Active Cell Balancing Control Method for Series-Connected Lithium-Ion Battery

Qays, Ohirul; Buswig, Yonis M.; Anyi, Martin

doi:10.35940/ijitee.i8905.078919

Cited by 13 publications

(3 citation statements)

References 20 publications

(27 reference statements)

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“…Due to the well-designed NN models from BES, the estimation error of SOC decreases compared to the KF or HF methods [55]. The NN sometimes tends to hit the extreme local minimum when learning, therefore, it is necessary to supervise the architecture and learning process very exactly [56,57].…”

Section: Neural Networkmentioning

confidence: 99%

Archives of Electrical Engineering

Hallmann,

Wenge,

Komarnicki

et al. 2022

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The use of lithium-ion battery energy storage (BES) has grown rapidly during the past year for both mobile and stationary applications. For mobile applications, BES units are used in the range of 10-120 kWh. Power grid applications of BES are characterized by much higher capacities (range of MWh) and this area particularly has great potential regarding the expected energy system transition in the next years. The optimal operation of BES by an energy storage management system is usually predictive and based strongly on the knowledge about the state of charge (SOC) of the battery. The SOC depends on many factors (e.g. material, electrical and thermal state of the battery), so that an accurate assessment of the battery SOC is complex. The SOC intermediate prediction methods are based on the battery models. The modeling of BES is divided into three types: fundamental (based on material issues), electrical equivalent circuit (based on electrical modeling) and balancing (based on a reservoir model). Each of these models requires parameterization based on measurements of input/output parameters. These models are used for SOC modelbased calculation and in battery system simulation for optimal battery sizing and planning. Empirical SOC assessment methods currently remain the most popular because they allow practical application, but the accuracy of the assessment, which is the key factor for optimal operation, must also be strongly considered. This scientific contribution is divided into two papers. Paper part I will present a holistic overview of the main methods of SOC assessment. Physical measurement methods, battery modeling and the methodology of using the model as a digital twin of a battery are addressed and discussed. Furthermore, adaptive methods and methods of artificial intelligence, which are important for the SOC calculation, are presented. In paper part II, examples of the application areas are presented and their accuracy is discussed.

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Section: Neural Networkmentioning

confidence: 99%

Archives of Electrical Engineering

Hallmann,

Wenge,

Komarnicki

et al. 2022

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“…The proposed method can also be used in renewable energy dependent devices as well as energy conversion systems [42][43][44][45][46][47][48][49][50]. It can also be adapted to monitor the condition of critical high voltage assets, power converters, and various flexible ac transmission system devices [51][52][53][54].…”

Section: Ishdb Tested With Iot Implementationmentioning

confidence: 99%

Cost-Effective Design of IoT-Based Smart Household Distribution System

Ahmed

Qays

Abu-Siada

et al. 2021

Designs

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The Internet of Things (IoT) plays an indispensable role in present-day household electricity management. Nevertheless, practical development of cost-effective intelligent condition monitoring, protection, and control techniques for household distribution systems is still a challenging task. This paper is taking one step forward into a practical implementation of such techniques by developing an IoT Smart Household Distribution Board (ISHDB) to monitor and control various household smart appliances. The main function of the developed ISHDB is collecting and storing voltage, current, and power data and presenting them in a user-friendly way. The performance of the developed system is investigated under various residential electrical loads of different energy consumption profiles. In this regard, an Arduino-based working prototype is employed to gather the collected data into the ThingSpeak cloud through a Wi-Fi medium. Blynk mobile application is also implemented to facilitate real-time monitoring by individual consumers. Microprocessor technology is adopted to automate the process, and reduce hardware size and cost. Experimental results show that the developed system can be used effectively for real-time home energy management. It can also be used to detect any abnormal performance of the electrical appliances in real-time through monitoring their individual current and voltage waveforms. A comparison of the developed system and other existing techniques reveals the superiority of the proposed method in terms of the implementation cost and execution time.

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“…IoT system relying on Arduino microcontroller supercapacitor was used for increasing the efficiency of the energy-storing system. Moreover, in [9][10][11][12][13][14][15], a comprehensive study shows a review of monitoring technologies for solar PV Systems using data processing modules and transmission protocols. In a related study [15][16][17][18][19][20][21][22], a cost-effective design of an IoT-based smart household distribution system was presented.…”

Section: Introductionmentioning

confidence: 99%

Integration of Solar Energy Supply on Smart Distribution Board Based on IoT System

Mallahi¹,

Mohamed²,

Shaker

2022

Designs

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Today’s residential electricity management depends heavily on the Internet of Things (IoT). It is still challenging to create practical, affordable smart condition monitoring, protection, and control systems for residential distribution networks. The performance of the developed system is evaluated for a variety of residential electric loads with different energy consumption patterns. In this paper, a photovoltaic tracking system and also a cleaning system will be connected to a smart board. All input data will be gathered and sent over Bluetooth using a working prototype built on an Arduino board. The mobile application is also used to let individual users monitor the system in real-time, alert users about electrical system issues, display their energy use in real time, and provide other essential alerts and warnings.

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Active Cell Balancing Control Method for Series-Connected Lithium-Ion Battery

Cited by 13 publications

References 20 publications

Archives of Electrical Engineering

Archives of Electrical Engineering

Cost-Effective Design of IoT-Based Smart Household Distribution System

Integration of Solar Energy Supply on Smart Distribution Board Based on IoT System

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