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VOL V L S I O P T I M I Z A T I O N L A B O |
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R I T S U M E I K A N U N I V E R S I T Y |
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VOL V L S I O P T I M I Z A T I O N L A B O |
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R I T S U M E I K A N U N I V E R S I T Y |
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Research Themes |
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Aiming at low carbon society, it is important that smart houses spread for efficient utilization of natural energy sources, e.g., photovoltaic battery. Lithium-ion battery is an important component to construct those systems. We discuss how to formulate the degradation of the batteries, and clarifies the efficient keys to control the cost of battery degradation. Furthermore, mobile systems with lithium-ion batteries have become very popular. Higher performance, longer battery life, and safer operation are required strongly. We are developing an efficient battery management and control system to get the highest performance and highest reliability.
Battery Smart Sensor for Smart Grid: In a smart grid, a storage battery is an essential device for electric vehicles and renewable energy. But optimal management of the storage battery is difficult, because of manufacturing variations and degradation. It is important for battery optimal management to understand degradation and characteristics of the battery. So, We propose a battery management by the battery smart sensor. The battery smart sensor is based on IEEE1888 communication standard, and this system monitors a battery voltage, current, temperature and SOC (State-of-charge) on the Web.
Practical and Accurate SOC Estimation System for Lithium Ion Batteries: With the advent of big-scale popularization of secondary batteries, the accuracy as well as the inexpensiveness should be indispensable for measurement systems. Particularly, the SOC (state of charge) estimation is essential for capturing the state of secondary batteries. A precise SOC estimation system is devised by means of EKF (Extended Karman Filter) run on a microcomputer. Compared with the conventional techniques, such as OCV, internal resistance method, current accumulation method, etc., EKF attains higher accuracy. In fact, EKF is a statistical method which minimizes over time the gap between the prediction and the observation, on the basis of the information acquired from observation of the physical variable with SOC dependence, such as OCV and internal resistance.
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