• Journal of Software Engineering Society
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• v.27 no.1
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• pp.1-7
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• 2018

The consumption of mobile device batteries which are limited resources is an important criterion when circuit designers analyze and evaluate circuits. For this reason, researchers conducted researches with different models of battery consumption to analyze power consumption of mobile devices. The battery consumption model generation techniques have various characteristics depending on availability of sensors, run-time model generation, and models for using in verification and testing. However, there is lack of comparison and analysis between varied battery consumption model generation methods. In this research, we compare and evaluate the analysis methods which have been studied so far to support the circuit investigation for circuit designers. Finally, we suggest the direction of researches in battery consumption analysis using the comparison result.

• 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.

• Transactions on Electrical and Electronic Materials
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• v.18 no.5
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• pp.237-245
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• 2017

A novel battery model based on the manufacturer datasheet is proposed. According to this model, not only the steady state but also the dynamic charging performance of the Li-ion battery can be analyzed and evaluated. The major advantage of our model is that all the parameters can be directly obtained from the datasheet and no additional experiments are required. Moreover, the transition between charge and discharge stages was analyzed based on our model, and a novel Simulink module was built to predict the energy consumption of a battery-powered system. Experiments were carried out to verify the model accuracy. Although the new model was developed for the Li-ion battery, it is expected to be applicable to other batteries.

• Journal of Advanced Marine Engineering and Technology
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• v.32 no.5
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• pp.677-683
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• 2008

The electric propulsion system us closely related with the economical efficiency of ship operation. Fuel efficiency is mainly decided by propulsion system such as diesel engine, propulsion motor and steam turbine. The hybrid propulsion system for fishing boat consists of diesel engine and battery as propulsion power source. This paper is to design battery capacity according to power consumption with ship operation condition, and to test the power consumption of model ship in the circulating water channel. As a result. it can be known that the optimum ship operation condition affects the fuel efficiency.

• 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.

• Journal of Korea Society of Industrial Information Systems
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• v.28 no.1
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• pp.17-25
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• 2023

As the range of use of urban unmanned aerial vehicles (UAV) expands, it is necessary to operate UAVs efficiently because of its limited battery capacity. For this, it is required to find the optimal flight profile with various simulations. Therefore, it is important to predict the power and energy consumption of the UAV battery. In this paper, we analyzed the relationship between the speed and acceleration of the UAV and power consumption during the flight. Then, we derived a linear model, which is easily utilized. In addition, we also derived an accurate power consumption model based on deep neural network learning. To find the efficient model, we used learning data as 1) the GPS 3-axis velocity and acceleration data, 2) the IMU 3-axis velocity only, and 3) the IMU 3-axis velocity and acceleration data. The final model shows 5.86% error rate for power consumption and 1.50% error rate for the cumulative energy consumption.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.38 no.3
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• pp.227-234
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• 2014

Fuel consumption rates of electric vehicles strongly depend on their battery performance. Because the battery performance is sensitive to the operating temperature, temperature management of the battery ensures its performance and durability. In particular, the temperature distribution among modules in the battery pack affects the cooling characteristics. This study focuses on the thermal modeling of a battery pack to observe the temperature distribution among the modules. The battery model is a prismatic model of 10 NiMH battery modules. The thermal model of the battery consists of heat generation, convective heat transfer through the channel and conduction heat transfer among modules. The heat generation is calculated by the electric resistance heat during the charge/discharge state. The model is used to determine a strategy for proper thermal management in Electric vehicles.

• Transactions of the Korean Society of Automotive Engineers
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• v.23 no.3
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• pp.326-335
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• 2015

In this paper, is a study that analyzed the fuel consumption of hybrid tractor. Testing and analysis in order to evaluate the fuel consumption was performed. Analysis model was developed by using the SimulationX that is a commercial software. Also, map of the analysis model was modeled on the basis of test data. Test was performed using a dynamo device. The engine was tested the fuel consumption in accordance with the conditions on the load and throttle opening. The battery was tested the discharge and charge in accordance with the current amount. We verified the reliability of the analysis model by comparing the analysis results with the rest results. After considering the reliability of each analysis model was extended to the entire hybrid tractor system. To evaluate the efficiency using the analysis model, compared the fuel consumption of general tractor with hybrid tractor in the same load conditions.

• KIPS Transactions on Computer and Communication Systems
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• v.3 no.11
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• pp.419-426
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• 2014

The recent trend of performance increase in the smart mobile devices demands more power consumption and lower batter life time. Among three battery models of mathematical model, electrochemical model and electric model, the Thevenin's equivalent circuit with non-linear function model of SOC in the electrical model is widely used. However, the OCV results have only limited accuracy because of the characteristic shift caused by temperature and age and atypical impedance property that cannot expressed by electrical components. In this paper, the new battery model that improves the accuracy of the existing models is proposed. In the proposed simulator the mathematical model for SOC characteristic is improved and the adjustment for the temperature, the age of battery and atypical electrical characteristics. In the experimental results of predicting of the battery in the static and dynamic state, the proposed simulator shows improved MSE comparing to the results of the existing methods.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.11
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• pp.2418-2423
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• 2012

A smart phone which is one of the new IT technologies can be viable on various enterprise applications since it provides easily information and services. Especially, a smart phone can request queries to a server which finds massive data for decision making and receive the results of the queries from the server. However, a smart phone has a drawback that its battery is consumed easily due to the 4G communication. In this paper, we propose mobile views in order to reduce the easy consumption of a battery on mobile client-server environment. The proposed mobile view scheme reduces the number of communication which is leading to power consumption using materialized views for processing queries. We also classify the methods to control the consistency of the mobile view as the sources of the view are changed and present the cost of each method based on the cost model of battery consumption.