Blind and task-ware multi-cell battery management system

Motaqi, Ahmadreza; Mosavi, Mohammad Reza

doi:10.1016/j.jestch.2019.07.005

Cited by 10 publications

(7 citation statements)

References 16 publications

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“…The experimental verification is presented for the clear battery recovery effect in wearable devices. The power management schemes that are used for the utilization of the recovery effect can increase the lifetime of the sensor devices [6,7].…”

Section: Related Workmentioning

confidence: 99%

A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications

et al. 2021

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The internet of things (IoT) comprises various sensor nodes for monitoring physiological signals, for instance, electrocardiogram (ECG), electroencephalogram (EEG), blood pressure, and temperature, etc., with various emerging technologies such as Wi-Fi, Bluetooth and cellular networks. The IoT for medical healthcare applications forms the internet of medical things (IoMT), which comprises multiple resource-restricted wearable devices for health monitoring due to heterogeneous technological trends. The main challenge for IoMT is the energy drain and battery charge consumption in the tiny sensor devices. The non-linear behavior of the battery uses less charge; additionally, an idle time is introduced for optimizing the charge and battery lifetime, and hence the efficient recovery mechanism. The contribution of this paper is three-fold. First, a novel adaptive battery-aware algorithm (ABA) is proposed, which utilizes the charges up to its maximum limit and recovers those charges that remain unused. The proposed ABA adopts this recovery effect for enhancing energy efficiency, battery lifetime and throughput. Secondly, we propose a novel framework for IoMT based pervasive healthcare. Thirdly, we test and implement the proposed ABA and framework in a hardware platform for energy efficiency and longer battery lifetime in the IoMT. Furthermore, the transition of states is modeled by the deterministic mealy finite state machine. The Convex optimization tool in MATLAB is adopted and the proposed ABA is compared with other conventional methods such as battery recovery lifetime enhancement (BRLE). Finally, the proposed ABA enhances the energy efficiency, battery lifetime, and reliability for intelligent pervasive healthcare.

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Section: Related Workmentioning

confidence: 99%

A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications

et al. 2021

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“…For the analyzed power and energy management strategies, the lifetime expectancy of the Li-ion battery ESS varies between 8.5 years and 13.5 years. The blind and task-ware scheduling methods are proposed in [30] to prolong battery lifetime. The blind method uses a neural network time step estimator to find the optimum time step for batteries at different discharge currents without any contemplation about drawn current patterns.…”

Section: Power and Energy Management Strategiesmentioning

confidence: 99%

“…The lifetime improvement of the aforementioned power and energy management strategies are summarized in Table III. Neural network time step estimator [30] The lifetime is prolonged by around 31% High Dynamic programming algorithm [31] The aging rate has been reduced by 48.9%…”

Section: Load Profiles (Ctudc)mentioning

confidence: 99%

Overview of Methods for Battery Lifetime Extension

Jin

Huang

Sui

et al. 2021

2021 23rd European Conference on Power Electronics and Applications (EPE'21 ECCE Europe)

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Lithium-ion (Li-ion) batteries are widely used in transportation, aerospace, and electrical. How to extend their lifetime has become an important topic. In this paper, the methods for battery lifetime extension in terms of thermal management, charging/discharging optimization, and power and energy management control strategies are reviewed. Firstly, this paper summarizes and classifies the methods proposed in recent years to extend battery lifetime. Secondly, the advantages and drawbacks of each method are compared in detail. Finally, the advancement of various methods is summarized and prospect for future research direction on battery lifetime extension is provided.

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“…RUL prediction methods can be divided into three categories. Figure 1 shows the main RUL prognosis methods for Li-ion batteries (Motaqi and Mosavi, 2020;Xiong et al, 2019;Lucu et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods

et al. 2021

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Lithium-ion batteries play an important role in our daily lives. The prediction of the remaining service life of lithium-ion batteries has become an important issue. This article reviews the methods for predicting the remaining service life of lithium-ion batteries from three aspects: machine learning, adaptive filtering, and random processes. The purpose of this study is to review, classify and compare different methods proposed in the literature to predict the remaining service life of lithium-ion batteries. This article first summarizes and classifies various methods for predicting the remaining service life of lithium-ion batteries that have been proposed in recent years. On this basis, by selecting specific criteria to evaluate and compare the accuracy of different models, find the most suitable method. Finally, summarize the development of various methods. According to the research in this article, the average accuracy of machine learning is 32.02% higher than the average of the other two methods, and the prediction cycle is 9.87% shorter than the average of the other two methods.

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Blind and task-ware multi-cell battery management system

Cited by 10 publications

References 16 publications

A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications

A Novel Adaptive Battery-Aware Algorithm for Data Transmission in IoT-Based Healthcare Applications

Overview of Methods for Battery Lifetime Extension

A Critical Review of Online Battery Remaining Useful Lifetime Prediction Methods

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