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Battery Lifetime Estimation Based on Usage Pattern
Abstract
Gupta, ManuMary, ShrutiTrivedi, JayGoyal, JaiMaan, PriyankaSingh, MagendraWith the increasing number of applications and usage of mobile phones, the limited storage of battery needs to be managed properly so that the battery does not drain when it is desperately required by the user. It is important for the user to know how long her/his battery will last. There has been a significant study in the area of mobile phone battery lifetime estimation. Some researchers have studied user patterns from device logs, but only few of them have considered charging cycles into account for study. This paper proposes a new on-device approach to estimate battery lifetime based on user history, using both charging and discharging cycles. The charging cycle is processed as well to estimate how much battery has been consumed in that cycle. The proposed algorithm fetches the battery discharge governing features from user handset, processes it, and creates the history for the user. Now based on the user’s individual history, the battery lifetime is estimated. The dataset for user history has been collected from October 2019 to December 2019. We have been successfully able to estimate battery lifetime using the proposed algorithm with a root mean square difference between estimated battery lifetime and the time battery actually lasted to be 160 min for any typical user, which is better by an average of 196 min when compared to results of the existing battery lifetime estimator present in the smartphones of the participant users.
... Monitoring the battery backup time [35] of the retrofit device holds significant importance for optimizing battery life, reducing maintenance costs, and ensuring system reliability. Battery backup time refers to the duration for which a device can operate on a single charge before requiring a recharge or battery replacement. ...
Monitoring water flow helps to identify leaks and wastage, leading to better management of water resources and conservation of this precious resource. To address this challenge, there is a need for an efficient and sustainable water management system. This paper presents an Internet of Things (IoT) based solution that involves retrofitting existing analog water meters using readily available off-the-shelf electronic components. Real-time data collection and analysis are performed through edge computation, which locally processes water meter images captured by the camera and extracts water meter readings. These readings are transmitted to the cloud for storage and further analysis. Various strategies have been implemented to optimize supply-current usage, preserving charge-discharge cycles of solar-powered batteries even in adverse environmental conditions. To streamline the firmware update process for multiple connected devices, a broadcasting technique is employed, offering the benefits of reduced manual labor and time savings. To assess the reliability and performance of developed solution, field deployment is conducted over several months, enabling the characterization of water usage patterns across different locations. Integrating energy harvesting capabilities into system reduces maintenance costs and promotes eco-friendly energy practices. Overall, this solution offers an effective and comprehensive approach towards achieving efficient and sustainable water management.
... The rationale behind the trend was the reduction in the operational hours of DG which caused a greater strain on the battery, potentially shortening the lifespan. This was in line with the findings of Gupta et al. (2021) and Zhou & Howey (2022) that the cycles of charging and discharging contributed to the degradation of battery life, causing the need for early replacement (Hosen et al., 2021). Therefore, the system recommended to satisfy the minimum LCOE required was the combination of additional loads such as Load C as well as wind and diesel generators (DG) operating around the clock as proposed in System 2. This was due to the ability of the system to reduce energy losses from load integration and the utilization of WG and DG to reduce battery replacement expenses. ...
Microgrid systems are part of the most reliable energy supply technologies for rural communities that do not have access to electricity but the system is generally dominated by diesel generators (DG). The implementation of de-dieselization programs to ensure efficient diesel operations requires addressing several scenarios such as the replacement of diesel completely with 100% renewable energy sources at a significant cost. The design and selection of appropriate configuration, as well as operating patterns, need to be considered in adopting economical and reliable microgrid systems. Therefore, this study aimed to design an optimal configuration and operational pattern for microgrid systems for the frontier, outermost, and least developed (3T) regions using Baron Techno Park (BTP) in Indonesia as a case study. The optimization was conducted through HOMER software combined with benefit-cost analysis and the focus was on daily load variations, selection of control algorithms, reconfiguration of the power supply system, and setting of the diesel generator operating hours. The results showed that the optimum configuration was achieved using loads of resort, 24 kWp of PV, 288 kWh of BESS, load-following (LF) as dispatch controller, and 25 kVa of DG. Moreover, the proposed microgrid system produced 12% excess energy, 36% renewable fraction (RF), 13.25 tons reduction in CO2 emissions per year, $0.28 LCOE per kWh, $250,478 NPC, and a benefit-cost ratio (BCR) of 0.89. It also had a potential energy efficiency savings of 55.56% and a cost efficiency of 20.95% compared to existing system configurations. In conclusion, the study showed that the addition of DG to microgrid systems in 3T areas was more optimal than using only PV and batteries. An effective operating schedule for the DG was also necessary to improve RF and reduce expenses. Furthermore, other energy storage devices considered less expensive than batteries could be introduced to improve the economics of microgrid systems in the 3T region.
Smartphones and smartphone apps have undergone an explosive growth in the past decade. However, smartphone battery technology hasn't been able to keep pace with the rapid growth of the capacity and the functionality of smartphones and apps. As a result, battery has always been a bottleneck of a user's daily experience of smartphones. An accurate estimation of the remaining battery life could tremendously help the user to schedule their activities and use their smartphones more efficiently. Existing studies on battery life prediction have been primitive due to the lack of real-world smartphone usage data at scale. This paper presents a novel method that uses the state-of-the-art machine learning models for battery life prediction, based on comprehensive and real-time usage traces collected from smartphones. The proposed method is the first that identifies and addresses the severe data missing problem in this context, using a principled statistical metric called the concordance index. The method is evaluated using a dataset collected from 51 users for 21 months, which covers comprehensive and fine-grained smartphone usage traces including system status, sensor indicators, system events, and app status. We find that the remaining battery life of a smartphone can be accurately predicted based on how the user uses the device at the real-time, in the current session, and in history. The machine learning models successfully identify predictive features for battery life and their applicable scenarios.
Smartphones are resource constrained external battery operated devices. The resources of smartphone devices such as a battery, CPU, and RAM are very low compared to the desktop server. However, the requirements of smartphone users are growing tremendously. As a result, smartphone applications perform rich functionality to enrich user experience. However, due to increase in the execution capacity of smartphone applications, smartphone battery lifetime minimizes. This study proposes a cloud-based framework that estimates battery lifetime of a smartphone device. Moreover, it overviews the theoretical design of computational offloading framework to present an application area for the proposed work. Finally, it presents preliminary results to evaluate the proposed framework.
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.
This paper presents a large, 4-week study of more than 4000 people to assess their smartphone charging habits to identify timeslots suitable for opportunistic data uploading and power intensive operations on such devices, as well as opportunities to provide interventions to support better charging behavior. The paper provides an overview of our study and how it was conducted using an online appstore as a software deployment mechanism, and what battery information was collected. We then describe how people charge their smartphones, the implications on battery life and energy usage, and discuss how to improve users’ experience with battery life.
This paper describes PowerBooter, an automated power model construction technique that uses built-in battery voltage sensors and knowledge of battery discharge behavior to monitor power consumption while explicitly controlling the power management and activity states of individual components. It requires no external measurement equipment. We also describe PowerTutor, a component power management and activity state introspection based tool that uses the model generated by PowerBooter for online power estimation. PowerBooter is intended to make it quick and easy for application developers and end users to generate power models for new smartphone variants, which each have different power consumption properties and therefore require different power models. PowerTutor is intended to ease the design and selection of power efficient software for embedded systems. Combined, PowerBooter and PowerTutor have the goal of opening power modeling and analysis for more smartphone variants and their users.
Battery interfaces provide important feedback about how much time users can continue using their mobile devices. Based on this information, they may develop mental models of the types of activities, tasks, and applications they can use before needing to recharge. Many of today's battery interfaces tend to report energy in coarse granularities or are highly inaccurate. As a result, users may find it difficult to depend on the estimates given. We conducted a survey with 104 participants to understand how users interact with various mobile battery interfaces. Based on the survey results, we designed and prototyped a task-centered battery interface on a mobile device that shows more accurate information about how long individual and combinations of tasks with several applications can be performed. Our pilot study of eight users demonstrated that fine-grained information separated by tasks can help users be more effective with and increase their understanding of their device's battery usage.
Within the past decade, mobile computing has morphed into a principal form of human communication, business, and social interaction. Unfortunately, the energy demands of newer ambient intelligence and collaborative technologies on mobile devices have greatly overwhelmed modern energy storage abilities. This paper proposes several novel techniques that exploit spatiotemporal and device context to predict device wireless data and location interface configurations that can optimize energy consumption in mobile devices. These techniques, which include variants of linear discriminant analysis, linear logistic regression, non-linear logistic regression with neural networks, k-nearest neighbor, and support vector machines are explored and compared on synthetic and user traces from real-world usage studies. The experimental results show that up to 90% successful prediction is possible with neural networks and k-nearest neighbor algorithms, improving upon prediction strategies in prior work by approximately 50%. Further, an average improvement of 24% energy savings is achieved compared to state-of-the-art prior work on energy-efficient location-sensing.
Mobile phone data collection and analysis with open battery
- A Bentevis