Development of a Matlab/Simulink Model for Monitoring Cell State-of-Health and State-of-Charge via Impedance of Lithium-Ion Battery Cells

Kim, Jonghyeon; Kowal, Julia

doi:10.3390/batteries8020008

Cited by 19 publications

(13 citation statements)

References 34 publications

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“…This uses the SoC to determine the remaining energy and is expressed as 0%, 10%, 100%, etc. 0% corresponds to an empty battery and 100% corresponds to a fully charged battery [17,20]. Accurate estimation of SoC information required for battery management systems and charging control is difficult and complex due to limited models and parametric uncertainties between models.…”

Section: Batteriesmentioning

confidence: 99%

“…In addition, machine-based methods such as artificial neural networks and prediction logic provide a better estimate of the state of charge by taking non-consumption use into account. However, the prediction processes of these models are offline since the learning processes are very computational [20]. Equation ( 1) can be used to calculate SoC, which expresses the ratio of remaining capacity to rated capacity [16].…”

Section: Batteriesmentioning

confidence: 99%

“…In order to determine the resistance and capacity values of 𝑅 ; , 𝑅 C , 𝐶 C , 𝑅 D , 𝐶 D which vary according to temperature and current charge state, the operating functions subsystem is created using the data tables found in literature [20,29,31] and obtained from the tests carried out by the companies. Using the literature, the data table given in Table 1 is created for resistance and capacity values.…”

Section: Figure 3 Model Of Thermal Calculations Subsystemmentioning

confidence: 99%

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Detailed Analysis of Li-ion Batteries for Use in Unmanned Aerial Vehicles

Kaya,

Ural Bayrak

2024

Turkish Journal of Science and Technology

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With the developing technologies in the field of aviation, the transition to more electrical systems is increasing day by day. For this reason, research on the development of batteries has accelerated. Nowadays, Lithium ion (Li-ion) batteries are more widely preferred due to their energy-to-weight ratio and advantages such as having a lower self-discharge rate when not working compared to other battery technologies. Batteries produce energy as a result of chemical reactions and heat is released. The heat released negatively affects the battery's lifespan, charging/discharging time and battery output voltage. In order to see these negative effects and intervene in time, the battery must be modeled correctly. In this way, negative situations that may occur in the battery can be intervened at the right time without any incident. In this study, the unmanned aerial vehicle (UAV) is powered by Li-ion batteries. It is simulated in Matlab/Simulink environment using the electrical equivalent circuit. A detailed model is created, taking into account temperature, state of charge (SoC), cell dynamics and operating functions. To estimate state of health (SoH) of the battery, resistance values must be known. Resistance and capacity values in the equivalent circuit of the Li-ion battery are obtained with the help of the simulation model. The SoH of the Li-ion batteries can be accurately predicted with the results obtained.

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Section: Batteriesmentioning

confidence: 99%

Section: Batteriesmentioning

confidence: 99%

Section: Figure 3 Model Of Thermal Calculations Subsystemmentioning

confidence: 99%

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Detailed Analysis of Li-ion Batteries for Use in Unmanned Aerial Vehicles

Kaya,

Ural Bayrak

2024

Turkish Journal of Science and Technology

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“…The Wheatstone bridge consists of two resistors with a resistance of 120 Ω, two strain gauges and a potentiometer. Selecting 120 Ω strain gauge can reduce the heat generated by the strain gauge [15]. Since the resistance between the strain gauge and the resistance is not the standard 120 Ω, a certain bias voltage will be generated when the strain gauge is not deformed.…”

Section: Strain Measurement Modulementioning

confidence: 99%

Design of Remote Monitoring System for Lithium Battery Operating Statu

Wang

2022

JERR

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The safety of lithium batteries has always been an important problem that restricts the rapid development of lithium batteries. In view of the problems such as the untimely and difficult monitoring of the thermal runaway behavior of the lithium battery energy storage system, a remote monitoring system for the operating state of lithium batteries based on ZigBee and NB-IOT technology is designed. The real-time collection of the operating parameters of lithium batteries is realized through various sensors, the whole system is constructed by the Internet of things technology, and the uploading of operating parameters is realized through the network. It is verified that the system can operate stably, collect the operating parameters of lithium battery and upload relevant data in real time, facilitate the remote monitoring of staff, and provide a strong guarantee for the safety of lithium battery energy storage system.

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“…With the battery model, a closed-loop state estimation algorithm based on modern control theory are often applied to improve SOC estimation accuracy. Extended Kalman filtering (EKF) and particle filter (PF) algorithms are the most commonly used algorithms, which achieve good results in the former studies [15][16][17].…”

Section: Literature Review 121 State Of Chargementioning

confidence: 99%

An Electrical–Thermal Coupling Model with Artificial Intelligence for State of Charge and Residual Available Energy Co-Estimation of LiFePO4 Battery System under Various Temperatures

Mao

Han

et al. 2022

Batteries

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The LiFePO4 (LFP) battery tends to underperform in low temperature: the available energy drops, while the state of charge (SOC) and residual available energy (RAE) estimation error increase dramatically compared to the result under room temperature, which causes mileage anxiety for drivers. This paper introduces an artificial intelligence-based electrical–thermal coupling battery model, presents an application-oriented procedure to estimate SOC and RAE for a reliable and effective battery management system, and puts forward a model-based strategy to control the battery thermal state in low temperature. Firstly, an LFP battery electrical model based on artificial intelligence is proposed to estimate the terminal voltage, and a thermal resistance model with an EKF estimation algorithm is established to assess the temperature distribution in the battery pack. Then, the electrical and thermal models are coupled, a closed-loop EKF algorithm is employed to estimate the battery SOC, and a fusion method is discussed. The coupled model is simulated under a given protocol and RAE can be obtained. Finally, based on the electrical–thermal coupling model and RAE calculation algorithm, a preheating method and constant power condition-based RAE estimation are discussed, and the thermal management strategy of the battery system under low temperature is formed. Results show that the estimation error of SOC can be controlled within 2% and RAE can be controlled within 4%, respectively. The preheating strategy at low temperature and low SOC can significantly improve the energy output of the battery pack system.

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Development of a Matlab/Simulink Model for Monitoring Cell State-of-Health and State-of-Charge via Impedance of Lithium-Ion Battery Cells

Cited by 19 publications

References 34 publications

Detailed Analysis of Li-ion Batteries for Use in Unmanned Aerial Vehicles

Detailed Analysis of Li-ion Batteries for Use in Unmanned Aerial Vehicles

Design of Remote Monitoring System for Lithium Battery Operating Statu

An Electrical–Thermal Coupling Model with Artificial Intelligence for State of Charge and Residual Available Energy Co-Estimation of LiFePO4 Battery System under Various Temperatures

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