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To adapt to Battery Electric Vehicle (BEV) integration, the significance of protective designs for battery packs against ground impact caused by road debris is very high, and there is also a keen interest in the feasibility assessment technique using Computer-Aided Engineering (CAE) tools for prototype-free evaluations. However, the challenge lies in obtaining real-world empirical data to verify the accuracy of the predictive CAE model. Collecting real-world data using actual battery pack can be time-consuming, costly, and accurately ascertaining the precise direction, magnitude, and location of the force applied from the road to the battery pack poses a challenging task. Therefore, in this study, we developed a methodology using machine learning, specifically Gaussian process regression (GPR), to perform inverse analysis of the direction, magnitude, and location of vehicle-road contact forces during rough road conditions.

When developing an off-road vehicle, it is essential to create excellent drivability that enables the vehicle to be driven on all surfaces while ensuring passenger comfort. Since durability is another indispensable performance aspect for these vehicles, the development method must be capable of considering a high-level combination of a wide range of performance targets. This paper proposes a method to identify the region in which each performance aspect is realized through a complex domain combination problem. The proposed method is helpful in the initial design stage when the detailed specifications of the target vehicle are not determined because it is capable of considering both the specifications and usage method of the target vehicle, such as the selection of road profiles and driving speeds as design variables. The proposed method has the advantage of enabling efficient concurrent studies to search for feasible regions.

Carbon neutrality has become a significant target. One essential parameter regarding energy consumption and emissions is the mass of vehicles. Lightweight design improves the result of vehicle life cycle assessment (LCA), increases efficiency, and can be a step towards sustainability and CO2 neutrality. Weight reduction through structural optimization is a challenging task. Typical design development procedures have to be overcome. Instead of just a facelift or the creation of a derivative of the predecessor design, completely alternative design creation methods have to be applied. Automated structural optimization is one tool for exploring completely new design approaches. Different methods are available and weight reduction is the focus of topology optimization. This paper describes a fatigue life homogenization method that enables the weight reduction of vehicle parts. The applied CAE process combines fatigue life prediction and topology optimization.

Structural Health Monitoring (SHM), especially in the field of rotary machinery diagnosis, plays a crucial role in determining the defect category as well as its intensity in a machine element. This paper proposes a new framework for real-time classification of structural defects in a roller bearing test rig using time domain-based classification algorithms. Along with the bearing defects, the effect of eccentric shaft loading has also been analyzed. The entire system comprises of three modules: sensor module – using accelerometers for data collection, data processing module – using time-domain based signal processing algorithms for feature extraction, and classification module – comprising of deep learning algorithms for classifying between different structural defects occurring within the inner and outer race of the bearing.

This paper provides a simulation analysis of a novel interconnected roll stability control (RSC) system for improving the roll stability of semitrucks with double trailers. Different from conventional RSC systems where each trailer’s RSC module operates independently, the studied interconnected RSC system allows the two trailers’ RSC systems to communicate with each other. As such, if one trailer’s RSC activates, the other one is also activated to assist in further scrubbing speed or intervening sooner. Simulations are performed using a multi-body vehicle dynamics model that is developed in TruckSim® and coupled with the RSC model established in Simulink®. The dynamic model is validated using track test data. The simulation results for a ramp steer maneuver (RSM) and sine-with-dwell (SWD) maneuver indicate that the proposed RSC system reduces lateral acceleration and rollover index for both trailers, decreasing the likelihood of wheel tip-up and vehicle rollover.

This paper describes a new control technology that coordinates the operation of multiple actuators in a new hybrid electric vehicle (HEV) system consisting of a turbocharged engine, front and rear electric motors, two clutches, and a 6-speed automatic transmission. The development concept for this control technology is to achieve the driver’s desired acceleration G with a natural feeling engine speed. First, to realize linear acceleration G even while the engine is starting from EV mode, clutch hydraulic pressure reduction control is implemented. Furthermore, the engine start timing is optimized to prevent delayed drive force response by predicting the required maximum power during cranking. Second, to realize linear acceleration, this control selects the proper gear position based on the available battery power, considering noise and vibration (NV) restrictions and turbocharging response delays.

Nitric Oxide (NO) can significantly influence the autoignition reactivity and this can affect knock limits in conventional stoichiometric SI engines. Previous studies also revealed that the role of NO changes with fuel type. Fuels with high RON (Research Octane Number) and high Octane Sensitivity (S = RON - MON (Motor Octane Number)) exhibited monotonically retarding knock-limited combustion phasing (KL-CA50) with increasing NO. In contrast, for a high-RON, low-S fuel, the addition of NO initially resulted in a strongly retarded KL-CA50 but beyond the certain amount of NO, KL-CA50 advanced again. The current study focuses on same high-RON, low-S Alkylate fuel to better understand the mechanisms responsible for the reversal in the effect of NO on KL-CA50 beyond a certain amount of NO.

Teammate Advanced Drive is a driving support system with state-of-the-art automated driving technology that has been developed for customers’ safe and secure driving on highways based on the Toyota’s Mobility Teammate Concept. This SAE Level 2 (L2) system assists overtaking, lane changes, and branching to the destination, in addition to providing hands-free lane centering and car following. The automated driving technology includes self-localization onto a High Definition Map, multi-modal sensing to cover 360 degrees of the surrounding environment using fusion of LiDARs, cameras, and radars, and a redundant architecture to realize fail-safe operation when a malfunction or system limitation occurs. High-performance computing is provided to implement deep learning for predicting and responding to various situations that may be encountered while driving.

This paper describes a method for assisting pedestrians to cross a road. As motorization develops, pedestrian protection techniques are becoming more and more important. Advanced driving assistance systems (ADAS) are improving rapidly to provide even greater safety. However, since the accident risk of pedestrians remains high, the development of an advanced walking assistance system for pedestrian protection may be an effective means of reducing pedestrian accidents. Crossing a road is one of the highest risk events, and is a complex phenomenon that consists of many dynamically changing elements such as vehicles, traffic signals, bicycles, and the like. A road crossing assistance system requires three items: real-time situational recognition, a robust decision-making function, and reliable information transmission. Edge devices equipped with autonomous systems are one means of achieving these requirements.

Since the regulations of CO2 emissions have been tightened in each country recently, each automotive manufacturer has responded by bringing competitive technologies that maximize efficiency while promoting vehicle electrification such as xEV. Not only the efficiency, we need to meet or exceed occupant performance and comfort expectations. The climate control system expends a large amount of energy to keep a comfortable environment, having a significant impact on fuel consumption and EV driving range. Therefore, many manufacturers try to save energy and improve occupant comfort quickly by using not only the conventional convective heating by HVAC but also the conductive heating to heat the human body directly such as seat and steering wheel heater. In this study, a radiative heater, which is more efficient than a convective heating to warm anterior thigh and shin where a conductive heating cannot warm, was applied to vehicle.

Woven Core, Inc. has developed Teammate Advanced Drive, a driving support system with state-of-the-art automated driving technology based on the Mobility Teammate Concept by Toyota Motor Corporation. Teammate Advanced Drive enables intelligent Ramp to Ramp hands-off driving on highways. The system features a self-localization estimation system that uses an HD-Map (High Definition Map) and high-level redundancy across sensors, computing, actuators, power supplies, and data communication. The system also includes digital data uploading and downloading capabilities wirelessly OTA (Over the Air) in order to provide customers the latest map updates as well as new software features and upgraded performance. A number of characteristically unique sensors have been combined to monitor the entire perimeter of the vehicle with high reliability.

In recent years, GPFs (Gasoline particulate filters) have been installed in gasoline engines to comply with stricter environmental regulations in China and Europe. In particular, coated-GPFs having a catalytic purification function are required to have high conversion performances, high filter efficiencies in the sense of a high collection efficiency, and low pressure loss. It is not easy to design a filter that satisfies all these parameters. Experimental studies are being conducted, but it is costly to study in trial productions. In this technical paper, a GPF design optimization method will be proposed that combines multi-scale simulation, surrogate models by machine learning, and an optimization algorithm. By using this method, a GPF design that minimizes pressure loss while providing high conversion performance and particle collection rates that satisfy current regulations can be created.

Routing quality always dominates the top 20% of in vehicle- navigation customer complaints. In vehicle navigation routing engines do not customize results based on customer behavior. For example, some users prefer the quickest route while some prefer direct routes. This is because in vehicle navigation systems are traditionally embedded systems. Toyota announced that new model vehicles in JP, CN, US will be connected with routing function switching from the embedded device to the cloud in which there are plenty of probe data uploaded from the vehicles. Probe data makes it possible to analyze user preferences and customize routing profile for users. This paper describes a method to analyze the user preferences from the probe data uploaded to the cloud. The method includes data collection, the analysis model of route scoring and user profiling. Furthermore, the evaluation of the model will be introduced at the end of the paper.

Automobile manufactures need to adopt new technologies to meet global CO2 (carbon dioxide) emission regulations and better fuel efficiency demands from customers. Also, the production cost should be as low as possible for an affordable vehicle. Therefore, it is advantageous for OEMs to develop fuel efficient technologies which can be controlled by software without additional hardware costs. The coasting control is a fuel efficiency improvement technology that can be implemented by the change of vehicle software only. The coasting control is a technology that reduces the driving resistance (Deceleration) when the driver releases the gas pedal. This technology leads to reducing the energy required for the vehicle to drive and results in improving the real-world fuel economy. In an internal combustion engine (ICE) vehicle, the coasting state is achieved by changing the gear to neutral, and the effect has been discussed and clarified by many previous studies.

When the air conditioning (A/C) is turned on, the intake air to the HVAC is cooled at the evaporator. This is not only used for cooling the air temperature but also to dehumidify. Therefore, for a typical automatic climate control system, A/C will automatically operate even in winter (cold ambient temperature conditions) in order to prevent the windows from fogging despite its effect on fuel economy. In some applications, a humidity sensor is installed on top of the windshield and when the probability of fogging is low the A/C operation is disabled automatically to prevent unnecessary compressor operation which can increase fuel consumption. However, humidity sensor is not widely adopted as it requires some space to be installed and the cost is relatively expensive compared with other HVAC equipped sensors. In this study, a system was invented that disables the compressor operation when the fogging probability is low without using the conventional humidity sensor.

Knowledge of experts is necessary for judging motor current waveforms. Here, we develop an automatic judgement system for motor current waveform by establishing an AI model trained by knowledge of experts and CAE technology.

The quantity of heavy components in fuel is increasing as automotive fuels diversify, and engine oil formulations are becoming more complex. These trends result in the formation of larger amounts of carbon deposits as reaction byproducts during combustion, potentially worsening the susceptibility of the engine to knock [1]. The research described in this paper aimed to identify the mechanism that causes knocking to deteriorate due to carbon deposits in low to medium engine load ranges, which are mainly used when the vehicle drives off and accelerates. With this objective, the cylinder temperature and pressure with and without deposits were measured, and it was found that knocking deteriorates in a certain range of ignition timing.

This article proposes a rubber suspension bushing model considering amplitude dependence as a useful tool at the initial design phase. The purpose of this study is not to express physical phenomena accurately and in detail and to explore the truth academically, but to provide a useful design method for initial design phase. Experiments were carried out to verify several dynamic characteristics of rubber bushings under vibration up to a frequency of 100 Hz, which is an important frequency range when designing ride comfort performance. When dynamic characteristic theory and the geometrical properties of the force-displacement characteristic curve were considered using these dynamic characteristics as assumptions, an equation was derived that is capable of calculating the dynamic stiffness under an arbitrary amplitude by identifying only two general design parameters (dynamic stiffness and loss factor) under a reference amplitude.

A hybrid navigation system [1] that performs route calculations and highly flexible natural speech location searches in the cloud using dynamic databases that combine probe data collected from the vehicle and external data, and transmits to on-board devices has been developed. The system automatically switches to the on-board device when the vehicle is out of mobile network communication range or when faster processing is required for tasks such as re-routing. The transition between the on-board devices and the cloud provide a seamless user experience adapted to use conditions and other factors. In addition, representing the route downloaded from the cloud by the on-board device requires synchronizing the map with the cloud, and a map caching function has been used to reduce the volume of data that needs to be synchronized. The cloud-based route calculation is based not only on average travel time, but on dispersion as well.

One of the principal goals of modern vehicle control systems is to ensure passenger safety during dangerous maneuvers. Their effectiveness relies on providing appropriate parameter inputs. Tire-road contact forces are among the most important because they provide helpful information that could be used to mitigate vehicle instabilities. Unfortunately, measuring these forces requires expensive instrumentation and is not suitable for commercial vehicles. Thus, accurately estimating them is a crucial task. In this work, two estimation approaches are compared, an observer method and a neural network learning technique. Both predict the lateral and longitudinal tire-road contact forces. The observer approach takes into account system nonlinearities and estimates the stochastic states by using an extended Kalman filter technique to perform data fusion based on the popular bicycle model.