A Passive Battery Management System for Fast Balancing of Four LiFePO4 Cells

Perişoară, Lucian Andrei; Guran, Ionut Constantin; Costache, Dumitrel Catalin

doi:10.1109/siitme.2018.8599258

Cited by 31 publications

(10 citation statements)

References 5 publications

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“…Balancing the cells at different charge current reduces the balancing time by 20% [18]. The power resistors and power transistors are used as main components to dissipate the excess energy [19]. But, the power dissipation across each resistor and transistor is approximatively 7.25W and 3.6W.…”

Section: A Passive Cell Balancing Approachesmentioning

confidence: 99%

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Machine Learning-Based Optimal Cell Balancing Mechanism for Electric Vehicle Battery Management System

Duraisamy¹,

Deepa

2021

IEEE Access

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Cell balancing is a vital function of battery management system (BMS), which is implemented to extend the battery run time and service life. Various cell balancing techniques are being focused due to the growing requirements of larger and superior performance battery packs. The passive balancing approach is the most popular because of its low cost and easy implementation. As the balancing energy is dissipated as heat by the balancing resistors, an appropriate thermal scheme of the balancing system is necessary, to keep the BMS board temperature under a tolerable limit. In this paper, optimum selection of balancing resistor with respect to degree of cell imbalance, balancing time, C-rate, and temperature rise using machine learning (ML) based balancing control algorithm is proposed to improve the balancing time and optimal power loss management. Variable resistors are utilised in the passive balancing system, in order to optimize the power loss and to obtain optimal thermal characterization. The performance of the proposed system is evaluated using back propagation neural network (BPNN), radial basis neural network (RBNN), and long short term memory (LSTM). Error analysis of the balancing system is done to optimize balancing parameters and the proposed algorithms are compared using performance indices such as mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) to validate the balancing model performance. The possible optimization scope for implementing passive balancing using machine learning algorithms are experimented in the Matlab-Simscape environment.

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Section: A Passive Cell Balancing Approachesmentioning

confidence: 99%

“…Balancing resistor value is calculated from the battery voltage and the balancing current. The equations ( 12), ( 13) and (19) indicate that the balancing resistor selection plays a key role in addressing slow balancing and heat dissipation challenges of conventional passive algorithms.…”

Section: Figure 3 Thevenin 3rc Equivalent Circuit Modelmentioning

confidence: 99%

Machine Learning-Based Optimal Cell Balancing Mechanism for Electric Vehicle Battery Management System

Duraisamy¹,

Deepa

2021

IEEE Access

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“…The proposed battery efficiency calculation formula uses the charging time, charging current, and battery capacity. An algorithm that can accurately determine the battery state is proposed by applying the proposed SoC and state of health (SoH) calculations [24]. Other studies have addressed similar research topics.…”

Section: Introductionmentioning

confidence: 99%

Performance of Passive and Active Balancing Systems of Lithium Batteries in Onerous Mine Environment

et al. 2021

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To use lithium-iron-phosphate battery packs in the supply systems of any electric mining equipment and/or machines, the required conditions of work safety must be met. This applies in particular to coal mines endangered by fire and/or explosion. To meet the spark-safety conditions, the cells (together with the battery management system—BMS) must be isolated from the influence of the environment, and therefore placed in special fire-tight housings. This significantly degrades the heat dissipation, thus affecting the operating conditions of the cell-packs. Therefore, their usage without the so-called BMS is not recommended, as shown in the authors’ preliminary research. In practice, various BMS are used, most often with the so-called passive balancing. However, their application in mines is uncertain, due to the effect of heating under operation. When it comes to active BMS, they usually possess a quite complex structure and hence, are relatively expensive. Therefore, the authors conducted research for two specially developed active and one commercial passive BMS cooperating with selected lithium-iron-phosphate (LiFePO4) batteries when used in a suspended mining vehicle type PCA-1. The tests were carried out under environmental temperatures ranging from +5 °C to +60 °C. The effect of mismatching (12.5% to 37.5% of total cells number) of the cell parameters on the temperature distribution and voltage fading at the terminals of individual cells was checked. As a result of the investigations, the practical usefulness of the developed active BMS was determined, enabling the extension of the lithium-iron-phosphate battery life under onerous mine conditions, for a single recharge, which is a novelty. On the basis of the obtained results, appropriate practical conclusions were formulated.

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“…It removes excess energy from the higher voltage cells by bypassing the current of the cell, and waits until it is the same level as low voltage cells. This can be categorized into two subcategories: fixed or switched resistor balancing methods [18][19][20][21][22]. The first method uses continuous bypassing of the current for all cells.…”

Section: Introductionmentioning

confidence: 99%

“…Furthermore, a Bluetooth module is designed to command the microcontroller to turn on/off the charger via voice/text message by using a smartphone. A notable characteristic of the system is its ability to carry a very-low current; as a result, the system does not contribute significantly to the self-discharge of the battery and does not require complex control as compared to the existing passive methods [21,22,29].…”

Section: Introductionmentioning

confidence: 99%

A Smart High-Voltage Cell Detecting and Equalizing Circuit for LiFePO4 Batteries in Electric Vehicles

Moghaddam

Bossche

2019

Applied Sciences

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A battery management system (BMS) plays an important role in electric vehicles (EVs) in order to achieve a reasonable-lasting lifetime. An equalizing method is essential in order to obtain the best performance. A monitoring system is required to check if any cell voltage is high or low. In this paper, an equalizing and monitoring system for an ultra-light electric vehicle is proposed. The monitoring system detects if one cell is fully charged or all cells are fully charged and the equalizing system tops each cell at the desired voltage. To solve this issue, a light-emitting diode (LED) band gap is used as a voltage reference to inform the user if any cell is at its high voltage. A smart monitoring displays on the liquid crystal display (LCD), if one cell is high or all cells are high. This detection also provides a signal to the microcontroller to turn on/off the charger if all cells are high. Also, a Bluetooth module was designed to command the microcontroller the charger to turn on/off via voice/text message by using a smartphone. Additionally, a new smart monitoring system based on the Bluetooth model (HC05) and mobile app has been made in order to monitor individual cell voltage. A major feature of the system is to draw a very-low current, so that the system does not contribute significantly to the self-discharge of the battery and the circuit does not need sophisticated control. Manufacturers of large electric vehicles may have more intelligent systems that may require a permanent connection to the grid and allow high standby losses, where more state of charge (SOC) may be lost per day. The paper is rather focused on reducing the standby losses, and to activate the equalizer only when charging and/or driving. The experimental results are performed in order to verify the feasibility of the proposed circuit.

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A Passive Battery Management System for Fast Balancing of Four LiFePO4 Cells

Cited by 31 publications

References 5 publications

Machine Learning-Based Optimal Cell Balancing Mechanism for Electric Vehicle Battery Management System

Machine Learning-Based Optimal Cell Balancing Mechanism for Electric Vehicle Battery Management System

Performance of Passive and Active Balancing Systems of Lithium Batteries in Onerous Mine Environment

A Smart High-Voltage Cell Detecting and Equalizing Circuit for LiFePO4 Batteries in Electric Vehicles

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