitude of the rider’s compensatory control action required to avoid harm, the simulation is useful for evaluating the C class of the degrees of malfunction that cannot be implemented in practice for the sake of the test rider’s safety. To aim at supplying data that the CCP can use to judge the C class, we try to estimate the vehicle behavior and a rider’s compensatory control actions following a malfunction using vehicle dynamics simulations.

A compact intelligent power unit capable of being installed under the rear seating was developed for the 2018 model year Accord Hybrid that is to be equipped with the SPORT HYBRID Intelligent Multi Mode Drive (i-MMD) system. The space under the rear seat features multiple constraints on dimensions. In the longitudinal direction, it is necessary to attempt to help ensure occupant leg room and to position the fuel tank; in the vertical direction, it is necessary to attempt to help ensure occupants comfort and a minimum ground clearance; and in the lateral direction, it is necessary to avoid the position of the body side frames and the penetrating section of the exhaust pipe. The technologies described below were applied in order to reduce the size of components, making it possible to position the IPU amid these constraint conditions.

It is experimentally known that the surface color of bearing balls gradually becomes brown during long term operation of the bearings under appropriate lubrication conditions. That exhibits the possibility of an estimation method for residual life of ball bearings without any abnormal wear on the surfaces by precise color measurements. Therefore, we examined what set colors on bearing balls by surface observation using scanning electron microscopy and subsurface analysis using transmission electron microscopy. Results showed that an amorphous carbon layer had gradually covered ball surfaces during operation of the bearings. The layer not only changed ball color but also made overall ball shapes closer to a complete sphere. The report also introduces a uniquely developed color analyzer which enabled color measurements on metallic surfaces, such as the above-mentioned balls.

Flash boiling has drawn much attention recently for its ability to enhance spray atomization and vaporization, while providing better fuel/air mixing for gasoline direct injection engines. However, the behaviors of flash boiling spray with multi-component fuels have not been fully discovered. In this study, isooctane, ethanol and the mixtures of the two with three blend ratios were chosen as the fuels. Measurements were performed with constant fuel temperature while ambient pressures were varied to adjust the superheated degree. Macroscopic and microscopic characteristics of flash boiling spray were investigated using Diffused Back-Illumination (DBI) imaging and Phase Doppler Anemometry (PDA). Comparisons between flash boiling sprays with single component and binary fuel mixtures were performed to study the effect of fuel properties on spray structure as well as atomization and vaporization processes.

The spray structure and vaporization processes of flash-boiling sprays in a constant volume chamber under a wide range of superheated conditions were experimentally investigated by a high speed imaging technique. The Engine Combustion Network’s Spray G injector was used. Four fuels including gasoline, ethanol, and gasoline-ethanol blends E30 and E50 were investigated. Spray penetration length and spray width were correlated to the degree of the superheated degree, which is the ratio of the ambient pressure to saturated vapor pressure (pa/ps). It is found that parameter pa/ps is critical in describing the spray transformation under flash-boiling conditions. Three distinct stages namely the slight flash-boiling, the transition flash-boiling, and the flare flash-boiling are identified to describe the transformation of spray structures.

The study of vehicle coupling state estimation accuracy especially in observer-based vehicle chassis control for improving road handling and ride comfort is a challenging task for vehicle industry under various driving conditions. Due to a large amount of life safety arising from vehicle roll behavior, how to precisely acquire vehicle roll state and rapidly provide for the vehicle control system are of great concern. Simultaneously, uncertainty is unavoidable for various aspects of a vehicle system, e.g., varying sprung mass, moment of inertia and position of the center of gravity. To deal with the above issues, a novel dual observer approach, which combines adaptive Unscented Kalman Filter (AUKF) and Takagi-Sugeno (T-S), is proposed in this paper. A full-car nonlinear model is first established to describe vehicle lateral and vertical coupling roll behavior under various road excitation.

Adhesive bonding is a key technology for the lighter weight of battery pack trays using aluminum material. A robust design method of adhesive bonding with the required strength for battery pack structure after degradation was developed to minimize variability of strength under various noise conditions. The parameter design based on Taguchi methods determined the optimum adhesive condition of the bonding process. To guarantee strength after degradation, it is essential to select a robust adhesive material and to minimize the strength variation derived from the adhesive material. The functional evaluation, which includes experimental design method, determined adhesive material with the minimum strength variation among material candidates. Then, robustness of the adhesive material itself has been evaluated as the result of collaboration with the adhesive material supplier. This analysis was able to regulate the compound ratio of raw materials without reducing the adhesive strength.

Honda has developed the 2018 model CLARITY PLUG-IN HYBRID. Honda’s new plug-in hybrid is a midsize sedan and shares a body platform with the CLARITY FUEL CELL and the CLARITY ELECTRIC. The vehicle’s electric powertrain boosts driving performance as an electric vehicle (EV) over Honda’s previous plug-in hybrid. The CLARITY PLUG-IN HYBRID’s electric powertrain consists of a traction motor and generator built into the transmission, a Power Control Unit (PCU) positioned above the transmission, an Intelligent Power Unit (IPU) fitted under the floor, and an onboard charger fitted below the rear trunk. The PCU integrates an inverter that drives the traction motor, an inverter that drives the generator, and a DC-DC converter to boost battery voltage (referred to as a “Voltage Control Unit (VCU)” below).

The advanced Pedestrian Legform Impactor (aPLI) incorporates a number of enhancements for improved lower limb injury prediction capability with respect to its predecessor, the FlexPLI. The aPLI also incorporates a simplified upper body part (SUBP), connected to the lower limb via a mechanical hip joint, that expands the impactor’s applicability to evaluate pedestrian’s lower limb injury risk also in high-bumper cars.As the aPLI has been developed to be used in standardized testing, further considerations on the impactor’s manufacturability, robustness, durability, usability, and repeatability need to be accounted for.. The aim of this study is to define and verify, by means of numerical analysis, a battery of design modifications that may simplify the manufacturing and use of physical aPLIs, without reducing the impactors’ biofidelity. Eight candidate parameters were investigated in a two-step numerical analysis.

Increasing the strength of materials is effective in reducing weight and boosting structural part performance, but there are cases in where the residual strain generated during the process of manufacturing of high-strength materials results in a decline of durability. It is therefore important to understand how the residual strain in a manufactured component changes due to processing conditions. In the case of a connecting rod, because the strain load on the connecting rod rib sections is high, it is necessary to clearly understand the distribution of strain in the ribs. However, because residual strain is generally measured by using X-ray diffractometers or strain gauges, measurements are limited to the surface layer of the parts. Neutron beams, however, have a higher penetration depth than X-rays, allowing for strain measurement in the bulk material.