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This research aims to develop an inverse control method capable of adaptively simulating dynamic models of car subsystems in the rig-test condition. Accurate simulation of the actual vibration conditions is one of the most crucial factors in realizing reliable rig-test platforms. However, most typical rig tests are conducted under simple random or harmonic sweep conditions. Moreover, the conventional test methods are hard to directly adapt to the actual vibration conditions when switching the dynamic characteristics of the subsystem in the rig test. In the present work, we developed an inverse controller to adaptively control the vibration exciter referring to the target vibration signal. An adaptive LMS filter, employed for the control algorithm, updated the filter weights in real time by referring to the target and the measured acceleration signals.

As electric vehicles become more widespread, such vehicles may be subject to “range anxiety” due to the risk of discharging during driving or the discharging when left unused for a long period. Accordingly, a vehicle equipped with a mobile charger that can provide a charge in an emergency. The vehicle with the mobile charger is usually composed of a large capacity battery, a power converter in a small truck. However, the large capacity battery and the power converter are disadvantageous in that they are large in size and expensive and should be produced as a special vehicle. In this paper, we propose a method to solve the problem using an internal EV system without requiring an additional power generation, battery and a charging-and-discharging device. The method is a novel Vehicle-to-Vehicle (V2V) fast charging control system utilizing motor and inverter in EV.

In case of all gasoline vehicles such as the passenger vehicle, heavy duty truck and light duty truck etc., a fuel pump is located inside the fuel tank and transfers the fuel to an engine for stable driving, however, engine stall can be occurred by low pressure fuel pump. The boiling temperature of gasoline fuel is very low, the initial boiling point is around 40°C so fuel can boil easily while driving and end boiling point is around 190°C. It boils sequentially depending on the temperature. It becomes the criteria to determine the amount of vapor released inside the fuel tank at high temperature. The main cause of engine stall at high temperature is rapid fuel boiling by increasing fuel temperature. This causes a lot of vapor. Such vapor flows into the fuel pump which leading to decrease the pump load and the current consumption of the fuel pump continuously. This ultimately results in engine stall.

As the original engine sound is usually not enough to satisfy the driver’s desire for a sporty and fascinating sound, Active Noise Control (ANC) and Active Sound Design (ASD) have been great technologies in automobiles for a long time. However, these technologies which enhance the sound of vehicles using loud speakers or electromagnetic actuators etc. lead to the increase of cost and weight due to the use of external amplifiers or actuators. This paper presents a new technology for generating a target sound by the active control of a permanent magnet synchronous motor (PMSM) of a mass-production steering system. The existing steering hardware or motor is not changed, but only additional software is added. Firstly, an algorithm of this technology, called Active Sound Generation (ASG), is introduced which is compiled and included in the ECU target code. Then the high frequency noise issue and its countermeasures are presented.

This paper presents an energy-optimal deceleration planning system (EDPS) to maximize regenerative energy for electrified vehicles on deceleration events perceived by map and navigation information, machine vision and connected communication. The optimization range for EDPS is restricted within an upcoming deceleration event rather than the entire routes while in real time considering preceding vehicles. A practical force balance relationship based on an electrified powertrain is explicitly utilized for building a cost function of the associated optimal control problem. The optimal inputs are parameterized on each computation node from a set of available deceleration profiles resulting from a deceleration time model which are configured by real-world test drivings.

Voice Recognition (VR) systems have become an integral part of the infotainment systems in the current automotive industry. However, its recognition rate is impacted by external factors such as vehicle cabin noise, road noise, and internal factors which are a function of the voice engine in the system itself. This paper analyzes the VR performance under the effect of two external factors, vehicle cabin noise and the speakers’ speech patterns based on gender. It also compares performance of mid-level sedans from different manufacturers.

Fuel tank in vehicle must hold the fuel in a stable way under any driving condition. However, the fuel tank might not conserve the fuel firmly in case a crack emerged while the fuel tank is exposed to different driving condition. Basically, when the engine is in purging at a normal ambient temperature before fuel boiling, the pressure inside the fuel tank decreases. However, the pressure inside a fuel tank increases while a vehicle is driven at extreme hot ambient temperature as fuel is boiling. This repetitive pressure change in the fuel tank comes with fuel tank’s physical expansion and shrink, which would cause a damage to the fuel tank. The main purpose of this research is to investigate the root cause of why fuel tank cracks at a fatigue point. We also aim to set up the method of how to test durability of the fuel tank in association with the pressure inside the tank.

An integrated starter and generator (ISG) is a type of electric machine which is mechanically connected to an internal combustion engine (ICE). The ISG is intended to conduct important roles in the hybrid electric vehicle (HEV) such as engine start and stop. Since the HEV has frequent electric vehicle (EV)/HEV mode transition, rapid engine cranking and vibration-free engine stop controls are necessary. In the case of the engine stop, the ISG provides the negative torque output to the ICE which can rapidly escape from its resonance speed. However, the ISG torque is determined by engineering intuition, the amount of energy recovery is hardly considered. Dynamic programming (DP) is an effective solution to find optimal ISG control strategy to maximize energy recovery during engine stop transition. Even though DP is an offline algorithm, the result can be used as a reference to evaluate and improve an existing on-line algorithm.

Electro-Mechanical Brake (EMB) is the brake system that is actuated by electrical energy and has a similar design with the Electric Parking Brake (EPB). It uses motor power and gears to provide the necessary torque and a screw & nut mechanism is used to convert the rotational movement into a translational one. The main difference of EMB compared with EPB is that the functional requirements of components are much higher to provide the necessary performance for service braking such as response time. Such highly responsive and independent brake actuators at each wheel lead to enhanced controllability which should result in not only better basic braking performance, but also improvements in various active braking functions such as integrated chassis control, driver assistance systems, or cooperative regenerative braking.

This research was to model a 6×4 tractor-trailer rig using TruckSim and simulate severe braking maneuvers with hardware in the loop and software in the loop simulations. For the hardware in the loop simulation (HIL), the tractor model was integrated with a 4s4m anti-lock braking system (ABS) and straight line braking tests were conducted. In developing the model, over 100 vehicle parameters were acquired from a real production tractor and entered into TruckSim. For the HIL simulation, the hardware consisted of a 4s4m ABS braking system with six brake chambers, four modulators, a treadle and an electronic control unit (ECU). A dSPACE simulator was used as the “interface” between the TruckSim computer model and the hardware.

A variety of methodologies to use embedded multicore controllers efficiently has been discussed in the last years. Several assumptions are usually made in the automotive domain, such as static assignment of tasks to the cores. This paper shows an approach for efficient task allocation depending on different system modes. An engine management system (EMS) is used as application example, and the performance improvement compared to static allocation is assessed. The paper is structured as follows: First the control algorithms for the EMS will be classified according to operating modes. The classified algorithms will be allocated to the cores, depending on the operating mode. We identify mode transition points, allowing a reliable switch without neglecting timing requirements. As a next step, it will be shown that a load distribution by mode-dependent task allocation would be better balanced than a static task allocation.

Numerical durability analysis is the only approach that can be used to assess the durability of vehicles in early stages of development. In these stages, where there are no physical prototypes available, the road wheel forces (or spindle forces) for durability testing on Belgian PG (Proving Ground) must be predicted by VPG (Virtual Proving Ground) or derived from the measured forces of predecessor vehicles. In addition, the tuning parts and geometry are not fixed at these stages. This results in the variation of spindle forces during the development stages. Therefore, it is not reasonable to choose the forces predicted at a specific tuning condition as standard forces. It is more reasonable to determine the standard forces stochastically using the DB of the measured forces of predecessor vehicles. The spindle forces measured or predicted on Belgian PG are typically stationary random.

This paper presents an adaptation method of equivalent factor in equivalent consumption minimization strategy (ECMS) of fuel cell hybrid electric vehicle (FCHEV) using hilly road information. Instantaneous optimization approach such as ECMS is one of real-time controllers. Furthermore, it is widely accepted that ECMS achieves near-optimum results with the selection of the appropriate equivalent factor. However, a lack of hilly road information no longer guarantees near-optimum results as well as charge-sustaining of ECMS under hilly road conditions. In this paper, first, an optimal control problem is formulated to derive ECMS analytical solution based on simplified models. Then, we proposed updating method of equivalent factor based on sensitivity analysis. The proposed method tries to mimic the globally optimal equivalent factor trajectory extracted from dynamic programming solutions.

Fluctuation in the sound pressure level of the interior noise of an on-road vehicle is always caused by unpredictable factors such as wind gusts, traffic, roadside obstacles, and changing drive-by-drive conditions, and is hence, not reproducible in nature. Since the human brain is known to be more sensitive to noise that is amplitude-modulated than noise at a steady level [1], it is important to evaluate and improve the NVH performance of a vehicle in terms of the fluctuating interior noise likely to be experienced by drivers or users. To this end, an evaluation system was developed as part of this study, the details of which are presented in this paper. The system is composed of hardware for database storage and replay of sounds, and software for synthesizing the noise signals. For given wind tunnel test results, the evaluation system yields a wind noise model that can synthesize wind noise signals for any wind scenario.

This work studied the control technique for the engine clutch engagement at launch for the TMED parallel HEV for the improved drivability and dynamic performance. Analysis are done on the speed synchronization of the clutch plates, the speed control using the starter motor (ISG), and the fluid pressure control for the clutch. Possible external factors such as changes in the friction coefficient of transmission fluid, temperature variation, auxiliary power and pressure losses are identified and their effects on the targeted dynamic performance are examined. The targeted system performance was achieved with a learning control technique using fluid pressure as the only control input. This involves the compensation for the effect of external factors on the fluid pressure profile and this effect is memorized for the subsequent slip-launch application.

The efforts to improve automatic transmission (AT) efficiency for vehicle fuel economy are constantly continuing. In an AT the oil pump is the largest power loss factor. Therefore the effect on fuel economy is very high. The AT oil pump system has structural contradictions (high pressure × high flow), and the efforts to improve these areas are concentrated. In this paper, a two oil pumping system was designed to improve the efficiency and performance of a 6 speed AT installed in a Hybrid Electric Vehicle (HEV) [1], and the improvement was confirmed by a prototype experiment. As a result of the experiment, two pumping system was shown to improve vehicle fuel economy while reducing noise and oil pressure vibration.

Development of eco-friendly vehicles have risen in importance due to fossil fuel depletion and the strengthened globalized emission control regulatory requirements. A lot of automotive companies have already developed and launched various types of eco-friendly vehicles which include hybrid vehicles (HEVs) or electric vehicles (EVs) to reduce fuel consumption. To maximize fuel economy Hyundai-Kia Motor Company has introduced eco-friendly vehicles which have downsized or eliminated vibration damping components such as a torque converter. Comparing with Internal Combustion Engine(ICE) powered vehicles, one issue of the electric motor propulsion system with minimized vibration damping components is NVH (Noise, Vibration and Harshness). The NVH problem is caused by output torque fluctuation of the motor system, resulting in the degradation of ride comfort and drivability.

This paper proposes an effective nonlinear bicycle model including longitudinal, lateral, and yaw motions of a vehicle. This bicycle model uses a simplified piece-wise linear tire model and tire force tuning algorithm to produce closely matching vehicle trajectory compared to real vehicle for wide vehicle operation ranges. A simplified piece-wise tire model that well represents nonlinear tire forces was developed. The key parameters of this model can be chosen from measured tire forces. For the effects of dynamic load transfer due to sharp vehicle maneuvers, a tire force tuning algorithm that dynamically adjusts tire forces of the bicycle model based on measured vehicle lateral acceleration is proposed. Responses of the proposed bicycle model have been compared with commercial vehicle dynamics model (CarSim) through simulation in various vehicle maneuvers (ramp steer, sine-with-dwell).

The worldwide automotive industry is currently preparing for a market introduction of hydrogen-fueled powertrains. These powertrains in fuel cell electric vehicles (FCEVs) offer many advantages: high efficiency, zero tailpipe emissions, reduced greenhouse gas footprint, and use of domestic and renewable energy sources. To realize these benefits, hydrogen vehicles must be competitive with conventional vehicles with regards to fueling time and vehicle range. A key to maximizing the vehicle's driving range is to ensure that the fueling process achieves a complete fill to the rated Compressed Hydrogen Storage System (CHSS) capacity. An optimal process will safely transfer the maximum amount of hydrogen to the vehicle in the shortest amount of time, while staying within the prescribed pressure, temperature, and density limits. The SAE J2601 light duty vehicle fueling standard has been developed to meet these performance objectives under all practical conditions.

Currently, the interest in accelerated reliability testing (ART) of electric vehicles parts has been increasing. In particular, an electric motor and battery are vital components of battery powered electric vehicles. The electric motor has two major roles, to discharge or charge battery when it is driven or braking. For analyzing the exact behavior mechanism of electric motor and predicting lithium-ion battery cell degradation, new accelerated reliability testing technology is required. This paper describes the results of research and development in new approach to reliability testing for electric vehicles. The methodology to measure a precise motor output torque of the rotating rotor using telemetry system was provided. The electric energy quantities as well as the used quantities of the electric power were also analyzed. The results of research and development in new approach to reliability testing for electric vehicles were systematized and reflected in development.