Non-Linear Model and Parameter Extraction for Charge/Discharge Behavior of Valve Regulated Lead-Acid Battery

Lavety, Sandhya; Keshri, Ritesh Kumar; Ghosh, Subhojit; Chaudhari, Madhuri A.

doi:10.1109/tec.2021.3062087

Cited by 6 publications

(4 citation statements)

References 20 publications

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“…In this subsection, an algorithm has been discussed to measure and monitor the battery temperature effectively in order to protect the battery from the thermal runway and also enhance the battery life span. The following discrete formulation has been used to monitor battery temperature 36

{\theta}_{uc}\left(k+1\right)={\theta}_{uc}(k)+\frac{R_s\ {t}_s\ {I}_{opt}^2(k)}{mC_p}-\left(\lambda {t}_s\right)\left\{{\theta}_{uc}(k)-{\theta}_a\right\}

where λ and t s depict the rate of cooling and sampling time, respectively.…”

Section: Battery Thermal Modelingmentioning

confidence: 99%

Fractional order modeling based optimal multistage constant current charging strategy for lithium iron phosphate batteries

Rao,

Chappa,

Chaitanya

et al. 2024

Energy Storage

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The primary power source for electric vehicles (EVs) is batteries. Due to the superior characteristics like higher energy density, power density, and life cycle of the lithium iron phosphate (LFP) battery is most frequently chosen among the various types of lithium‐ion batteries (LIBs). The main issues that users encounter are the time required to charge an EV battery and the safety of the EV battery during the charging period. The fast‐charging means, charging a battery with high currents which may lead to a rise in the temperature of a battery. The abrupt rise in battery temperature may cause changes in the internal chemical structures of the battery, reducing battery life even further. In this regard, an optimal charging profile design is of utmost importance in order to satisfy dual objectives simultaneously such as less charging time and improvement in life of the battery. To overcome the conflict between charging speed and rise in temperature an optimal multistage constant current (MSCC) based charging strategy has been investigated under different operating conditions. In addition, the proposed charging profiles have been studied using experimentation.

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{\theta}_{uc}\left(k+1\right)={\theta}_{uc}(k)+\frac{R_s\ {t}_s\ {I}_{opt}^2(k)}{mC_p}-\left(\lambda {t}_s\right)\left\{{\theta}_{uc}(k)-{\theta}_a\right\}

where λ and t s depict the rate of cooling and sampling time, respectively.…”

Section: Battery Thermal Modelingmentioning

confidence: 99%

Fractional order modeling based optimal multistage constant current charging strategy for lithium iron phosphate batteries

Rao,

Chappa,

Chaitanya

et al. 2024

Energy Storage

View full text Add to dashboard Cite

show abstract

“…The model would be improved by considering a non-zero constant time to simulate the current direction's hysteresis fluctuation. The model represented by Equation ( 9) is simply replaced by the equation (20), where the sign is the function with value 1 if the current i(t) is positive and −1 if the current i(t) is negative.…”

Section: Simulation Of the Agingmentioning

confidence: 99%

“…How et al in [19] describes the newest state of charge (SoC) estimation for Li-Ion batteries, which is required to increase the service life of batteries and prevent emergencies caused by battery failures. Studies of Lavety and Zhou [20,21] are dedicated to modern problems of rechargeable batteries simulation and estimation of batteries capacity.…”

Section: Introductionmentioning

confidence: 99%

Accessible Battery Model with Aging Dependency

Savard¹,

Iakovleva

Ivanchenko

et al. 2021

Energies

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Designed to store and discharge electrical energy, rechargeable batteries consist of elementary storage cell assemblies. Aging is affected by various aggravating factors, mainly temperature. There are many electric or electrochemical models which describe their operation. Most standard models do not consider the aging phenomena of batteries and their consequences, while batteries deteriorate when used or stored. Precisely, most battery models do not simulate the influence of cell aging on other cells. The model presented in this paper incorporates aging and the effects of mutual interactions between cells. The model can be established based on four measurement points on the cell characteristic curve and allows the simulation of a single cell’s or multiple coupled cells’ behavior. The model can then be easily implemented in simulation software like Matlab.

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“…In work [28], researchers reported that to delay battery degradation, the charging process can be significantly affected by adjusting the charging current and cut-off voltage. In work [29], researchers studied the aging behavior of batteries during long-term cycling at high charging rates to explain the aging effects of batteries. In this paper, based on these reports, we propose a model for accurately estimating the state of health (SoH) of gel batteries under different operating conditions.…”

Section: Introductionmentioning

confidence: 99%

Bidirectional Long Short-Term Memory Model of SoH Prediction for Gelled-Electrolyte Batteries under Charging Conditions

Kuo,

Chao

2023

Gels

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The impact of different charging currents and surrounding temperatures has always been an important aspect of battery lifetime for various electric vehicles and energy storage equipment. This paper proposes a bidirectional long short-term memory model to quantify these impacts on the aging of gel batteries and calculate their state of health. The training data set of the bidirectional long short-term memory model is collected by charging and discharging the gel battery for 300 cycles in a temperature-controlled box and an automated charge and discharge device under different operating conditions. The testing set is generated by a small energy storage device equipped with small solar panels. Data for 220 cycles at different temperatures and charging currents were collected during the experiment. The results show that the mean absolute error (MAE) and root-mean-square error (RMSE) between the training set and testing set are 0.0133 and 0.0251, respectively. In addition to the proposed model providing high accuracy, the gel battery proved to be stable and long-lasting, which makes the gel battery an ideal energy storage solution for renewable energy.

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Non-Linear Model and Parameter Extraction for Charge/Discharge Behavior of Valve Regulated Lead-Acid Battery

Cited by 6 publications

References 20 publications

Fractional order modeling based optimal multistage constant current charging strategy for lithium iron phosphate batteries

Fractional order modeling based optimal multistage constant current charging strategy for lithium iron phosphate batteries

Accessible Battery Model with Aging Dependency

Bidirectional Long Short-Term Memory Model of SoH Prediction for Gelled-Electrolyte Batteries under Charging Conditions

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