• Korean Journal of Materials Research
• /
• v.31 no.2
• /
• pp.75-83
• /
• 2021

To cope with automobile exhaust gas regulations, ISG (Idling Stop & Go) and charging control systems are applied to HEVs (Hybrid Electric Vehicle) for the purpose of improving fuel economy. These systems require quick charge/discharge performance at high current. To satisfy this characteristic, improvement of the positive electrode plate is studied to improve the charge/discharge process and performance of AGM(Absorbent Glass Mat) lead-acid batteries applied to ISG automotive systems. The bonding between grid and A.M (Active Material) can be improved by applying the Sand-Blasting method to provide roughness to the surface of the positive grid. When the Sand-Blasting method is applied with conditions of ball speed 1,000 rpm and conveyor speed 5 M/min, ideal bonding is achieved between grid and A.M. The positive plate of each condition is applied to the AGM LAB (Absorbent Glass Mat Lead Acid Battery); then, the performance and ISG life characteristics are tested by the vehicle battery test method. In CCA, which evaluates the starting performance at -18 ℃ and 30 ℃ with high current, the advanced AGM LAB improves about 25 %. At 0 ℃ CA (Charge Acceptance), the initial charging current of the advanced AGM LAB increases about 25 %. Improving the bonding between the grid and A.M. by roughening the grid surface improves the flow of current and lowers the resistance, which is considered to have a significant effect on the high current charging/discharging area. In a Standard of Battery Association of Japan (SBA) S0101 test, after 300 A discharge, the voltage of the advanced AGM LAB with the Sand-Blasting method grid was 0.059 V higher than that of untreated grid. As the cycle progresses, the gap widens to 0.13 V at the point of 10,800 cycles. As the bonding between grid and A.M. increases through the Sand Blasting method, the slope of the discharge voltage declines gradually as the cycle progresses, showing excellent battery life characteristics. It is believed that system will exhibit excellent characteristics in the vehicle environment of the ISG system, in which charge/discharge occurs over a short time.

• Journal of the Korean Society of Manufacturing Process Engineers
• /
• v.14 no.2
• /
• pp.14-18
• /
• 2015

In recent years, world is faced with a transportation energy dilemma, and the transportation is almost dependent on a single fuel - petroleum. However, Hybrid Electric Vehicle (HEV) technology holds more advantages to reduce the demand for petroleum in the transportation by efficiency improvements of petroleum consumption. Therefore, there is a trend that lower gear noise levels are demanded in HEV for drivers to avoid annoyance and fatigue during operation. And meshing transmission error (T.E.) is the excitation that leads to the tonal noise known as gear whine, and radiated gear whine is also the dominant source of noise in the whole gearbox. In this paper, the analysis of gear tooth profile and lead modification is firstly presented, and then, the different transmission error of no mesh misalignment and mesh misalignment under one loaded torque for the 1st gear pair of HEV gearbox was investigated and compared. At last, the appropriate tooth modification was used to minimize and compare the transmission error of the gear pair with mesh misalignment under the loaded torque.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
• /
• 2007.11a
• /
• pp.313-318
• /
• 2007

Interior permanent magnet synchronous motor(IPMSM) has become a popular choice in electric vehicle applications, due to their excellent power to weight ratio. This paper proposes maximum torque control of IPMSM drive using adaptive fuzzy neural network controller and artificial neural network(ANN). This control method is applicable over the entire speed range which considered the limits of the inverter's current and voltage rated value. For each control mode, a condition that determines the optimal d-axis current $i_d$ for maximum torque operation is derived. This paper considers the design and implementation of novel technique of high performance speed control for IPMSM using Adaptive-FNN controller and ANN controller. The hybrid combination of neural network and fuzzy control will produce a powerful representation flexibility and numerical processing capability. Also, this paper reposes speed control of IPMSM using Adaptive-FNN and estimation of speed using ANN controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The proposed control algorithm is a lied to IPMSM drive system controlled Adaptive-FNN and ANN controller, the operating characteristics controlled by maximum torque control are examined in detail. Also, this paper proposes the analysis results to verify the effectiveness of the Adaptive-FNN and ANN controller.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.20 no.2
• /
• pp.24-28
• /
• 2006

Electrochemical hydrogenation/dehydrogenation properties were studied for a single particle of a Mm-based(Mm : misch metal) hydrogen storage alloy($MmNi_{3.55}Co_{0.75}Mn_{0.4}Al_{0.3}$) for the anode of Ni-MH batteries. A carbon fiber microelectrode was manipulated to make electrical contact with an alloy particle, and the cyclic voltammetry and the galvanostatic charge/discharge experiments were performed. A single particle of the alloy showed the discharge capacity of 280[mAh/g], the value being 90[%] of the theoretical capacity. Data were compared with that of the composite film consisting of the alloy particles and a polymer binder, which is more practical form for Ni-MH batteries. Additionally, pulverization of the alloy particles are directly observed. Compared with the conventional composite film electrodes, the single particle measurements using the microelectrode gave more detailed, true information about the hydrogen storage alloy.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.31 no.1 s.256
• /
• pp.26-35
• /
• 2007

The vehicle of urban transit is a complex system that consists of various electric, electronic, and mechanical equipments, and the maintenance cost of this complex and large-scale system generally occupies sixty percent of the LCC (Life Cycle Cost). For reasonable establishing of maintenance strategies, safety security and cost limitation must be considered at the same time. The concept of system reliability has been introduced and optimized as the key of reasonable maintenance strategies. For optimization, three preceding studies were accomplished; standardizing a maintenance classification, constructing RBD (Reliability Block Diagram) of VVVF (Variable Voltage Variable Frequency) urban transit, and developing a web based reliability evaluation system. Historical maintenance data in terms of reliability index can be derived from the web based reliability evaluation system. In this paper, we propose applying inverse problem analysis method and hybrid neuro-genetic algorithm to system reliability optimization for using historical maintenance data in database of web based system. Feed-forward multi-layer neural networks trained by back propagation are used to find out the relationship between several component reliability (input) and system reliability (output) of structural system. The inverse problem can be formulated by using neural network. One of the neural network training algorithms, the back propagation algorithm, can attain stable and quick convergence during training process. Genetic algorithm is used to find the minimum square error.

• Journal of the Korean Electrochemical Society
• /
• v.10 no.1
• /
• pp.20-24
• /
• 2007

Lithium ion secondary battery has been applied widely to portable devices, and has been studied for application to high power electric cell system such as power tool or hybrid electronic vehicle. The structure change of the electrodes materials occur when lithium ions move between electrodes. Neutron or X-rays can analyze the structure of electrode. The advantage of X-rays is convenient in test. However X-rays is scattered by electron cloud in atoms. Therefore, The elucidation for correct position of lithium is difficult with X-rays because lithium has small atomic weight. Neutron analysis techniques could solve this problem. In this review, We wish to discuss about structure analysis and the principle of structural characterization method using neutron beam source.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.23 no.5
• /
• pp.640-645
• /
• 2012

DC-DC converter and DC-AC inverter in a hybrid electric vehicle (HEV) generate the switching noise. It may be generated by the reverse recovery operation of the power diode in the switching circuit of the converter or the inverter. The shape of the reverse recovery region may be determined by both reverse time and recovery time in the diode. So, in this paper, the frequency spectrum of switching noise was estimated by the shape of the reverse recovery region and compared with the measured results. It shows that the meaningful region of the frequency spectrum is directly related with the reverse time.

• Journal of the Korean Electrochemical Society
• /
• v.25 no.4
• /
• pp.184-190
• /
• 2022

Spinel LiMn₂O₄ (LMO) and layered LiNi_0.5Co_0.2Mn_0.3O₂ (NCM) are widely used as positive electrode materials for lithium-ion batteries. LMO and NCM positive electrode materials have a complementary properties. LMO has low cost and high safety and NCM materials show a relatively high specific capacity and better cycle life even at elevated temperature. Therefore, the LMO and NCM active materials are blended and used as a positive electrode in large-size batteries for electric vehicles (xEV). In this study, the cycle performance of a blended electrode prepared by simply mixing LMO and NCM and a bi-layer electrode in which two electrode layers aree sequentially coated are compared. The bi-layer electrode prepared by composing the same ratio of both active materials has similar capacity and cycle performance to the blend electrode. However, the LN electrode coated with LMO first and then NCM is the best in the full cell cycle performance at elevated temperature, and the NL electrode, in which NCM is first coated with LMO has a faster capacity degradation than the blended electrode because LMO is mainly located on the top of the electrode adjacent to electrolyte and graphite negative electrode. Also, the LSTA (linear sweep thermmametry) analysis results show that the LN bi-layer electrode in which the LMO is located inside the electrode has good thermal stability.

• Journal of Technology Innovation
• /
• v.26 no.3
• /
• pp.37-68
• /
• 2018

Since the 20th century, automobiles, which are the most common means of transportation, have been evolving as the use of electronic control devices and automotive semiconductors increases dramatically. Automotive semiconductors are a key component in automotive electronic control devices and are used to provide stability, efficiency of fuel use, and stability of operation to consumers. For example, automotive semiconductors include engines control, technologies for managing electric motors, transmission control units, hybrid vehicle control, start/stop systems, electronic motor control, automotive radar and LIDAR, smart head lamps, head-up displays, lane keeping systems. As such, semiconductors are being applied to almost all electronic control devices that make up an automobile, and they are creating more effects than simply combining mechanical devices. Since automotive semiconductors have a high data rate basically, a microprocessor unit is being used instead of a micro control unit. For example, semiconductors based on ARM processors are being used in telematics, audio/video multi-medias and navigation. Automotive semiconductors require characteristics such as high reliability, durability and long-term supply, considering the period of use of the automobile for more than 10 years. The reliability of automotive semiconductors is directly linked to the safety of automobiles. The semiconductor industry uses JEDEC and AEC standards to evaluate the reliability of automotive semiconductors. In addition, the life expectancy of the product is estimated at the early stage of development and at the early stage of mass production by using the reliability test method and results that are presented as standard in the automobile industry. However, there are limitations in predicting the failure rate caused by various parameters such as customer's various conditions of use and usage time. To overcome these limitations, much research has been done in academia and industry. Among them, researches using data mining techniques have been carried out in many semiconductor fields, but application and research on automotive semiconductors have not yet been studied. In this regard, this study investigates the relationship between data generated during semiconductor assembly and package test process by using data mining technique, and uses data mining technique suitable for predicting potential failure rate using customer bad data.