• Journal of Computing Science and Engineering
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• v.5 no.4
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• pp.338-345
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• 2011

Nowadays mobile devices are used for various applications such as making voice/video calls, browsing the Internet, listening to music etc. The average battery consumption of each of these activities and the length of time a user spends on each one determines the battery lifetime of a mobile device. Previous methods have provided predictions of battery lifetime using a static battery consumption rate that does not consider user characteristics. This paper proposes an approach to predict a mobile device's available battery lifetime based on usage patterns. Because every user has a different pattern of voice calls, data communication, and video call usage, we can use such usage patterns for personalized prediction of battery lifetime. Firstly, we define one or more states that affect battery consumption. Then, we record time-series log data related to battery consumption and the use time of each state. We calculate the average battery consumption rate for each state and determine the usage pattern based on the time-series data. Finally, we predict the available battery time based on the average battery consumption rate for each state and the usage pattern. We also present the experimental trials used to validate our approach in the real world.

• Proceedings of the KIPE Conference
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• 2016.07a
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• pp.423-424
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• 2016

Recently, the mass production of Energy storage system (ESS) is actively perform around world. Energy storage system is a technique that stores power to energy storage device to supply energy into grid and load at peak-load. Therefore, the efficient energy management is available by using ESS system. The life of Lithium-ion battery is varied corresponding to the power usage, especially selected depth of discharge (DOD). The lifetime of battery is the one of the most issue of the ESS system because of its stability and reliability. Therefore, lifetime management of battery and power converter of ESS module is required. In this paper, the battery lifetime management method estimating residual power and lifetime of lithium ion battery of ESS system is proposed. Also, total avenue prediction of ESS system is simulated considering the total lifetime of battery.

• Journal of Electrical Engineering and Technology
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• v.13 no.3
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• pp.1173-1184
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• 2018

The lifetime of a lithium-ion battery is one of the most important issues of the energy storage system (ESS) because of its stable and reliable operation. In this paper, the lifetime management method of the lithium-ion battery for energy storage system is proposed. The lifetime of the lithium-ion battery varies, depending on the power usage, operation condition, and, especially the selected depth of discharge (DOD). The proposed method estimates the total lifetime of the lithium-ion battery by calculating the total transferable energy corresponding to the selected DOD and achievable cycle (ACC) data. It is also demonstrated that the battery model can obtain state of charge (SOC) corresponding to the ESS operation simultaneously. The simulation results are presented performing the proposed lifetime management method. Also, the total revenue and entire lifetime prediction of a lithium-ion battery of ESS are presented considering the DOD, operation and various condition for the nations of USA and Korea using the proposed method.

• Journal of IKEEE
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• v.18 no.1
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• pp.96-105
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• 2014

This paper proposes a battery lifetime enhancement method based on the nonlinear discharge charisteristics called recovery effect. In general, the stored energy in a battery is considered in the prediction of battery lifetime. However, due to the chemical reaction in a battery, more energy can be drawn from a battery when it is not continuously but intermittently discharged, which is called recovery effect. In the proposed method, several battery cells are alternately discharged, and some battery cells rest while maintaining the system power supply. This makes recovery effect of battery cells, which extends battery lifetime. In the experiment, battery lifetime increases about 7% in the alternating discharge of two battery cells, when compared with conventional parallel discharge.

• Journal of the Semiconductor & Display Technology
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• v.10 no.4
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• pp.25-29
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• 2011

Emerging demands for rechargeable battery for various applications needs more effective battery management system such as the prediction of the usable time about a battery. Many prediction methods have been suggested but none of them come into bounds of reliability. In this paper, we proposed a new prediction algorithm for the remaining capacity of a rechargeable battery by using the transformed curve based on its impedance. Hardware for monitoring a battery was designed and made. Through a series of experiment, we showed the effectiveness of the proposed prediction algorithm of a battery's remaining capacity.

• Journal of Energy Engineering
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• v.22 no.4
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• pp.338-346
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• 2013

The conventional lead acid battery is optimized for cranking performance of engine. Recently electric devices and fuel economy technologies of battery have influenced more deep cycle of dynamic behavior of battery. I also causes to reduce battery life-time. This study proposed that aging battery model is focused for increasing of battery durability. The stress factors of battery aging consist of discharge rate, charging time, full charging time and temperature. This paper considers the electrochemical kinetics, the ionic species conservation, and electrode porosity. For prediction of battery life cycle we consider battery model containing strong impacts, corrosion of positive grid and shedding. Finally, we validated that modeling results were compared with the accelerated thermal measurement data.

• Journal of Internet Computing and Services
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• v.9 no.5
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• pp.165-173
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• 2008

Hybrid ad hoc networks are integrated networks referred to Home Networks, Telematics and Sensor networks can offer various services. Specially, in ad hoc network where each node is responsible for forwarding neighbor nodes' data packets, it should net only reduce the overall energy consumption but also balance individual battery power. Unbalanced energy usage will result in earlier node failure in overloaded nodes. it leads to network partitioning and reduces network lifetime. Therefore, this paper studied the routing protocol considering efficiency of energy. The suggested algorithm can predict the status of energy in each node using the energy prediction model. This can reduce the overload of establishing route path and balance individual battery power. The suggested algorithm can reduce power consumption as well as increase network lifetime.

• International Journal of Internet, Broadcasting and Communication
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• v.16 no.3
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• pp.236-241
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• 2024

Due to the recent emphasis on carbon neutrality and environmental regulations, the global electric vehicle (EV) market is experiencing rapid growth. This surge has raised concerns about the recycling and disposal methods for EV batteries. Unlike traditional internal combustion engine vehicles, EVs require unique and safe methods for the recovery and disposal of their batteries. In this process, predicting the lifespan of the battery is essential. Impedance and State of Charge (SOC) analysis are commonly used methods for this purpose. However, predicting the lifespan of batteries with complex chemical characteristics through electrical measurements presents significant challenges. To enhance the accuracy and precision of existing measurement methods, this paper proposes using a Long Short-Term Memory (LSTM) model, a type of deep learning-based recurrent neural network, to diagnose battery performance. The goal is to achieve safe classification through this model. The designed structure was evaluated, yielding results with a Mean Absolute Error (MAE) of 0.8451, a Root Mean Square Error (RMSE) of 1.3448, and an accuracy of 0.984, demonstrating excellent performance.

• Journal of Power Electronics
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• v.16 no.1
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• pp.217-226
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• 2016

A new method is proposed based on a hidden Markov model (HMM) to estimate and analyze battery states of health. Battery system health states are defined according to the relationship between internal resistance and lifetime of cells. The source data (terminal voltages and currents) can be obtained from vehicular battery models. A characteristic value extraction method is proposed for HMM. A recognition framework and testing datasets are built to test the estimation rates of different states. Test results show that the estimation rates achieved based on this method are above 90% under single conditions. The method achieves the same results under hybrid conditions. We can also use the HMMs that correspond to hybrid conditions to estimate the states under a single condition. Therefore, this method can achieve the purpose of the study in estimating battery life states. Only voltage and current are used in this method, thereby establishing its simplicity compared with other methods. The batteries can also be tested online, and the method can be used for online prediction.

• Journal of Advanced Marine Engineering and Technology
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• v.39 no.7
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• pp.754-759
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• 2015

Nowadays, short distance communication systems with low power energy (LPE) are developed for identification and monitoring of site identification of vessel crews and passengers (SIVCP). LPE communication modules, such as Bluetooth low energy (BLE) and Zigbee, are used for short distance communications with LPE. These modules enable 1:N communications and their popularity is growing since the modules can be mounted on movable objects, such as mobile devices and human body. When these modules are used, the important factor that affects their operation time and design are the capacity and size of battery. Therefore, they must be made as small as possible, and the battery should be selected to be slightly smaller than the module. In this study, we calculate the theoretical life of batteries used in SIVCP BLE modules using data sheet and discharge characteristic graph under the condition of a 1/250 transmission-ratio (TR). We thus calculate experimental life by measuring transmission current for the same TR, and low speed mode current for a 1/5000 TR and measure long-term experimental life using 1/25 TR for days. Through these experiments, we verify experimental methods for the prediction and extension of battery life that would enable us to select appropriate sizes of batteries based on vessel usage and passenger types. The selections of the module TR and battery size are important factors affecting the cost reduction of module design, the battery maintenance, and passenger convenience.