battery life
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2022 ◽  
Vol 46 ◽  
pp. 103897
Eshan Karunarathne ◽  
Anjana Wijesekera ◽  
Lilantha Samaranayake ◽  
Prabath Binduhewa ◽  
Janaka Ekanayake

2022 ◽  
Vol 21 (1) ◽  
pp. 1-22
Dongsuk Shin ◽  
Hakbeom Jang ◽  
Kiseok Oh ◽  
Jae W. Lee

A long battery life is a first-class design objective for mobile devices, and main memory accounts for a major portion of total energy consumption. Moreover, the energy consumption from memory is expected to increase further with ever-growing demands for bandwidth and capacity. A hybrid memory system with both DRAM and PCM can be an attractive solution to provide additional capacity and reduce standby energy. Although providing much greater density than DRAM, PCM has longer access latency and limited write endurance to make it challenging to architect it for main memory. To address this challenge, this article introduces CAMP, a novel DRAM c ache a rchitecture for m obile platforms with P CM-based main memory. A DRAM cache in this environment is required to filter most of the writes to PCM to increase its lifetime, and deliver highest efficiency even for a relatively small-sized DRAM cache that mobile platforms can afford. To address this CAMP divides DRAM space into two regions: a page cache for exploiting spatial locality in a bandwidth-efficient manner and a dirty block buffer for maximally filtering writes. CAMP improves the performance and energy-delay-product by 29.2% and 45.2%, respectively, over the baseline PCM-oblivious DRAM cache, while increasing PCM lifetime by 2.7×. And CAMP also improves the performance and energy-delay-product by 29.3% and 41.5%, respectively, over the state-of-the-art design with dirty block buffer, while increasing PCM lifetime by 2.5×.

2022 ◽  
Vol 13 (1) ◽  
pp. 21
Wenguang Li ◽  
Guosheng Feng ◽  
Sumei Jia

This study involved a detailed analysis of an energy distribution strategy and the parameters of key components of fuel cell electric vehicles (FCEVs). In order to better utilize the advantages of multiple energy sources, the wavelet-fuzzy energy management method was used to adjust the demand power allocation among multiple energy sources, and particle swarm optimization (PSO) was used to solve highly nonlinear optimization problems under multi-dimensional and multi-condition constraints. The multi-objective optimization problem of predefined driving cycle powertrain parameters about fuel economy and system durability was studied. The parameters of the key components of the system were optimized, including the size parameters of the air com-pressor and the number of batteries and ultra-capacitors. Furthermore, the driving state under specific working conditions was analyzed, and a nonlinear model with system durability and fuel economy as the optimization objectives were established, which greatly reduced the costs, reduced the fuel consumption rate and extended the battery life. The simulation results showed that for a UDDS cycle, the FCS’s maximal net output power of 83 kW was optimal for the fuel economy and system durability of a fuel cell city bus.

2022 ◽  
pp. 249-268
S. Ravikrishna ◽  
Kumar C. S. Subash ◽  
M. Sundaram

Tatiana Von Hertwig Fernandes de Oliveira ◽  
Jennyfer Paulla Galdino Chaves ◽  
Thiago Teixeira Silva ◽  
Alexandre Novicki Francisco ◽  
Sérgio Leandro Stebel

Abstract Introduction Vagal nerve stimulation (VNS) is an adjuvant therapy used in the treatment of patients with refractory epilepsy who are not candidates for resective surgery or who have limited results after surgical procedures. Currently, there is enough evidence to support its use in patients with various types of epilepsy. Therefore, the present study was conducted to explore the possibility of optimizing therapy by reducing the consumption of the system's battery. Methods The prospective and double-blind analysis consisted in the evaluation of 6 patients submitted to VNS implantation for 3 months, followed by adjustment of the stimulation settings and continuity of follow-up for another month. The standard protocol was replaced by another with a frequency value of 20 Hz instead of 30 Hz to increase battery life. The safety of this procedure was evaluated through the assessment of two main variables: seizures and side effects. Results The stimulation at 20 Hz showed 68% reduction in the incidence of seizures (p = 0.054) as well as low incidence of side effects. Conclusion The present study suggests that the reduction of the stimulation frequency from 30 to 20 Hz is a safe procedure, and it does not compromise the effectiveness of therapy.

2022 ◽  
Vol 12 (1) ◽  
pp. 484
Dominik Piątkowski ◽  
Krzysztof Walkowiak

As the COVID-19 pandemic emerged, everyone’s attention was brought to the topic of the health and safety of the entire human population. It has been proven that wearing a face mask can help limit the spread of the virus. Despite the enormous efforts of people around the world, there still exists a group of people that wear face masks incorrectly. In order to provide the best level of safety for everyone, face masks must be worn correctly, especially indoors, for example, in shops, cinemas and theaters. As security guards can only handle a limited area of the frequently visited objects, intelligent sensors can be used. In order to mount them on the shelves in the shops or near the cinema cash register queues, they need to be capable of battery operation. This restricts the sensor to be as energy-efficient as possible, in order to prolong the battery life of such devices. The cost is also a factor, as cheaper devices will result in higher accessibility. An interesting and quite novel approach that can answer all these challenges is a TinyML system, that can be defined as a combination of two concepts: Machine Learning (ML) and Internet of Things (IoT). The TinyML approach enables the usage of ML algorithms on boards equipped with low-cost, low-power microcontrollers without sacrificing the classifier quality. The main goal of this paper is to propose a battery-operated TinyML system that can be used for verification whether the face mask is worn properly. To this end, we carefully analyze several ML approaches to find the best method for the considered task. After detailed analysis of computation and memory complexity as well as after some preliminary experiments, we propose to apply the K-means algorithm with carefully designed filters and a sliding window technique, since this method provides high accuracy with the required energy-efficiency for the considered classification problem related to verification of using the face mask. The STM32F411 chip is selected as the best microcontroller for the considered task. Next, we perform wide experiments to verify the proposed ML framework implemented in the selected hardware platform. The obtained results show that the developed ML-system offers satisfactory performance in terms of high accuracy and lower power consumption. It should be underlined that the low-power aspect makes it possible to install the proposed system in places without the access to power, as well as reducing the carbon footprint of AI-focused industry which is not negligible. Our proposed TinyML system solution is able to deliver very high-quality metric values with accuracy, True Positive Ratio (TPR), True Negative Ratio (TNR), precision and recall being over 96% for masked face classification while being able to reach up to 145 days of uptime using a typical 18650 battery with capacity of 2500 mAh and nominal voltage of 3.7 V. The results are obtained using a STM32F411 microcontroller with 100 MHz ARM Cortex M4, which proves that execution of complex computer vision tasks is possible on such low-power devices. It should be noted that the STM32F411 microcontroller draws only 33 mW during operation.

2022 ◽  
pp. 233-252
Changbin Hu ◽  
Lisong Bi ◽  
ZhengGuo Piao ◽  
ChunXue Wen ◽  
Lijun Hou

This article describes how basing on the future behavior of microgrid system, forecasting renewable energy power generation, load and real-time electricity price, a model predictive control (MPC) strategy is proposed in this article to optimize microgrid operations, while meeting the time-varying requirements and operation constraints. Considering the problems of unit commitment, energy storage, economic dispatching, sale-purchase of electricity and load reduction schedule, the authors first model a microgrid system with a large number of constraints and variables to model the power generation technology and physical characteristics. Meanwhile the authors use a mixed logic dynamical framework to guarantee a reasonable behavior for grid interaction and storage and consider the influences of battery life and recession. Then for forecasting uncertainties in the microgrid, a feedback mechanism is introduced in MPC to solve the problem by using a receding horizon control. The objective of minimizing the operation costs is achieved by an MPC strategy for scheduling the behaviors of components in the microgrid. Finally, a comparative analysis has been carried out between the MPC and some traditional control methods. The MPC leads to a significant improvement in operating costs and on the computational burden. The economy and efficiency of the MPC are shown by the simulations.

2022 ◽  
pp. 96-113
Mamdouh Ahmed Ezzeldin ◽  
Ahmed Mohsen Ali ◽  
Jomana Ashraf Mahmoud ◽  
Sohaila Ahmed Rabie ◽  
Hossam Hassan Ammar

Electrical vehicles are the future of the world; hence, there is a necessity to pave the way for the upcoming technology and to ensure its contribution to the society fairly. Nevertheless, if the EVs completely replaced the fuel-based cars, more EV charging stations would be needed which might develop overconsumption of the main grid power causing remarkable instability. Consequently, the micro grids become the solution to this problem, in which they are defined as relatively small networks of energy sources and loads at the distribution level that aim to provide electricity to remote locations where the charging stations are located. In this chapter, the EV is considered as a load to the micro grid indirectly through the EV charging stations. Thus, micro grid loads will be retrieved from experimental data of an actual prototype electric vehicle to reflect on the battery degradation in a micro-grid connected system.

2022 ◽  
Vol 306 ◽  
pp. 118134
Chia-Wei Hsu ◽  
Rui Xiong ◽  
Nan-Yow Chen ◽  
Ju Li ◽  
Nien-Ti Tsou

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