Applications of nanogenerators for biomedical engineering and healthcare systems

Wang, Wanli; Pang, Jinbo; Su, Jie; Li, Fujiang; Li, Qiang; Wang, Xiaoxiong; Wang, Jingang; Ibarlucea, Bergoi; Liu, Xiaoyan; Li, Yufen; Zhou, Weijia; Wang, Kai; Han, Qingfang; Liu, Lei; Zang, Ruohan; Rümmeli, Mark H.; Li, Yang; Liu, Hong; Han, Hwataik; Cuniberti, Gianaurelio

doi:10.1002/inf2.12262

Cited by 59 publications

(66 citation statements)

References 341 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…111 Further research is needed in practical applications, as its algorithm is too complex and requires much computation. [112][113][114] The model-based method allows for relatively easy online measurement of battery SOC. There are fewer components for the equivalent circuit model, so the relationship between the parameters is easier to derive.…”

Section: Summarization Of Past Studiesmentioning

confidence: 99%

A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network

Cui

Wang

et al. 2021

Intl J of Energy Research

Self Cite

205

View full text Add to dashboard Cite

Implementing carbon neutrality and emission peak policies requires a highlevel electric vehicle field. Lithium-ion batteries have been considered an essential component of electric vehicle power batteries. Effective state of charge (SOC) estimation for lithium-ion batteries is a critical problem that needs to be addressed at present. With the feature extraction and fitting capability, the neural network can achieve accurate SOC estimation without considering the internal electrochemical state of the battery. This article overviews the definition of SOC and the relationship with battery aging state. Then, by examining recent literature on estimating the SOC of Lithium-ion batteries using neural network methods, the methods are classified into three categories: feed-forward neural network method, deep learning method, and hybrid method. The progress of neural network methods in SOC estimation applications is systematically reviewed, including principles, advantages, disadvantages, current status, and estimation errors. Possible recommendations for next-generation intelligent battery management systems and SOC estimation are also presented. This review's highlighted insights will inspire researchers in the battery field and point the way to developing electric vehicles.

show abstract

Section: Summarization Of Past Studiesmentioning

confidence: 99%

A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network

Cui

Wang

et al. 2021

Intl J of Energy Research

Self Cite

205

View full text Add to dashboard Cite

show abstract

“…However, with the continuous development of electric vehicles, intelligent electronics, and other fields, higher requirements are put forward for the capacity, safety, and cycle life of rechargeable batteries 5 . Among numerous energy storage batteries, lithium‐ion batteries (LIBs) have become the most mainstream energy storage element due to their low self‐discharge rate, large battery capacity, long cycle life, and no memory effect have been widely applied to electric equipment such as rechargeable cars and mobile phones 6–10 . By 2020, more than 90% of large battery storage capacity in the United States will be provided by LIBs.…”

Section: Introductionmentioning

confidence: 99%

“…5 Among numerous energy storage batteries, lithium-ion batteries (LIBs) have become the most mainstream energy storage element due to their low self-discharge rate, large battery capacity, long cycle life, and no memory effect have been widely applied to electric equipment such as rechargeable cars and mobile phones. [6][7][8][9][10] By 2020, more than 90% of large battery storage capacity in the United States will be provided by LIBs.…”

Section: Introductionmentioning

confidence: 99%

Temperature prediction of lithium‐ion batteries based on electrochemical impedance spectrum: A review

Wang

Duan

et al. 2022

Intl J of Energy Research

Self Cite

View full text Add to dashboard Cite

Summary With the rapid development of global electric vehicles, artificial intelligence, and aerospace, lithium‐ion batteries (LIBs) have become more and more widely used due to their high property. More and more disasters are caused by battery combustion. Among them, the temperature prediction of LIBs is the key to prevent the occurrence of fire. At present, using surface temperature sensor to measure the temperature of LIBs is the main method. High‐capacity LIB packs used in electric vehicles and grid‐tied stationary energy storage system essentially consist of thousands of individual LIB cells. Therefore, installing a physical sensor at each cell, especially at the cell core, is not practically feasible from the solution cost, space, and weight point of view. So developing a new method for battery temperature prediction has become an urgent problem to be solved. Electrochemical impedance spectroscopy (EIS) is a widely applied non‐destructive method of characterization of LIBs. In recent years, methods of predicting LIBs temperature by EIS have been developed. The prediction of LIBs temperature based on EIS has the advantages of high real‐time performance and prediction accuracy, and the device is simple and practical. The proposed method has a good development prospect in electric vehicles and other fields and can effectively solve the current problems of LIBs temperature prediction. Therefore, it is urgent to summarize these works to promote the next development. This review summarizes the main methods of using EIS to predict the temperature of LIBs in recent years, including the methods based on the impedance, phase shift, and intercept frequency. The principle and application of various methods are reviewed. The advantages and disadvantages of different methods and the future development direction are discussed. Highlights Use EIS to quickly and effectively predict the internal temperature changes of LIBs. No hardware temperature sensors and thermal model are required. The methods to predict battery temperature based on impedance, phase shift, and intercept frequency are reviewed.

show abstract

“…Among the bulkiest parts of an IMD are the implanted power sources, which may include non-rechargeable batteries that have to be periodically replaced [ 10 ], wirelessly rechargeable batteries (transcutaneous charging) [ 11 ], and self-charging nanogenerators [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…Some of the biomedical applications of PENGs are presented in References [ 10 , 11 , 12 , 17 , 18 , 19 , 20 , 21 , 22 , 23 , 24 ], for example. PENG-powered IMDs inserted directly in the brain have been suggested by some authors [ 11 , 23 ].…”

Section: Introductionmentioning

confidence: 99%

Optimized Design of a Self-Biased Amplifier for Seizure Detection Supplied by Piezoelectric Nanogenerator: Metaheuristic Algorithms versus ANN-Assisted Goal Attainment Method

et al. 2022

View full text Add to dashboard Cite

This work is dedicated to parameter optimization for a self-biased amplifier to be used in preamplifiers for the diagnosis of seizures in neuro-diseases such as epilepsy. For the sake of maximum compactness, which is obligatory for all implantable devices, power is to be supplied by a piezoelectric nanogenerator (PENG). Several meta-heuristic optimization algorithms and an ANN (artificial neural network)-assisted goal attainment method were applied to the circuit, aiming to provide us with the set of optimal design parameters which ensure the minimal overall area of the preamplifier. These parameters are the slew rate, load capacitor, gain–bandwidth product, maximal input voltage, minimal input voltage, input voltage, reference voltage, and dissipation power. The results are re-evaluated and compared in the Cadence 180 nm SCL environment. It has been observed that, among the metaheuristic algorithms, the whale optimization technique reached the best values at low computational cost, decreased complexity, and the highest convergence speed. However, all metaheuristic algorithms were outperformed by the ANN-assisted goal attainment method, which produced a roughly 50% smaller overall area of the preamplifier. All the techniques described here are applicable to the design and optimization of wearable or implantable circuits.

show abstract

Applications of nanogenerators for biomedical engineering and healthcare systems

Cited by 59 publications

References 341 publications

A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network

A comprehensive review on the state of charge estimation for lithium‐ion battery based on neural network

Temperature prediction of lithium‐ion batteries based on electrochemical impedance spectrum: A review

Optimized Design of a Self-Biased Amplifier for Seizure Detection Supplied by Piezoelectric Nanogenerator: Metaheuristic Algorithms versus ANN-Assisted Goal Attainment Method

Contact Info

Product

Resources

About