Experimentally Validated Coulomb Counting Method for Battery State-of-Charge Estimation under Variable Current Profiles

Zine, Bachir; Bia, Haithem; Benmouna, Amel; Becherif, Mohamed; Iqbal, Mehroze

doi:10.3390/en15218172

Cited by 12 publications

(3 citation statements)

References 49 publications

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“…The Coulomb counting method was used in this study to calculate lithium-polymer battery capacity and SOC value, which were then used to train deep learning networks. The coulomb counting method is given in Equation 3 [42].…”

Section: Lithium Polymer Battery Datasetmentioning

confidence: 99%

Performance Comparison of Lithium Polymer Battery SOC Estimation Using GWO-BiLSTM and Cutting-Edge Deep Learning Methods

Taş

Bal

Uysal

2023

Preprint

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In this study, the GWO-BiLSTM method has been proposed by successfully estimating the SOC with the BiLSTM deep learning method using the hyper-parameter values determined by the GWO method of the lithium polymer battery. In studies using deep learning methods, it is important to solve the problems of underfitting, overfitting, and estimation error by determining the hyper-parameters appropriately. EV, HEV, and robots are used more healthily with the successful, reliable, and fast SOC estimation, which has an important place in the Battery Management System. The success of the proposed method was verified by comparing the cutting-edge data-based deep learning methods and the BiLSTM method with the SOC estimation MAE, MSE, RMSE, and Runtime(s) metrics. In the comparison, the prediction successes of the BiLSTM method, which was trained with the optimal hyper-parameter values obtained by the GWO method, with the cutting-edge deep learning methods trained with the hyper-parameter values obtained through trial and error were compared. The GWO-BiLSTM method was the most successful method with RMSE of 0.09244% and R2 of 0.9987 values according to the average results of SOC estimation made with the lithium polymer battery data set, which was created by experiments performed at different discharge levels and is new in the literature.

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Section: Lithium Polymer Battery Datasetmentioning

confidence: 99%

Performance Comparison of Lithium Polymer Battery SOC Estimation Using GWO-BiLSTM and Cutting-Edge Deep Learning Methods

Taş

Bal

Uysal

2023

Preprint

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“…Section 3 aims to investigate the LIBs degradation in Evs, including large format cell degradation modes. Section 4 presents the The most well-known empirical models are the coulomb counting method [46][47][48][49], the amperehour counting method [50][51][52][53][54], and the event-driven aging accumulation method. The coulomb counting technique accumulates the coulombs dissipated since the start of the discharge cycle and estimates the remaining capacity based on the difference between the accumulated value and a prerecorded total charge capacity.…”

Section: [39]mentioning

confidence: 99%

A Comprehensive Review of EV Lithium-Ion Battery Degradation

Rufino Júnior,

Sanseverino,

Gallo

et al. 2023

Preprint

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Lithium-ion batteries with improved energy densities have made understanding the Solid Electrolyte Interphase (SEI) generation mechanisms that cause mechanical, thermal, and chemical failures more complicated. SEI processes reduce battery capacity and power. Thus, a review of this area's understanding is important. It is essential to know how batteries degrade in EVs to estimate battery lifespan as it goes, predict, and minimize losses, and determine the ideal time for a replacement. Lithium-ion batteries used in EVs mainly suffer two types of degradation: calendar degradation and cycling degradation. Despite the existence of several existing works in the literature, several aspects of battery degradation remain unclear or have not been analyzed in detail. This work presents a systematic review of existing works in the literature. The results of the present investigation provide insight into the complex relationships among various factors affecting battery degradation mechanisms. Specifically, this systematic review examined the effects of time, side reactions, temperature fluctuations, high charge/discharge rates, depth of discharge, mechanical stress, thermal stress, and the voltage relationship on battery performance and longevity. The results revealed that these factors interact in complex ways to influence the degradation mechanisms of batteries. For example, high charge currents and deep discharges were found to accelerate degradation, while low temperatures and moderate discharge depths were shown to be beneficial for battery longevity. Additionally, the results showed that the relationship between cell voltage and State-of-Charge (SOC) plays a critical role in determining the rate of degradation. Overall, these findings have important implications for the design and operation of battery systems, as they highlight the need to carefully manage a range of factors to maximize battery performance and longevity. The result is an analysis of the main articles published in this field in recent years. This work aims to present new knowledge about fault detection, diagnosis, and management of lithium-ion batteries based on battery degradation concepts. The new knowledge is presented and discussed in a structured and comprehensive way.

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“…For simplicity, the SoC of battery ith is calculated using the Coulomb method: first, the stored energy is calculated, and second, the result is obtained by dividing the energy by the battery capacity Ebat v [48]. For the runtime simulation, the forward Euler method was used for energy calculation.…”

Section: Battery Modelingmentioning

confidence: 99%

Linear Programming-Based Power Management for a Multi-Feeder Ultra-Fast DC Charging Station

Rubino

Esempio

2023

Energies

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The growing number of electric vehicles (EVs) affects the national electricity system in terms of power demand and load variation. Turning our attention to Italy, the number of vehicles on the road is 39 million; this represents a major challenge, as they will need to be recharged constantly when the transition to electric technology is complete. If we consider that the average power is 55 GW and the installed system can produce 120 GW of peak power, we can calculate that with only 5% of vehicles in recharging mode, the power demand increases to 126 GW, which is approximately 140% of installed power. The integration of renewable energy sources will help the grid, but this solution is less useful for handling large load variations that negatively affect the grid. In addition, some vehicles committed to public utility must have a reduced stop time and can be considered to have higher priority. The introduction of priorities implies that the power absorption limit cannot be easily introduced by limiting the number of charging vehicles, but rather by computing the power flow that respects constraints and integrates renewable and local storage power contributions. The problem formulated in this manner does not have a unique solution; in this study, the linear programming method is used to optimise renewable resources, local storage, and EVs to mitigate their effects on the grid. Simulations are performed to verify the proposed method.

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Experimentally Validated Coulomb Counting Method for Battery State-of-Charge Estimation under Variable Current Profiles

Cited by 12 publications

References 49 publications

Performance Comparison of Lithium Polymer Battery SOC Estimation Using GWO-BiLSTM and Cutting-Edge Deep Learning Methods

Performance Comparison of Lithium Polymer Battery SOC Estimation Using GWO-BiLSTM and Cutting-Edge Deep Learning Methods

A Comprehensive Review of EV Lithium-Ion Battery Degradation

Linear Programming-Based Power Management for a Multi-Feeder Ultra-Fast DC Charging Station

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