Battery Consumption Research Articles

Implementing and Evaluating the Timed Up and Go Test Automation Using Smartphones and Smartwatches.

Physical performance tests aim to assess the physical abilities and mobility skills of individuals for various healthcare purposes. They are often driven by experts and usually performed at their practice, and therefore they are resource-intensive and time-demanding. For tests based on objective measurements (e.g., duration, repetitions), technology can be used to automate them, allowing the patients to perform the test themselves, more frequently and anywhere, while alleviating the expert from supervising the test. The well-known Timed Up and Go (TUG) test, typically used for mobility assessment, is an ideal candidate for automation, as inertial sensors (among others) can be deployed to detect the various movements constituting the test without expert supervision. To move from expert-led testing to self-administered testing, we present a mHealth system capable of automating the TUG test using a pocket-sized smartphone or a wrist smartwatch paired with a smartphone, where data from inertial sensors are used to detect the activities carried out by the patient while performing the test and compute their results in real time. All processing (i.e., data processing, machine learning-based activity inference, results calculation) takes place on the smartphone. The use of both devices to automate the TUG test was evaluated (w.r.t. accuracy, reliability and battery consumption) and mutually compared, and set off with a reference method, obtaining excellent Bland-Altman agreement results and Intraclass Correlation Coefficient reliability. Results also suggest that the smartwatch-based system performs better than the smartphone-based system.

УПРАВЛІННЯ ГІБРИДНОЮ СИСТЕМОЮ ЕЛЕКТРОЖИВЛЕННЯ ТВАРИННИЦЬКОЇ ФЕРМИ З ВІДНОВЛЮВАЛЬНИМИ ДЖЕРЕЛАМИ ТА ЕЛЕКТРОТРАНСПОРТОМ

The article considers the improvement of the control with using the generation of renewable energy sources forecast for a grid-connected a hybrid power supply system of a livestock farm with electric transport and a gas generator. The task is to form a graph of the state of charge of the main battery with a limitation of the depth of discharge with the maximum use of renewable sources energy. The calculation method of the state of charge of a battery and power consumption graph when working with the grid and in autonomous mode has been improved, with the definition of recommendations for charging electric transport. A mechanism has been developed for correcting the power consumption based on the deviation of the battery charge state relative to the specified value, with the correction disabled to implement the function of eliminating the export of excess energy to the grid or gas generator. This ensures the limitation of the gas generator power within certain limits and helps to limit the influence of generation and load deviations relative to the forecast on the battery discharge depth. The structure of the energy processes model in the system for the daily operation cycle has been developed. In the process of modeling, the deviation of renewable energy generation up to 20% and 10% was studied simultane-ously with a 10% increase in load.

Spiking neural networks-based generation of caterpillar-like soft robot crawling motions

Robots have been widely used in daily life in recent years. Unlike conventional robots made of rigid materials, soft robots utilize stretchable and flexible materials, allowing flexible movements similar to those of living organisms, which are difficult for traditional robots. Previous studies have used periodic signals to control soft robots, which lead to repetitive motions and make it challenging to generate environment-adapted motions. To address this issue, control methods can be learned through deep reinforcement learning to enable soft robots to select appropriate actions based on observations, improving their adaptability to environmental changes. In addition, as mobile robots have limited onboard resources, it is necessary to conserve battery consumption and achieve low-power control. Therefore, the use of spiking neural networks (SNNs) with neuromorphic chips enables low-power control of soft robots. In this study, we investigated the learning methods for SNNs aimed at controlling soft robots. Experiments were conducted using a caterpillar-like soft robot model based on previous studies, and the effectiveness of the learning method was evaluated.

Probing pyrolysis conversion of separator from spent lithium-ion batteries: Thermal behavior, kinetics, evolved gas analysis and aspen plus modeling

The exponential growth in the consumption of lithium-ion batteries (LIBs) has resulted in a concomitant increase in the spent LIBs. Multi-component recycling is a highly desirable and beneficial process, yet the separator component remains overlooked. This study examined the thermal behavior, kinetics, thermodynamics, and evolved products of separator pyrolysis in N2 and CO2 atmospheres. Furthermore, the conversion was modeled using Aspen Plus and a sensitivity analysis was conducted to investigate the impact of varying operating conditions. The findings indicated that the whole degradation process could be divided into three stages: below 200 °C, 200–550 °C and 550–900 °C. The evolved products at peak temperatures of 485 °C in N2 and 500 °C in CO2 were composed of olefins and alkanes. The average apparent activation energy values were determined to be 222.78 kJ mol−1 and 159.36 kJ mol−1 in the N2 and CO2 atmospheres, respectively. Thermodynamic analysis indicated that the separator decomposition was an endothermic and non-spontaneous process. Aspen Plus modeling results were in agreement with the experimental data. The sensitivity analysis revealed that an increase in temperature lead to an increased content of lighter compounds (≤C9) and a decrease in the proportion of heavier compounds (>C10) in the pyrolysis products.

Reductive Dissolution of NCM Cathode through Anaerobic Respiration by Shewanella putrefaciens.

The consumption of lithium-ion batteries (LIBs) has considerably increased over the past decade, leading to a rapid increase in the number of spent LIBs. Exposing spent LIBs to the environment can cause serious environmental harm; however, there is a lack of experimentally obtained information regarding the environmental impacts of abandoned cathode materials. Here, we report the interactions between Shewanella putrefaciens, a microorganism commonly found in diverse low-oxygen natural settings, and LiNi0.6Co0.2Mn0.2O2 (NCM622) under anaerobic conditions. We present compelling evidence that the anaerobic respiration of Shewanella putrefaciens triggers ∼59 and ∼78% dissolution of 0.2 g/L pristine and spent NCM622, respectively. We observed that Shewanella putrefaciens interacted with the pristine and the spent NCM622 under anaerobic conditions at a neutral pH and room temperature and induced the reduction of Ni, Co, and Mn, resulting in the subsequent dissolution of Li, Ni, Co, and Mn. Moreover, we found that secondary mineralization occurred on the surface of reacted NCM622. These findings not only shed light on the substantial impact of microbial respiration on the fate of discarded cathode materials in anaerobic environments but also reveal the potential for sustainable bioleaching of cathodes in spent LIBs.

Evaluation of electric vehicle power routing and charging analysis using artificial neural network synchronized with 5G-network

To motivate the advanced National Clean Air Program (NCAP) strategy announced by the Indian Government in 2017, automizing the routing control on electric vehicle (EV) is a challenging area. According to the public authority guideline, the air contamination issues over the nation focus on accomplishing a 20% to 30% decrease in vehicular outflows in 2024 by enhancing EV mobility. However electric vehicle's limitations on battery capacity and route strategy to charging stations to preserve an adequate level of battery charge still need automized communication strategy. Fixed route solutions might not always be executable because battery consumption varies on several external factors. To better fit the real situation, this study proposes the notion of breaking down the self-adaptive online routing by encountering automatic path detection concerning battery efficiency with adaptive charging decisions. An inventory control-style charging technique is suggested to account for stochastic battery use. In the proposed work, by using an artificial neural network, the power demand and energy consumption of the EV world will be monitored and controlled by using 5G coverage on the smart city evolution. The obtained simulation analysis confirms that in comparison to current conventional and fixed tariff schemes, 5G accompanied EV accomplish an examined rate of SoC charge around 19.95 MW holding a peak range of 108.96 MW. Also, an evaluated typical power loss rate of 2.873 kWH which is less than 11.95% through frequency band analysis with signal-to-noise ratio and downlink power utilization.

Design and development of an intelligent zone based master electronic control unit for power optimization in electric vehicles

The development of electric vehicles (EVs) has been incremental because EVs satisfy a significant demand for energy sources. Electronic control unit (ECU) is an important component that processes the electric signals received from various sensors for generating the control signals for the actuators. Automotive control systems were initially operated manually throughout the automotive revolution based on the responses of input signals received from ECUs and drivers. Most of the functions in EV are controlled by the ECU and every ECU consumes power at all times even if it is not in use. The larger power consumption of passive ECUs like adaptive lighting systems (ALS), automatic wiper systems (AWS) brake light systems (BLS), etc., affect the life of ECUs and the range of EVs. This article is primarily concerned with limiting power consumption by switching the power supply to the passive ECUs based on their requirements. Hence, to achieve the objective, the intelligent zone (i-zone) based master ECU is triggered to activate the slave ECUs. Designing suites including Proteus and KiCAD were used for designing the circuits including master as well as slave ECU. This prototype is built using three secondary ECUs such as ALS & AWS and BLS which are controlled using i-zone-based master ECU. The performance of this implemented design is evaluated, and it is discovered that almost 40% of the battery consumption is reduced. This i-zone-based master ECU and all its slave ECUs manage power while ensuring the safety and reliability of EVs.

Novel energy savings method considering extra sensor battery discharge time for fish farming applications

Energy savings in Wireless Sensor Networks for fish farms is necessary and beneficial. We present a novel energy savings method that minimizes the interpolation errors of sensors' measurements. Sensors follow a working cycle in which they are active when measuring data, and inactive or suspended when data is interpolated from the above measurements and consumed energy is reduced. To our knowledge, we are the first to implement interpolation for energy savings. We improved that model to describe the non-linear property of the consumed energy in the batteries, adding a new variable that explains their real behavior. Several experiments with a prototype of a Wireless Sensor Network with pH, water temperature, and ambient temperature sensors are implemented to validate our method. We made a series of measurements to determine the actual energy savings at each sensor and compared these savings with those predicted by each theory model. The results show that the model is more accurate, presenting less than 5 % prediction errors which does not affect fish growth. Furthermore, our paper introduces an energy-saving method for extending WSN lifetime by modeling the non-linear power consumption of sensors' batteries. We propose a new mathematical optimization formulation using an efficient interpolation mechanism that operates in real-time. A real-scale WSN prototype installed over water validates and refines our method. Finally, we showed that the number of interpolated values is of a broader range for aquatic sensors than for outdoor sensors such as ambient temperature. That is, energy savings for fish farming is acceptable.

Revolutionizing RPAS logistics and reducing CO2 emissions with advanced RPAS technology for delivery systems

To manage remotely piloted aircraft system (RPAS) networks effectively, this research presents a multi-objective location-routing optimization model. This model integrates time window constraints, concurrent pick-up and delivery demands, and rechargeable battery functionality, and also introduces a standardized framework to clarify the RPAS CO2 emission model. These integrations significantly decrease battery consumption in Remotely Piloted Aircraft Systems (RPAS) and lower transportation costs, while also optimizing delivery times, reducing operational risks, and minimizing CO2 emissions. The model’s enhancement for optimizing delivery schedules takes into account uncertain traffic conditions, thus improving accuracy in dynamic environments and further contributing to environmental sustainability. Risk assessment employs the Specific Operations Risk Assessment (SORA) standard, adding a third objective function. This combination of the model, further enhance the efficiency and sustainability of RPAS operations, by optimizing delivery schedules, reducing CO2 emissions and battery consumption, and improving accuracy under dynamic conditions. Also, it make RPAS logistics more practical and effective in real-world applications. As result, the NSGA-II algorithm achieves significant reductions across all objectives: 33.3 % in cost, 6.48 % in time, 33.3 % in risk, and 35.7 % in battery usage within 250 generations. The use of the NSGA-II meta-heuristic method for validation enhances the credibility and practicality of the model. The optimization model’s performance over 250 generations shows rapid initial improvements in cost, time, risk, and battery usage, followed by stabilization, indicating efficient convergence and effective evolutionary computation. Also the findings show that with a CO2 emission rate of 3.773 × 104 kg of CO2 per Wh, highlighting the model’s efficiency and effectiveness.

Estimating energy left in discarded alkaline batteries: Evaluating consumption and recovery opportunities

Each year, a significant number of single-use alkaline batteries with untapped energy are discarded. This study aims to analyze the usage patterns of alkaline batteries based on a dataset of 1021 used batteries, ranging from Size AA to 9V, collected from households in the State of New York. We measure the energy loss resulting from underutilized batteries and examine the corresponding environmental and economic impacts on a national scale. Discarded AA alkaline batteries maintain about 13 % of their initial energy, that results in an estimated annual energy loss of 660 MWh for all AA alkaline batteries in the U.S., and about 40 MWh in New York State. Annually in the U.S., consumers discard AA alkaline batteries with approximately $80 million worth of unused energy, including $4.8 million in New York State alone. We also show that the lifecycle impact of batteries should be multiplied by 1.25 to account for their underutilization. To address these issues, we propose actionable recommendations for improving battery consumption practices and facilitating End-of-Life/Use (EoL/U) recovery processes. The findings show the need for policy interventions to better manage battery usage and disposal toward reducing energy waste and mitigating environmental impacts.

Corun: Concurrent Inference and Continuous Training at the Edge for Cost-Efficient AI-Based Mobile Image Sensing.

Intelligent mobile image sensing powered by deep learning analyzes images captured by cameras from mobile devices, such as smartphones or smartwatches. It supports numerous mobile applications, such as image classification, face recognition, and camera scene detection. Unfortunately, mobile devices often lack the resources necessary for deep learning, leading to increased inference latency and rapid battery consumption. Moreover, the inference accuracy may decline over time due to potential data drift. To address these issues, we introduce a new cost-efficient framework, called Corun, designed to simultaneously handle multiple inference queries and continual model retraining/fine-tuning of a pre-trained model on a single commodity GPU in an edge server to significantly improve the inference throughput, upholding the inference accuracy. The scheduling method of Corun undertakes offline profiling to find the maximum number of concurrent inferences that can be executed along with a retraining job on a single GPU without incurring an out-of-memory error or significantly increasing the latency. Our evaluation verifies the cost-effectiveness of Corun. The inference throughput provided by Corun scales with the number of concurrent inference queries. However, the latency of inference queries and the length of a retraining epoch increase at substantially lower rates. By concurrently processing multiple inference and retraining tasks on one GPU instead of using a separate GPU for each task, Corun could reduce the number of GPUs and cost required to deploy mobile image sensing applications based on deep learning at the edge.

Dynamic pickup and delivery problem for autonomous delivery robots in an airport terminal

Autonomous delivery robot (ADR) operation for short-range delivery purposes is becoming an increasingly popular mode of service. Assigning orders to robots is critical in ensuring high quality in these automated delivery operations. This paper considers a dynamic pickup and delivery problem using autonomous robots (DPDP-AR), where a fleet of ADRs picks up desired items from stores and delivers them to customers. The arrival time of orders is uncertain, with a hard delivery deadline for each order. This study considered the battery consumption of ADRs, which has been less extensively covered in previous research, and devised a battery recharging strategy for the operation. To handle this stochasticity and dynamism in the problem, we developed a reassignment algorithm that reschedules previously assigned orders at the arrival of a new order. Additionally, as periods of high demand can be estimated, peak time management of the battery is proposed to enhance ADR utilization at peak periods. Computational experiments were performed on real-world ADR food delivery service instances in an airport terminal. Test instances on various demand scenarios demonstrated improvement in service quality when devised policies were used compared to current practice. We substantiated that the proposed algorithms found efficient solutions within short computation times, validating their applicability to real-world operations.

(Invited) Towards Scalable and Low-Cost Direct Recycling of Lithium-Ion Batteries

The increasing amount of lithium-ion battery (LIBs) consumption will result in the resource shortage and price increase of lithium and precious transition metals that are critical for making high-performance LIBs. Also, future batteries that mainly use low-cost materials (Na, Fe, Mn) will have limited economic benefits to recycle even though the wastes generated from disposal of used batteries can cause severe environment pollution. In this context, design of low-cost and scalable recycling and regeneration process for spent batteries is attracting growing interest. This talk will focus on our recent progress on direct recycling and regeneration spent LIBs, which can lead to new electrode materials that can show the same level of performance as the native materials. We will show successful recycling of various battery materials, including cathode (LiMO2, LiMn2O4, and LiFePO4) and anode (graphite) using the direct regeneration approach. We will discuss how we integrate different operation steps into a pathway for scalable directly recycling.

Adaptive Aberrance Repressed Correlation Filters with Cooperative Optimization in High-Dimensional Unmanned Aerial Vehicle Task Allocation and Trajectory Planning

In the rapidly evolving field of unmanned aerial vehicle (UAV) applications, the complexity of task planning and trajectory optimization, particularly in high-dimensional operational environments, is increasingly challenging. This study addresses these challenges by developing the Adaptive Distortion Suppression Correlation Filter Cooperative Optimization (ARCF-ICO) algorithm, designed for high-dimensional UAV task allocation and trajectory planning. The ARCF-ICO algorithm combines advanced correlation filter technologies with multi-objective optimization techniques, enhancing the precision of trajectory planning and efficiency of task allocation. By incorporating weather conditions and other environmental factors, the algorithm ensures robust performance at low altitudes. The ARCF-ICO algorithm improves UAV tracking stability and accuracy by suppressing distortions, facilitating optimal path selection and task execution. Experimental validation using the UAV123@10fps and OTB-100 datasets demonstrates that the ARCF-ICO algorithm outperforms existing methods in Area Under the Curve (AUC) and Precision metrics. Additionally, the algorithm’s consideration of battery consumption and endurance further validates its applicability to current UAV technologies. This research advances UAV mission planning and sets new standards for UAV deployment in both civilian and military applications, where adaptability and accuracy are critical.

The effect of task processing management on energy consumption at the edge of Internet of things network with using reinforcement learning method

In this paper, the task processing approach at the edge of the Internet of Things (IoT) network in improving energy consumption was investigated. First, the subject of maximizing the use of energy is defined as the main problem and then based on the state of assignment of task processing from end devices at the edge of the network to smart gateways and local servers at the edge using possible low bandwidth and low battery consumption, the problem is divided into two subject which are obtained independently. After modeling by recombining these two sub-problems, the subject of maximizing the utility gain and battery life of the end devices is solved despite the limitations of processing time and energy resources.Considering that the environment of the problem and how the terminal devices are connected with the edge devices are unknown, a repetitive reinforcement learning algorithm has been used to create an optimal solution to maximize the operational benefit of the energy consumed in edge processing. The results achieved from the simulation show the increase in network load and processing overhead with the increase in the number of active devices. The suggested method, while increasing the edge processing speed, decreases the delay and maximizes the performance gain of the IOT edge system compared to the central cloud system. It should be noted that when the number of active end devices dramatically increases, the energy consumption and bandwidth at the network edge improves, and energy consumption at the network edge decreases lonely by 12.5%.

Code smells analysis for android applications and a solution for less battery consumption

In the digitization era, the battery consumption factor plays a vital role for the devices that operate Android software, expecting them to deliver high performance and good maintainability.The study aims to analyze the Android-specific code smells, their impact on battery consumption, and the formulation of a mathematical model concerning static code metrics hampered by the code smells. We studied the impact on battery consumption by three Android-specific code smells, namely: No Low Memory Resolver (NLMR), Slow Loop (SL) and Unclosed Closable, considering 4,165 classes of 16 Android applications. We used a rule-based classification method that aids the refactoring ideology. Subsequently, multi-linear regression (MLR) modeling is used to evaluate battery usage against the software metrics of smelly code instances. Moreover, it was possible to devise a correlation for the software metric influenced by battery consumption and rule-based classifiers. The outcome confirms that the refactoring of the considered code smells minimizes the battery consumption levels. The refactoring method accounts for an accuracy of 87.47% cumulatively. The applied MLR model has an R-square value of 0.76 for NLMR and 0.668 for SL, respectively. This study can guide the developers towards a complete package for the focused development life cycle of Android code, helping them minimize smartphone battery consumption and use the saved battery lives for other operations, contributing to the green energy revolution in mobile devices.

Zinc-Air Hearing Aid Batteries: An Analysis of Functional Performance.

The aim of this study was to evaluate the performance of three disposable hearing aid battery brands available in Wales. Hearing-impaired individuals who utilise hearing aids rely on the functionality of their devices, which is often contingent upon the quality and longevity of disposable batteries. A grey literature review foregrounded the battery standards. The "real-life" use of batteries was supplemented through laboratory testing. Parameters relating to performance quality were used to quantify an overall service life of five PR44- and four PR48-size batteries per manufacturer. The literature review signalled a large gap in hearing aid battery consumption research. All battery brands underperformed compared to their specifications but met IEC standards. Revisions to battery consumption test conditions should reflect new technological features and refine expectations of real-life use. It was possible to statistically identify the best performing hearing aid battery brand.

Comparing and Parameterizing the Electrical Energy Consumption Models in SUMO

This paper examined the performances of the current four battery models in SUMO. The possibility of expanding the model parameterization was also investigated and the corresponding extension was carried out for PHEMlight. Accordingly, the models can be compared more fairly. Three scenarios were used, namely the Worldwide harmonized Light vehicles Test Cycle, a constant high-speed highway scenario and an area scenario with a relatively complex traffic situation. The results show that all models can address recuperation and propulsion, and deliver the similar result at very low acceleration. The models based on average vehicle data generally tend to deliver higher battery consumption than the models with individual vehicle type-specific parameterization, especially PHEMlight5, while HBEFA4 only has one electric vehicle class and is therefore not sensitive to various vehicle characteristics. Moreover, the model by Kurczveil and López (EVM) seems to tend to have the lowest consumption of all models.

Enhancing Autonomous Driving Robot Systems with Edge Computing and LDM Platforms

The efficient operation and interaction of autonomous robots play crucial roles in various fields, e.g., security, environmental monitoring, and disaster response. For these purposes, processing large volumes of sensor data and sharing data between robots is essential; however, processing such large data in an on-device environment for robots results in substantial computational resource demands, causing high battery consumption and heat issues. Thus, this study addresses challenges related to processing large volumes of sensor data and the lack of dynamic object information sharing among autonomous robots and other mobility systems. To this end, we propose an Edge-Driving Robotics Platform (EDRP) and a Local Dynamic Map Platform (LDMP) based on 5G mobile edge computing and Kubernetes. The proposed EDRP implements the functions of autonomous robots based on a microservice architecture and offloads these functions to an edge cloud computing environment. The LDMP collects and shares information about dynamic objects based on the ETSI TR 103 324 standard, ensuring cooperation among robots in a cluster and compatibility with various Cooperative-Intelligent Transport System (C-ITS) components. The feasibility of operating a large-scale autonomous robot offloading system was verified in experimental scenarios involving robot autonomy, dynamic object collection, and distribution by integrating real-world robots with an edge computing–based offloading platform. Experimental results confirmed the potential of dynamic object collection and dynamic object information sharing with C-ITS environment components based on LDMP.

Quadrotor with wheels: design and experimental evaluation

Quadrotors have found widespread use in indoor applications, including tracking and mapping. In general, to carry out such tasks effectively, a navigation solution should provide both accuracy and battery efficiency. To achieve both, we propose a cost-effective and lightweight wheeled quadrotor that combines both driving and flying capabilities. Our design allows the quadrotor to perform both functions seamlessly. We provide a detailed description of the design and construction process, highlighting its advantages. Our focus was on the Tello quadrotor, which weighs 80 grams. Our design allowed driving capability with an increased weight of only fifteen grams, resulting in less than 20% of the added weight. Furthermore, we evaluate the quadrotor’s pure inertial navigation performance and corresponding battery consumption by employing various flying and driving patterns. Our results show that when only driving the battery consumption was the lowest with 10% and some flying scenarios improve the positioning error by more than 70%.