• Journal of the Korean Electrochemical Society
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• v.11 no.3
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• pp.197-210
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• 2008

The cathode materials for lithium ion battery have been developed in accordance with the battery performance. $LiCoO_2$ initially adapted at lithium ion battery is going to be useful even at the charging voltage of 4.3 V by surface treatment or doping which drastically improved the performance of $LiCoO_2$. On the other hand, the complicate and multiple functions of recent electronic equipments required higher operational voltage and higher capacity than ever, which is going to be driving force for developing new cathode materials. Some of them are $LiNi_{1-x}{M_xO_2}$, $Li[Ni_{x}Mn_{y}Co_{z}]O_{2}$, $Li[{Ni}_{1/2}{Mn}_{1/2}]O_{2}$. Other new type of cathode materials having high safety is also developed to apply for HEV (hybrid electrical vehicle) and power tool applications. ${LiMn}_{2}{O}_{4}$ and $LiFePO_4$ are famous for highly stable material, which are expected to give contribution to make safer battery. In near future, the various materials having both capacity and safety will be developed by new technology, such as solid solution composite.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.31 no.1 s.256
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• pp.26-35
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• 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
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• v.10 no.1
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• pp.20-24
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• 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.

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