• 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.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.49 no.12
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• pp.989-996
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• 2021

In the case of an unmanned aerial vehicle using a battery as a power source, there are restrictions in performing the mission because the battery capacity is limited. To extend the mission capability, it is important to minimize battery usage while the flight to the mission area. In addition, by using the battery usage prediction model, the possibility of mission completeness can be determined and it can be a criterion for selecting an emergent landing point in the mission planning stage. In this paper, we propose a battery usage prediction model considering as one of the environmental factors in the three-dimensional space. The required power is calculated according to the flight geometry of an unmanned aerial vehicle. True battery usage which is predicted from the required power is verified through the comparison with the battery usage prediction model. The optimal flight trajectory that minimizes battery usage is produced and compared with the shortest travel distance.

• Journal of Information Processing Systems
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• v.12 no.3
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• pp.436-454
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• 2016

This research presents the battery discharge rate models for the energy consumption of mobile phone batteries based on machine learning by taking into account three usage patterns of the phone: the standby state, video playing, and web browsing. We present the experimental design methodology for collecting data, preprocessing, model construction, and parameter selections. The data is collected based on the HTC One X hardware platform. We considered various setting factors, such as Bluetooth, brightness, 3G, GPS, Wi-Fi, and Sync. The battery levels for each possible state vector were measured, and then we constructed the battery prediction model using different regression functions based on the collected data. The accuracy of the constructed models using the multi-layer perceptron (MLP) and the support vector machine (SVM) were compared using varying kernel functions. Various parameters for MLP and SVM were considered. The measurement of prediction efficiency was done by the mean absolute error (MAE) and the root mean squared error (RMSE). The experiments showed that the MLP with linear regression performs well overall, while the SVM with the polynomial kernel function based on the linear regression gives a low MAE and RMSE. As a result, we were able to demonstrate how to apply the derived model to predict the remaining battery charge.

• 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.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.3
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• pp.133-140
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• 2023

The performance of lithium ion batteries depends on the usage environment and the combination ratio of cathode materials. In order to develop a high-performance lithium-ion battery, it is necessary to manufacture the battery and measure its performance while varying the cathode material ratio. However, it takes a lot of time and money to directly develop batteries and measure their performance for all combinations of variables. Therefore, research to predict the performance of a battery using an artificial intelligence model has been actively conducted. However, since measurement experiments were conducted with the same battery in the existing published battery data, the cathode material combination ratio was fixed and was not included as a data attribute. In this paper, we define a training data model required to develop an artificial intelligence model that can predict battery performance according to the combination ratio of cathode materials. We analyzed the factors that can affect the performance of lithium-ion batteries and defined the mass of each cathode material and battery usage environment (cycle, current, temperature, time) as input data and the battery power and capacity as target data. In the battery data in different experimental environments, each battery data maintained a unique pattern, and the battery classification model showed that each battery was classified with an error of about 2%.

• Journal of Intelligence and Information Systems
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• v.24 no.2
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• pp.243-264
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• 2018

Although the worldwide battery market is recently spurring the development of lithium secondary battery, lead acid batteries (rechargeable batteries) which have good-performance and can be reused are consumed in a wide range of industry fields. However, lead-acid batteries have a serious problem in that deterioration of a battery makes progress quickly in the presence of that degradation of only one cell among several cells which is packed in a battery begins. To overcome this problem, previous researches have attempted to identify the mechanism of deterioration of a battery in many ways. However, most of previous researches have used data obtained in a laboratory to analyze the mechanism of deterioration of a battery but not used data obtained in a real world. The usage of real data can increase the feasibility and the applicability of the findings of a research. Therefore, this study aims to develop a model which predicts the battery deterioration using data obtained in real world. To this end, we collected data which presents change of battery state by attaching sensors enabling to monitor the battery condition in real time to dozens of golf carts operated in the real golf field. As a result, total 16,883 samples were obtained. And then, we developed a model which predicts a precursor phenomenon representing deterioration of a battery by analyzing the data collected from the sensors using machine learning techniques. As initial independent variables, we used 1) inbound time of a cart, 2) outbound time of a cart, 3) duration(from outbound time to charge time), 4) charge amount, 5) used amount, 6) charge efficiency, 7) lowest temperature of battery cell 1 to 6, 8) lowest voltage of battery cell 1 to 6, 9) highest voltage of battery cell 1 to 6, 10) voltage of battery cell 1 to 6 at the beginning of operation, 11) voltage of battery cell 1 to 6 at the end of charge, 12) used amount of battery cell 1 to 6 during operation, 13) used amount of battery during operation(Max-Min), 14) duration of battery use, and 15) highest current during operation. Since the values of the independent variables, lowest temperature of battery cell 1 to 6, lowest voltage of battery cell 1 to 6, highest voltage of battery cell 1 to 6, voltage of battery cell 1 to 6 at the beginning of operation, voltage of battery cell 1 to 6 at the end of charge, and used amount of battery cell 1 to 6 during operation are similar to that of each battery cell, we conducted principal component analysis using verimax orthogonal rotation in order to mitigate the multiple collinearity problem. According to the results, we made new variables by averaging the values of independent variables clustered together, and used them as final independent variables instead of origin variables, thereby reducing the dimension. We used decision tree, logistic regression, Bayesian network as algorithms for building prediction models. And also, we built prediction models using the bagging of each of them, the boosting of each of them, and RandomForest. Experimental results show that the prediction model using the bagging of decision tree yields the best accuracy of 89.3923%. This study has some limitations in that the additional variables which affect the deterioration of battery such as weather (temperature, humidity) and driving habits, did not considered, therefore, we would like to consider the them in the future research. However, the battery deterioration prediction model proposed in the present study is expected to enable effective and efficient management of battery used in the real filed by dramatically and to reduce the cost caused by not detecting battery deterioration accordingly.

• 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 Environmental Science International
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• v.33 no.7
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• pp.523-532
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• 2024

In popular tourist destinations such as Jeju and Gangwon, electric rental cars are increasingly adopted. However, sudden battery drain due to weather conditions can pose safety issues. To address this, we developed a battery consumption analysis model that considers resistive energy factors such as acceleration, rolling resistance, and aerodynamic drag. Focusing on the effects of ambient temperature and wind speed, the model's performance was evaluated during an empirical validation period from November to December 2023. Comparing predicted and actual state of charge (SoC) across different routes identified ambient temperature, wind speed, and driving time as major sources of error. The mean absolute error (MAE) increased with lower temperatures due to reduced battery efficiency. Higher wind speeds on routes 1 and 6 resulted in larger errors, indicating the model's limitation in considering only tailwinds for aerodynamic drag calculations. Additionally, longer driving times led to higher actual SoC than predicted, suggesting the need to account for varying driver habits influenced by road conditions. Our model, providing more accurate SoC predictions to prevent battery depletion incidents, shows high potential for application in navigation apps for electric vehicle users in tourist areas. Future research should endeavor to the model by including wind direction, HVAC system usage, and braking frequency to improve prediction accuracy further.

• 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.

• 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.