Performance Validation of Electric Vehicle’s Battery Management System under state of charge estimation for lithium-ion Battery

Khalid, Mamoona; Sheikh, Shehzar Shahzad; Janjua, Abdul Kashif; Khalid, Hassan Abdullah

doi:10.1109/icecube.2018.8610969

Cited by 16 publications

(6 citation statements)

References 13 publications

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“…The electrical behavior of a dual polarization (DP) circuit is represented by following equations [17]:…”

Section: Mathematical Model Of a Batterymentioning

confidence: 99%

“…The CCM measures the rate of discharging current and integrates the current and time to find SOC [16]. However, this method depends on the accuracy of the current sensor and due to the open-loop nature of CCM, it accumulates error [17]. The last method of the data-driven approach is model-based and it is an online method that takes waveforms as inputs for models that output SOC.…”

Section: Introductionmentioning

confidence: 99%

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A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach

et al. 2020

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Batteries are combinations of electrochemical cells that generate electricity to power electrical devices. Batteries are continuously converting chemical energy to electrical energy, and require appropriate maintenance to provide maximum efficiency. Management systems having specialized monitoring features; such as charge controlling mechanisms and temperature regulation are used to prevent health, safety, and property hazards that complement the use of batteries. These systems utilize measures of merit to regulate battery performances. Figures such as the state-of-health (SOH) and state-of-charge (SOC) are used to estimate the performance and state of the battery. In this paper, we propose an intelligent method to investigate the aforementioned parameters using a data-driven approach. We use a machine learning algorithm that extracts significant features from the discharge curves to estimate these parameters. Extensive simulations have been carried out to evaluate the performance of the proposed method under different currents and temperatures.

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“…The electrical behavior of a dual polarization (DP) circuit is represented by following equations [17]:…”

Section: Mathematical Model Of a Batterymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach

et al. 2020

Self Cite

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“…An important function of BMS is to evaluate State of Charge (SOC), capacity, state of function (SOF) and State of Health (SOH). This parameter is used for monitoring and controlling in the process of charging and discharging the battery so that the life span of the system can last longer [5,9]. In this study, a battery management system performance test will be carried out for single-cell, two-cell, and three-cell battery series.…”

Section: Introductionmentioning

confidence: 99%

Battery Management System (BMS) Performance Test with 1- Cell, 2-Cell and 3-Cell Battery Arrays

Mubarak,

Diantoro,

karim

2024

E3S Web of Conf.

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Batteries are one of the storage media for electrical energy whose development is quite significant. A battery if there is no monitoring on its use will cause damage to the battery itself such as a battery that heats up quickly, leaks and bubbles. Currently, there are quite a lot of battery management systems available as battery monitoring and controlling modules to avoid overcharge, overdischarge and overcurrent that have the potential to damage battery quality. Tests on the battery management system (BMS) as a monitoring and controlling module are carried out in this article. Tests were carried out on BMS 1S, 2S, and 3S series with Li-Ion 18650 2200mAh 3.7 V Battery type. The results of the BMS controling test show that the overcharging and overdischarging protection functions can work well. While the results of monitoring the cut off value of overcharging protection of each BMS are 3.7 V, 7.2 V, and 11.1 V. The cut off value of overdischarging protection of each BMS is 3.23 V, 6.1 V, and 9.23 V.

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“…The most frequently considered battery chemistry for e-mobility is lithium-ion due to its high energydensity, low self-discharge rates, and acceptable costs [2,3]. Several studies have focused on the health management of lithium-ion batteries for electric (on-ground) vehicles [4,5], on State-of-Health (SOH) estimation [6] and Remaining-Useful-Life (RUL) estimation [2,7]. The State-of-Charge (SOC) of the batteries has also been investigated in, for instance, [8,9].…”

Section: Introductionmentioning

confidence: 99%

Prognostics for Lithium-ion batteries for electric Vertical Take-off and Landing aircraft using data-driven machine learning

et al. 2023

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Performance Validation of Electric Vehicle’s Battery Management System under state of charge estimation for lithium-ion Battery

Cited by 16 publications

References 13 publications

A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach

A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach

Battery Management System (BMS) Performance Test with 1- Cell, 2-Cell and 3-Cell Battery Arrays

Prognostics for Lithium-ion batteries for electric Vertical Take-off and Landing aircraft using data-driven machine learning

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