Battery Consumption Rate Research Articles

Dynamic routing optimization with electric vehicles under stochastic battery depletion

ABSTRACT This paper addresses a dynamic traveling salesman problem with electric vehicles under stochastic battery depletion. In the problem, traffic density and battery consumption rate are not known precisely, and their probability distributions are subject to change during the transportation operations. The problem has been formulated and solved using the Dynamic Programming (DP) approach. We develop a DP-based heuristic, which combines Restricted DP and Prim’s algorithms, to solve larger instances. The provided algorithms can determine distribution plans that reduce energy consumption and range anxiety of electric vehicle drivers. The added values of the model and the solution approach have been shown based on a case study and 270 instance-setting pairs that involve relatively larger problems. The heuristic algorithm outperformed a benchmark heuristic by providing 6.87% lower calculated required energy on average. The provided decision support tools can be used to assure energy conservation and emission reduction for short-haul freight distribution systems.

Infrastructure planning for ride-hailing services using shared autonomous electric vehicles

Ride-hailing services using shared autonomous electric vehicles (SAEVs) are soon expected to serve a significant portion of the public transportation demands, especially in metropolitan cities. The transportation demands will arise randomly across all transportation nodes and during all hours of operation. This spatio-temporal nature of the transportation demand together with the constraints of trip length, battery size and consumption rate, and power network will guide the actions of the SAEVs. These actions are pair-with-demand, park, charge, and relocate, which in turn will determine the necessary infrastructure plan for ride-hailing services. The plan will comprise SAEV fleet size, charging hub locations across the nodes of the transportation network, and the number of charging stations in each hub. We formulate a profit-maximizing mixed integer linear model comprising a space-time-battery representation of the transportation network and a DC power flow representation of the power network. The model, intended for developing infrastructure plans for ride-hailing services, is robustified to accommodate randomness in travel and electric power demand. The model solution is demonstrated via an illustrative numerical case study representing the Tampa Bay region of Florida, USA. The results show how the infrastructure plans and their profitability depend on the demand fulfillment goal, battery size, battery consumption rate, and power network configurations. A linear relaxation of some of the integer variables of the model to reduce computational time is discussed.

Research on Braking Efficiency of Master-Slave Electro-Hydraulic Hybrid Electric Vehicle

To address the problems of short-rangee and poor braking safety of electric vehicles, this paper proposes a master-slave electro-hydraulic hybrid passenger car drive system based on planetary gear. The system couples the electrical energy output from the electric motor with the hydraulic energy output from the electro-hydraulic pump/motor through the planetary gear. The hydraulic system is used as the auxiliary power source of the power system giving full play to the advantages of the hydraulic system and the electric system. After theoretical analysis, this paper establishes a master-slave electro-hydraulic hybrid electric vehicle (MSEHH-EV) model based on planetary gear in AMESim software. A braking energy recovery control strategy is designed with the maximum braking energy recovery efficiency as the target. Braking strength determines the switching of braking modes. Finally, comparing the certified pure electric vehicle (EV) model in AMESim, we are able to substantiate the superiority of the strategy proposed in this paper. The simulation results revealed that the battery consumption rate of the new power vehicle is reduced by 17.766%, 11.358%, and 9.427% under UDDS, NEDC, and WLTC conditions, respectively, which supports the range. At the same time, the braking distance is significantly shortened, and the maximum braking distance is shortened by 15.65 m, 21.97 m, and 21.45 m, respectively, under the three operating conditions, which improves the braking safety.

Research on driving control strategy and Fuzzy logic optimization of a novel mechatronics-electro-hydraulic power coupling electric vehicle

To reduce large peak torque and improve the vehicle's dynamic performance during frequent stops and starts, a novel mechatronics-electro-hydraulic power coupling system (MEH-PCS) is proposed. It combines a conventional electric motor and a piston pump/motor into a single unit that converts electrical, mechanical, and hydraulic energy. After a theoretical analysis of the system power flow, the paper establishes a rule-based dynamic optimal energy management strategy to control energy distribution and the dynamic switching of working modes in real-time. Moreover, a prototype vehicle model (MEH-PCEV) based on the MEH-PCS is developed by analyzing the working modes of the system. Thereafter, the feasibility and superiority of MEH-PCEV are verified by comparing it with AMESim certified pure electric vehicles. The verification results indicate that the MEH-PCEV's battery consumption rate is reduced by 14.418% and 21.174%, respectively, in the new European driving cycle and the American city cycle. Furthermore, the time for the vehicle to reach the start-up speed threshold is enhanced, and the electric peak torque is substantially reduced. Ultimately, the paper proposes a fuzzy controller with vehicle speed and pressure of the accumulator as variables to optimize the logic threshold control strategy. The electric torque is substantially reduced while the overall efficiency of the motor operating point is enhanced. The consumption rate of electric energy has been improved somewhat. Under the drive strategy's control, the MEH-PCS is well suited for frequent stops and starts conditions. As a development, this research is expected to provide a reference for improving electric torque.

Understanding Smartwatch Battery Utilization in the Wild.

Smartwatch battery limitations are one of the biggest hurdles to their acceptability in the consumer market. To our knowledge, despite promising studies analyzing smartwatch battery data, there has been little research that has analyzed the battery usage of a diverse set of smartwatches in a real-world setting. To address this challenge, this paper utilizes a smartwatch dataset collected from 832 real-world users, including different smartwatch brands and geographic locations. First, we employ clustering to identify common patterns of smartwatch battery utilization; second, we introduce a transparent low-parameter convolutional neural network model, which allows us to identify the latent patterns of smartwatch battery utilization. Our model converts the battery consumption rate into a binary classification problem; i.e., low and high consumption. Our model has 85.3% accuracy in predicting high battery discharge events, outperforming other machine learning algorithms that have been used in state-of-the-art research. Besides this, it can be used to extract information from filters of our deep learning model, based on learned filters of the feature extractor, which is impossible for other models. Third, we introduce an indexing method that includes a longitudinal study to quantify smartwatch battery quality changes over time. Our novel findings can assist device manufacturers, vendors and application developers, as well as end-users, to improve smartwatch battery utilization.

Drone Delivery Scheduling Optimization Considering Payload-induced Battery Consumption Rates

This paper addresses the design of a parcel delivery system using drones, which includes the strategic planning of the system and operational planning for a given region. The amount of payload affects the battery consumption rate (BCR), which can cause a disruption in delivery of goods if the BCR was under-estimated in the planning stage or cause unnecessarily higher expenses if it was over-estimated. Hence, a reliable parcel delivery schedule using drones is proposed to consider the BCR as a function of payload in the operational planning optimization. A minimum set covering approach is used to model the strategic planning and a mixed integer linear programming problem (MILP) is used for operational planning. A variable preprocessing algorithm and primal and dual bound generation methods are developed to improve the computational time for solving the operational planning model. The optimal solution provides the least number of drones and their flight paths to deliver parcels while ensuring the safe return of the drones with respect to the battery charge level. Experimental data show that the BCR is a linear function of the payload amount. The results indicate the impact of including the BCR in drone scheduling, 3 out of 5 (60%) flight paths are not feasible if the BCR is not considered. The numerical results show that the sequence of visiting customers impacts the remaining charge.

Adaptive job allocation scheduler based on usage pattern for computing offloading of IoT

Abstract Recently, with the rapid growth of information technology (IT), diverse studies have been carried out for the grafting of devices based on the Internet of Things (IoT) for use in real life. With certain sensor functions and downsized mobile devices, IoT devices have improved users’ work efficiency, ease of mobility, and convenience in terms of not being restricted by location. In the case of IoT devices as such, computing offloading is regarded to be very important to overcome issues of limited computing power and storage capacity and the limitations of built-in batteries. For the computing offloading of IoT devices, diverse job allocation techniques considering performance resources have been studied. However, since only the static performance, dynamic performance, or performance and battery size of IoT devices are considered in job allocation, job reallocation problems are caused by battery consumption due to the use of patterns in which users execute certain applications. In this paper, an adaptive job allocation scheduler (AJAS) that adaptively redistributes the jobs allocated to IoT devices based on user behavior patterns is proposed. The AJAS allocates jobs using the dynamic performance resources and battery consumption rates of diverse IoT devices. In addition, the AJAS measures the battery consumption rate of user applications executed in the IoT device to assess whether the allocated jobs can be processed. The AJAS identifies IoT devices that cannot process jobs and minimizes states in which allocated jobs cannot be processed due to battery exhaustion and delay time due to job reallocation. For verification, an AJAS is designed and implemented to show that the AJAS improves device availability for job processing.

Job Allocation Mechanism for Battery Consumption Minimization of Cyber-Physical-Social Big Data Processing Based on Mobile Cloud Computing

The rapid development of information & communication technology has led to the wide popularity of mobile devices, which have helped to improve business efficiency and enabled simple mobility as small and light devices and convenience of being available anytime, anywhere for cyber-physical-social big data. There are many ongoing studies on mobile cloud computing (MCC) to overcome the limited computing capability and storage capacity and internal battery limitation by taking advantage of the popularity of mobile devices for the processing cyber-physical-social big data. MCC consists of service-oriented architecture, agent-client architecture, and collaborative architecture, with job splitting and allocation as the critical factor. As such, job allocation techniques considering the performance resources of mobile devices have been studied. Note, however, that there is a problem of job reallocation due to continuous battery consumption, since the studies consider only the performance resources of mobile devices at the time of job allocation or take into account the performance resources and remaining battery power only. This paper proposes the job allocation mechanism (JAM) for battery consumption minimization of cyber-physical-social big data processing in MCC, which continuously reflects the battery consumption rate to process jobs with mobile devices only without an external cloud server in a collaborative architecture-based MCC environment. JAM allocates jobs considering the periodic measurement of battery consumption and surplus resource to minimize the problem of job reallocation due to battery rundown of the mobile devices. This paper designs and implements a system for verifying JAM and demonstrated that the job processing speed increased in an MCC environment for cyber-physical-social big data.

Sustainable Load-Balancing Scheme for Inter-Sensor Convergence Processing of Routing Cooperation Topology

Recent advancements in Information Technology (IT) have sparked the creation of numerous and diverse types of devices and services. Manual data collection measurement methods have been automated through the use of various wireless or wired sensors. Single sensor devices are included in smart devices such as smartphones. Data transmission is critical for big data collected from sensor nodes, such as Mobile Sensor Nodes (MSNs), where sensors move dynamically according to sensor mobility, or Fixed Sensor Nodes (FSNs), where sensor locations are decided by the users. False data transfer processing of big data results in topology lifespan reduction and data transfer delays. Hence, a variety of simulators and diverse load-balancing algorithms have been developed as protocol verification tools for topology lifespan maximization and effective data transfer processing. However, those previously developed simulators have limited functions, such as an event function for a specific sensor or a battery consumption rate test for sensor deployment. Moreover, since the previous load-balancing algorithms consider only the general traffic distribution and the number of connected nodes without considering the current topology condition, the sustainable load-balancing technique that takes into account the battery consumption rate of the dispersed sensor nodes is required. Therefore, this paper proposes the Sustainable Load-balancing Scheme (SLS), which maximizes the overall topology lifespan through effective and sustainable load-balancing of data transfer among the sensors. SLS is capable of maintaining an effective topology as it considers both the battery consumption rate of the sensors and the data transfer delay.

Biocompatible Cardiac Sensor with Continuous Arrhythmia Monitoring for Human Wellness

Prolonged monitoring is more likely to result in an accurate diagnosis of atrial fibrillation patients than intermittent or short-term monitoring. In this study, we present an implantable ECG sensor to monitor atrial fibrillation patients in real time. The developed implantable sensor is composed of a micro controller unit, analog to digital converter, signal transmitter, antenna, and two electrodes. The sensor detects ECG signals from the two electrodes and transmits these signals to the external receiver that is carried by the patient. The sensor continuously transmits signals, so its battery consumption rate is extremely high. To overcome this problem, we consider using a wireless power transmission module in the sensor module. This module helps the sensor charge power wirelessly without holding the battery in the body. The size of the integrated sensor is approximately 0.12 inch x 1.18 inch x 0.19 inch. This sensor size is appropriate enough for cardiologists to insert the sensor into patients without the need for a major surgery. The data sampling rate was 300 samples/sec, and the frequency was 430 Hz for signal and power transmission. To verify the validation of the developed sensor, the small animal experiments were conducted.

Miniaturized Human Insertable Cardiac Monitoring System with Wireless Power Transmission Technique

Prolonged monitoring is more likely to diagnose atrial fibrillation accurately than intermittent or short-term monitoring. In this study, an implantable electrocardiograph (ECG) sensor to monitor atrial fibrillation patients in real time was developed. The implantable sensor is composed of a micro controller unit, an analog-to-digital converter, a signal transmitter, an antenna, and two electrodes. The sensor detects ECG signals from the two electrodes and transmits these to an external receiver carried by the patient. Because the sensor continuously transmits signals, its battery consumption rate is extremely high; therefore, the sensor includes a wireless power transmission module that allows it to charge wirelessly from an external power source. The integrated sensor has the approximate dimensions 3 mm × 4 mm × 14 mm, which is small enough to be inserted into a patient without the need for major surgery. The signal and power transmission data sampling rate and frequency of the unit are 300 samples/s and 430 Hz, respectively. To validate the developed sensor, experiments were conducted on small animals.

SU‐E‐T‐252: Micro‐Sized Implantable Electrocardiogram (ECG) Sensor for Real‐Time Monitoring of A trial Fibrillation

Purpose: In this study, we present an implantable ECG sensor to monitor atrial fibrillation patients in real time. Methods: The developed implantable sensor is composed of a micro controller unit, analog to digital converter, signal transmitter, antenna, and two electrodes. The sensor detects ECG signals from the two electrodes and transmits these signals to the external receiver that is carried by the patient. The sensor continuously transmits signals, so its battery consumption rate is extremely high. To overcome this problem, we consider using a wireless power transmission module in the sensor module. This module helps the sensor charge power wirelessly without holding the battery in the body. The size of the integrated sensor is approximately 0.12 inch x 1.18 inch x 0.19 inch. This sensor size is appropriate enough for cardiologists to insert the sensor into patients without the need for a major surgery. The data sampling rate was 300 samples/sec, and the frequency was 430 Hz for signal and power transmission. Results: The proposed sensor was validated with shielding experiments to mimic the action of the implant and the shielding of sensor effects. The cylindrical case for the experiment was made of three different materials: quartz, titanium, and acryl. The sensor, which transmits signals to the external receiver, was placed into the cylindrical case. The experiments showed that the sensor transmits ECG signals correctly with the three different types of case materials. The quartz material shows the best results among the three materials because it sends data without any distortion. Conclusion: An implantable ECG sensor that is composed of a wireless signal transmitter, wireless power receiver, and two electrodes was designed and experimentally evaluated. The work presented in this study is an initial step toward the real‐time monitoring of atrial fibrillation patients. This work was supported by a Grant (No. 10041876) from the Technology Innovation Program funded by the Ministry of Knowledge Economy(MKE, Korea).

Personalized Battery Lifetime Prediction for Mobile Devices based on Usage Patterns

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.