Battery-powered Systems Research Articles

ENERGY EFFICIENT ENHANCED LEACH-C PROTOCOL IN WIRELESS SENSOR NETWORKS USING CLOUD

Due to their potential to combine the physical and virtual worlds, wireless sensor networks are a widely recognized, popular, and worthy approach in today's era all over the world. Wireless sensor networks are battery-powered systems that include a variety of sensor nodes as well as one or more base stations. WSN stands for Wireless Sensor Network, and it is comprised of a huge number of sensor components. These devices are self-contained tiny devices with a number of limitations, including rechargeable batteries, internet backbone range, and storage capabilities. They capture data from diverse environments using transmitters and receivers and relay it to a core network, in which the monitored and gathered information can be stored and made delivered to the end client. The fundamental difficulty with these wireless sensor networks is their excessive energy consumption. In our paper, it is proposed an energy efficient ENHANCED LEACH-C with cross pollination in plants and a few energy efficient intelligent protocol characteristics. Our article focuses on the distance when picking the cluster head in a wireless sensor network, as well as the shape and sizes of the sensor nodes in the network, which will significantly minimize the LEACH's energy consumption.

A Novel Capacity Estimation Method for the Lithium Batteries Using the Enhanced Coulomb Counting Method With Kalman Filtering

As the use of energy storage systems (ESSs) and electric vehicles (EVs) increases, the importance of lithium-ion (Li-ion) batteries is also growing. The accurate capacity estimation of a battery is useful for detecting the degradation and end-of-life of the battery for scheduled maintenance and replacement, thereby improving the reliability of battery-powered systems. However, many of the capacity estimation methods developed thus far are offline methods and are not suitable for online monitoring. In this study, a novel capacity estimation method for Li-ion batteries is proposed, based on the enhanced coulomb counting (ECC) method. Typically, the battery capacity calculated by ECC has a large error owing to the error in the state of charge estimation. Therefore, additional means are required to obtain an accurate value for the battery capacity. The Kalman filter is an optimal estimator of the hidden state with Gaussian noise and is applied to the capacity values calculated by the ECC method. The performance of the proposed method in estimating capacity is verified through experiments with a Li-ion battery operated using a standard vehicle driving schedule. The results show that the online capacity estimation using the proposed method is performed successfully within 10 h with a 1.7% error.

State-of-health prediction of lithium-ion battery based on improved gate recurrent unit

State of health (SOH) prediction of lithium-ion batteries is still a very important issue in evaluating the safety and reliability of battery-powered systems. This paper uses the lithiumion battery data in NASA Ames Research Center for research, analyzes the correlation between the relevant feature data, selects the data the most relevant to the capacity through the threshold, and uses it as the input of the neural network. We combine Particle Swarm Optimization (PSO) algorithm and Gate Recurrent Unit (GRU) to form PSO-GRU, and use the PSO-GRU method to find the time step and the number of neurons for the best prediction effect. The experimental results show that, compared with the LSTM method, the PSO-GRU method has higher prediction accuracy and has fewer weight parameters for the neural network training model.

Evaluation of potential tissue heating during percutaneous drill-assisted bone sampling in an in vivo porcine study

BackgroundMinimally invasive, battery-powered drilling systems have become the preferred tool for obtaining representative samples from bone lesions. However, the heat generated during battery-powered bone drilling for bone biopsies has not yet been sufficiently investigated. Thermal necrosis can occur if the bone temperature exceeds a critical threshold for a certain period of time.PurposeTo investigate heat production as a function of femur temperature during and after battery-powered percutaneous bone drilling in a porcine in vivo model.MethodsWe performed 16 femur drillings in 13 domestic pigs with an average age of 22 weeks and an average body temperature of 39.7 °C, using a battery-powered drilling system and an intraosseous temperature monitoring device. The standardized duration of the drilling procedure was 20 s. The bone core specimens obtained were embedded in 4% formalin, stained with haematoxylin and eosin (H&E) and sent for pathological analysis of tissue quality and signs of thermal damage.ResultsNo significant changes in the pigs’ local temperature were observed after bone drilling with a battery-powered drill device. Across all measurements, the median change in temperature between the initial measurement and the temperature measured after drilling (at 20 s) was 0.1 °C. Histological examination of the bone core specimens revealed no signs of mechanical or thermal damage.ConclusionOverall, this preliminary study shows that battery-powered, drill-assisted harvesting of bone core specimens does not appear to cause mechanical or thermal damage.

Wireless, battery-free, and fully implantable electrical neurostimulation in freely moving rodents

Implantable deep brain stimulation (DBS) systems are utilized for clinical treatment of diseases such as Parkinson’s disease and chronic pain. However, long-term efficacy of DBS is limited, and chronic neuroplastic changes and associated therapeutic mechanisms are not well understood. Fundamental and mechanistic investigation, typically accomplished in small animal models, is difficult because of the need for chronic stimulators that currently require either frequent handling of test subjects to charge battery-powered systems or specialized setups to manage tethers that restrict experimental paradigms and compromise insight. To overcome these challenges, we demonstrate a fully implantable, wireless, battery-free platform that allows for chronic DBS in rodents with the capability to control stimulation parameters digitally in real time. The devices are able to provide stimulation over a wide range of frequencies with biphasic pulses and constant voltage control via low-impedance, surface-engineered platinum electrodes. The devices utilize off-the-shelf components and feature the ability to customize electrodes to enable broad utility and rapid dissemination. Efficacy of the system is demonstrated with a readout of stimulation-evoked neural activity in vivo and chronic stimulation of the medial forebrain bundle in freely moving rats to evoke characteristic head motion for over 36 days.

Design and Research on Power Systems and Algorithms for Controlling Electric Underground Mining Machines Powered by Batteries

This article discusses the work that resulted in the development of two battery-powered self-propelled electric mining machines intended for operation in the conditions of a Polish copper ore mine. Currently, the global mining industry is seeing a growing interest in battery-powered electric machines, which are replacing solutions powered by internal combustion engines. The cooperation of Mine Master, Łukasiewicz Research Network—Institute of Innovative Technologies EMAG and AGH University of Science and Technology allowed carrying out a number of works that resulted in the production of two completely new machines. In order to develop the requirements and assumptions for the designed battery-powered propulsion systems, underground tests of the existing combustion machines were carried out. Based on the results of these tests, power supply systems and control algorithms were developed and verified in a virtual environment. Next, a laboratory test stand for validating power supply systems and control algorithms was developed and constructed. The tests were aimed at checking all possible situations in which the battery gets discharged as a result of the machine’s ride or operation and when it is charged from the mine’s mains or with energy recovered during braking. Simulations of undesirable situations, such as fluctuations in the supply voltage or charging power limitation, were also carried out at the test stand. Positive test results were obtained. Finally, the power supply systems along with control algorithms were implemented and tested in the produced battery-powered machines during operational trials. The power systems and control algorithms are universal enough to be implemented in two different types of machines. Both machines were specially designed to substitute diesel machines in the conditions of a Polish ore mine. They are the lowest underground battery-powered drilling and bolting rigs with onboard chargers. The machines can also be charged by external fast battery chargers.

Differences in Radiation Exposure of CT-Guided Percutaneous Manual and Powered Drill Bone Biopsy

PurposeApart from the commonly applied manual needle biopsy, CT-guided percutaneous biopsies of bone lesions can be performed with battery-powered drill biopsy systems. Due to assumably different radiation doses and procedural durations, the aim of this study is to examine radiation exposure and establish local diagnostic reference levels (DRLs) of CT-guided bone biopsies of different anatomical regions.MethodsIn this retrospective study, dose data of 187 patients who underwent CT-guided bone biopsy with a manual or powered drill biopsy system performed at one of three different multi-slice CT were analyzed. Between January 2012 and November 2019, a total of 27 femur (A), 74 ilium (B), 27 sacrum (C), 28 thoracic vertebrae (D) and 31 lumbar vertebrae (E) biopsies were included. Radiation exposure was reported for volume-weighted CT dose index (CTDIvol) and dose–length product (DLP).ResultsCTDIvol and DLP of manual versus powered drill biopsy were (median, IQR): A: 56.9(41.4–128.5)/66.7(37.6–76.2)mGy, 410(203–683)/303(128–403)mGy·cm, B: 83.5(62.1–128.5)/59.4(46.2–79.8)mGy, 489(322–472)/400(329–695)mGy·cm, C: 97.5(71.6–149.2)/63.1(49.1–83.7)mGy, 627(496–740)/404(316–515)mGy·cm, D: 67.0(40.3–86.6)/39.7(29.9–89.0)mGy, 392(267–596)/207(166–402)mGy·cm and E: 100.1(66.5–162.6)/62.5(48.0–90.0)mGy, 521(385–619)/315(240–452)mGy·cm. Radiation exposure with powered drill was significantly lower for ilium and sacrum, while procedural duration was not increased for any anatomical location. Local DRLs could be depicted as follows (CTDIvol/DLP): A: 91 mGy/522 mGy·cm, B: 90 mGy/530 mGy·cm, C: 116 mGy/740 mGy·cm, D: 87 mGy/578 mGy·cm and E: 115 mGy/546 mGy·cm. The diagnostic yield was 82.4% for manual and 89.4% for powered drill biopsies.ConclusionUse of powered drill bone biopsy systems for CT-guided percutaneous bone biopsies can significantly reduce the radiation burden compared to manual biopsy for specific anatomical locations such as ilium and sacrum and does not increase radiation dose or procedural duration for any of the investigated locations.Level of EvidenceLevel 3.

Energy-Efficient On-Platform Target Classification for Electric Air Transportation Systems

Due to the predicted rise of Unmanned Aircraft Systems (UAS) in commercial, civil, and military operations, there is a desire to make UASs more energy efficient so they can proliferate with ease of deployment and maximal life per charge. To address current limitations, a three-tiered approach is investigated to mitigate Unmanned Aerial Vehicle (UAV) hover time, reduce network datalink transmission to a ground station, and provide a real-time framework for Sense-and-Avoidance (SAA) target classification. An energy-efficient UAS architecture framework is presented, and a corresponding SAA prototype is developed using commercial hardware to validate the proposed architecture using an experimental methodology. The proposed architecture utilizes classical computer vision methods within the Detection Subsystem coupled with deeply learned Convolutional Neural Networks (CNN) within the Classification Subsystem. Real-time operations of three frames per second are realized enabling UAV hover time and associated energy consumption during SAA processing to be effectively eliminated. Additional energy improvements are not addressed in the scope of this work. Inference accuracy is improved by 19% over baseline COTS models and current non-adaptive, single-stage SAA architectures. Overall, by pushing SAA processing to the edge of the sensors, network offload transmissions and reductions in processing time and energy consumption are feasible and realistic in future battery-powered electric air transportation systems.

Designing efficient FPGA tiles for power-constrained ultra-low-power applications

High power consumption of Field-Programmable Gate Arrays (FPGAs) makes them a less attractive choice for ultra-low-power applications. Depending on the power source, ultra-low-power systems could either be constrained by power (energy harvesting systems) or by energy (battery-powered systems). In this work, we are evaluating four different FPGA tiles to find the one that is better suited for both power-constrained and energy-constrained systems. Ultra-low-power systems apply voltage downscaling to reduce the power consumption. However, the operational limits of different blocks do not allow conventional FPGA to be operated at very low voltage. Therefore, their logic capacity can only be increased by 2–4 times by applying voltage downscaling. In this work, we identified the blocks in FPGA tiles that are vulnerable at low voltage and replace them with alternate circuits. The results indicate that, by slight modifications in the conventional FPGA tiles, logic capacity can be increased up to 8 times, whereas power-delay-product can be reduced up to 74%.

Wireless and battery-free platforms for collection of biosignals

Recent progress in biosensors have quantitively expanded current capabilities in exploratory research tools, diagnostics and therapeutics. This rapid pace in sensor development has been accentuated by vast improvements in data analysis methods in the form of machine learning and artificial intelligence that, together, promise fantastic opportunities in chronic sensing of biosignals to enable preventative screening, automated diagnosis, and tools for personalized treatment strategies. At the same time, the importance of widely accessible personal monitoring has become evident by recent events such as the COVID-19 pandemic. Progress in fully integrated and chronic sensing solutions is therefore increasingly important. Chronic operation, however, is not truly possible with tethered approaches or bulky, battery-powered systems that require frequent user interaction. A solution for this integration challenge is offered by wireless and battery-free platforms that enable continuous collection of biosignals. This review summarizes current approaches to realize such device architectures and discusses their building blocks. Specifically, power supplies, wireless communication methods and compatible sensing modalities in the context of most prevalent implementations in target organ systems. Additionally, we highlight examples of current embodiments that quantitively expand sensing capabilities because of their use of wireless and battery-free architectures.

Incremental Delta-Sigma ADCs: A Tutorial Review

In many sensor applications, a high-resolution analog-to-digital converter (ADC) is a key block. The use of an incremental delta-sigma ADC (IADC) is often well suited for such applications. While the energy-efficiency of IADCs has improved by several orders of magnitude over the past decade, the implementation of high performance IADCs, especially in battery-powered systems, is still challenging. This paper presents a tutorial review on energy-efficient IADCs and addresses the progress in this area. This paper describes the fundamentals of IADCs and energy-efficient hybrid IADC architectures. Various design techniques for improving the energy-efficiency of the IADCs are described. This paper is intended to serve as a starting point for the development of a new energy-efficient IADC.

A novel deep learning framework for state of health estimation of lithium-ion battery

Abstract The state-of-health (SOH) estimation is a challenging task for lithium-ion battery, which contribute significantly to maximize the performance of battery-powered systems and guide the battery replacement. The complexity of degeneration mechanism enables data-driven methods to replace mechanism modeling methods to estimate SOH. The insight that motivates this study is that the charging curve of constant current-constant voltage charging mode could reflect the magnitude of SOH from the perspective of capacity. The proposed approach is based on a hybrid neural network called gate recurrent unit-convolutional neural network (GRU-CNN), which can learn the shared information and time dependencies of the charging curve with deep learning technology. Then the SOH could be estimated with the new observed charging curves such as voltage, current and temperature. The approach is demonstrated on the public NASA Randomized Battery Usage dataset and Oxford Battery Degradation dataset, and the maximum estimation error is limited to within 4.3%, thus proving its effectiveness.

A low-power RF mixer with harmonic cancellation for IEEE 802.15.4 portable, wearable wireless applications

There is an aggressive need for the rapid development of 5G based battery-powered, portable, wearable, wireless applications in the telecommunication industry. The battery-powered devices pose a critical challenge in processing higher data rates while achieving less distortion and low-power consumption. Hence, the problem of power-efficient distortion cancellation needs to be addressed to equip the 5G New Radio (NR) based portable, wearable wireless systems for better performance. This work addresses the problem by proposing a complementary post-distortion harmonic cancellation (CPDHC) technique to improve the linearity of the Mixer used in battery-powered wireless systems. In the proposed CPDHC method, the overall third-order transconductance of the input stage is reduced optimally by the overall third-order transconductance of the auxiliary transistors. This optimal reduction in third-order transconductance retards the growth of third-order harmonics in the circuit, thus improving the linearity performance of the Mixer circuit. From post-layout simulations, the current reuse based Mixer achieves an IIP3 of −6.823 dBm before linearization, and with linearization by CPDHC technique, the IIP3 is improved to 5.05 dBm, resulting in a spurious-free dynamic range (SFDR) of 66.03 dB for 100 MHz channel bandwidth. The CPDHC based Mixer contributes an integrated double-sideband noise figure (DSB-NF) of 12.57 dB and a peak gain of 14.36 dB while consuming 1.25 mW from a 1.2 V supply.

Simulation model and performance evaluation of battery-powered AGV systems in automated container terminals

The use of battery power is becoming widespread rapidly among the mega ports worldwide, owing its low emission and high energy efficiency. In this paper, a simulation approach is presented to configure the charging stations (CSs) and battery-powered automated guided vehicles (B-AGVs) at automated container terminals. The facility planning of the CSs and the operation strategies of the B-AGVs are crucial for the system performance of the terminal. A port system is modeled as a discrete event simulation model, mainly consisting of three parts: vessel generator, dispatcher, and traffic network. Two types of layout designs and two types of recharging policies are presented, to deploy the CSs and control the B-AGVs to be recharged, respectively. Extensive simulation experiments are conducted to analyze the parameter effects on the system performance. The results indicate that a decentralized CS layout and a progressive recharging policy lead to excellent performance. Some management insights are offered to gain a better understanding of the key factors when deploying a B-AGV system.

A Digital-Intensive Extended-Range Dual-Mode BLE5.0 and IEEE802.15.4 Transceiver SoC

The proposed fully integrated digital-intensive system on chip (SoC) allows dual-mode protocols of Bluetooth low energy (BLE) 5.0 and IEEE802.15.4 with extended-range wireless connectivity and ad hoc mesh networks for Internet-of-Things (IoT) systems in wireless sensor networks. The SoC fully integrates a dc–dc converter, LDOs, a digitally controlled crystal oscillator (DCXO), a PHY, a microcontroller (MCU), a security engine, an SRAM, and an eFlash memory. A triple-supply configuration and low-voltage circuit design enable the SoC to be applied for a wide range of battery-powered systems energy-efficiently and to extend battery lifetime. The TRX employs a low insertion-loss on-die TRX switch with impedance matching circuits, a pseudodifferential current-mode class-D PA with HD2 calibration, a low-IF RX architecture with fast AGC algorithm and complex CT- $\Delta \Sigma $ ADCs, and a low-power all-digital phase-locked loop (ADPLL) with fast settling injection LDOs and an LMS-based two-point direct frequency modulation structure. The TRX implemented in a 28-nm CMOS process achieves an output power of +10 dBm with a calibrated HD2 of −63 dBc while consuming 45 mW and sensitivities of −98 dBm in 2-Mb/s BLE and −102 dBm in 256-kb/s IEEE802.15.4 at ${10}^{-3}$ BER while consuming 6 mW.

Exploring Absolute Differences Arithmetic Operators for Power and Area-Efficient SAD Hardware Architectures

Managing the energy requirements of video encoders has been an important research topic throughout the latest years, given the limited resources of battery-powered systems. Sum of Absolute Differences (SAD) stands out among the most costly steps in the video encoding process compliant with the High Efficiency Video Coding (HEVC) standard. This metric is mainly used to explore temporal redundancies during the inter prediction stage of the encoder, and it is applied by adding the absolute differences of the colocalized pixels of two video blocks, as a means of measuring the similarity between them. SAD architectures are usually designed by using an adder tree, with its first level consisting of subtractors and absolute operators. This paper explores various structures of absolute operators in the context of SAD architectures, in order to define the most suitable implementation for a power-efficient SAD module. Besides the analysis of several different models, we exploit the use of pipelining, and the impact of varying block input bitwidth, to determine which versions scale better with the increase of input size. We have synthesized the architectures for ASIC CMOS technology using real-input vectors taking the delays into consideration, with an ST 65 nm standard cells library, and compared them with the default absolute operator macrofunction from the synthesis tool.

Online state of health prediction method for lithium‐ion batteries, based on gated recurrent unit neural networks

Online state of health (SOH) prediction of lithium-ion batteries remains a very important problem in assessing the safety and reliability of battery-powered systems. Deep learning techniques based on recurrent neural networks with memory, such as the long short-term memory (LSTM) and gated recurrent unit (GRU), have very promising advantages, when compared to other SOH estimation algorithms. This work addresses the battery SOH prediction based on GRU. A complete BMS is presented along with the internal structure and configuration parameters. The neural network was highly optimized by adaptive moment estimation (Adam) algorithm. Experimental data show very good estimation results for different temperature values, not only at room value. Comparisons performed against other relevant estimation methods highlight the performance of the recursive neural network algorithms such as GRU and LSTM, with the exception of the battery regeneration points. Compared to LSTM, the GRU algorithm gives slightly higher estimation errors, but within similar prediction error range, while needing significantly fewer parameters (about 25% fewer), thus making it a very suitable candidate for embedded implementations.

Smart Charging of Electric Vehicle

Transportation through electrical battery-powered cars can facilitate to decreasing fuel expenses and cut back greenhouse gas emissions. Electrified transportation requires a large kind of charging networks to be established during an eco-friendly atmosphere. Wireless electrical battery-powered vehicle charging systems is an identical different technology to charge the electric cars with none plug-in problems. This paper specifies the strategies of Wireless Charging of Electrical Vehicles (WCEV's). The WCEV is taken into account one amongst rising transportation systems during which the EV’s battery is charged through Wireless Power Transfer (WPT) generation. Similarly to in providing an automobile with the specified electricity to recharge the battery of electric vehicle (EV) system has several kinds the most important of them is a wireless charging system that transmits power from transmitter to receiver with none contact. During this project, the technology for electric car wireless charging is reviewed using the technique of inductive coupling. WPT is the transfer of electrical electricity from the power supply to a load without the usage of physical connectors. WPT circuitry is placed within the automobile that receives activated while the car reaches the charging location. The number one coil is supplied from the charging station. Flux is radiated out of the number one coil and link with number two coil present within the Electric vehicle. The voltage which is induced in number two coil is then regulated, then rectified and is used to charge the EV battery.

A Novel Passive Method for the Assessment of Skin-Electrode Contact Impedance in Intraoperative Neurophysiological Monitoring Systems

Intraoperative Neurophysiological Monitoring is a set of monitoring techniques consisting of reading electrical activity generated by the nervous system structures during surgeries. In order to guarantee signal quality, contact impedance between the sensing electrodes and the patient’s skin needs to be as low as possible. Hence, monitoring this impedance while signals are measured is an important feature of current medical devices. The most commonly used technique involves injection of a known current and measurement of the voltage drop in the contact interface. This method poses several problems, such as power consumption (critical in battery-powered systems), frequency dependency and regulation issues, which are overcome by using a passive method. The fundamentals of the method proposed in this paper are based on the utilization of the variation suffered by the input random signal when a known resistance is connected in parallel to the input terminals of the low-noise amplifier (LNA) of the analog front-end of the acquisition system. Controlling the connection of the resistors and computing the root mean square of the LNA output voltage has been proved to be a useful tool to assess that the contact impedance is suitably low, allowing the user to know if the neural measurements obtained are valid.

A Novel Simulated-Annealing Based Electric Bus System Design, Simulation, and Analysis for Dehradun Smart City

Smart transportation network development with environmental issues into consideration has brought Industry 4.0 based solutions on priority. In this direction, battery-powered electric bus systems have been considered widely for ensuring flexibility, operation cost, and lesser pollutants emission. Industry 4.0 provides automation through a cyber-physical system (CPS), the interconnection of bus system entities with industrial internet-of-things (IIoT), remote information availability through cloud computing and scientific disciplines (human-computer interaction, artificial intelligence, machine learning etc.) integration. In this work, a discrete event-based simulation-optimization approach is integrated that take care of bus energy consumption according to real-time city's passenger needs and on-road friction levels. The proposed simulation optimization methodology utilizes multi-objective with dependent and independent variables for optimizing the overall system performance. In simulation optimization, objective functions are designed to tackle battery consumption, Internet-of-Thing (IoT) network performance, cloud operations efficiency and smart scientific discipline integration. Simulation parameters are based on a real-time bus system which is further analyzed, filtered and adapted as per the needs of the system. In another analysis, supercharger's capacities are varied to evaluate the performance of the proposed system and identify the low cost and efficient smart transportation system. Simulation results show different scenarios for variations in the number of buses, charging stations, bus-depots, mobile charging facilities, and bus-schedules. Simulation results show that the average passenger's waiting time in the waiting is (after ticket booking) varies between 0.2 minutes to 0.7 minutes in real-time traffic conditions. In similar traffic conditions, total passenger's time in system (ticket booking to travel) varies between 41.6 minutes (for 24 hours) to 45.5 minutes (for 1 year). In the simulation, priorities are given to those dependent and independent variables which save the battery consumption and elongate the utilization of buses. Lastly, it is also observed that the proposed system is suitable for resource-constraint devices because Gate Equivalent (GE) calculation shows that the proposed system can be implemented between 1986 GEs (communicational cost without confidentiality and authentication) and 7939 GEs (computational cost with HMAC for authentication in data storage). This ensures varies security primitivs such as confidentiality, availability and authentication.