Mathematical Modeling of Li-Ion Battery Using Genetic Algorithm Approach for V2G Applications

Thirugnanam, Kannan; Joy, T.; Singh, Mukesh; Kumar, Praveen

doi:10.1109/tec.2014.2298460

Cited by 112 publications

(14 citation statements)

References 34 publications

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“…Various charging/discharging rates using the polynomial coefficients found at specified charge/discharge rates are calculated by Battery terminal voltage. Consequently, the constant current, the battery terminal voltage for charging (Vbc) and discharging scenario (Vbd) for time is as given [27]. Calculating battery terminal voltage for charging and discharging various Cr and Dr is performed using equations. )…”

Section: Battery Terminal Voltage Calculationsmentioning

confidence: 99%

Development of Internet of Things-Enabled Smart Battery Management System

Ekuewa

Afolabi

Ajibesin

et al. 2022

EJECE

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The Internet of Things has become an evolving area of wireless technologies. Energy storage plays a crucial role in developing, implementing, and deploying the Internet of Things (IoT) to help solve some of the problems facing our daily activities. It is known that in batteries, energy-stored systems are able to get spoiled due to undercharging or over-charging and might profoundly affect the operation of the whole system. Electric and hybrid vehicles need an exact state-of-charge approximation technique to improve battery safety and lifetime and prevent damage. An efficient battery managing system is vital to accurately indicate the battery operating temperature and state of charge and protect the Battery against cell disproportion. This paper presents a simulation-based Battery Management System (BMS) for e-bikes, it was implemented on Arduino Nano. The battery parameters such as Temperature, Voltage, Current, State-of-Charge (SoC), and Depth-of-Discharging (DoD) will be sent to a cloud server via the BOLT IoT module, and a data analysis tool will be used to approximate bad battery pack cells. To effectively estimate the state-of charge of the system battery, three different mathematical models for a single and packed battery management system techniques, namely, Coulomb Counting (CC), the Unscented Kalman filter (UKF), and the Extended Kalman Filter (EKF), were proposed and explained in this study. The battery Management System is expected to improve the overall performance of the IoT-enabled systems and will also contribute to mass acceptance of renewable energy systems since Battery Management systems would be one of its key components.

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Section: Battery Terminal Voltage Calculationsmentioning

confidence: 99%

Development of Internet of Things-Enabled Smart Battery Management System

Ekuewa

Afolabi

Ajibesin

et al. 2022

EJECE

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“…The parameters of the original CIEMAT model are fixed [8,9] as per Equation (12). This study proposes to loosen four parameters (V OCP , a 1 , a 2 and a 3 ) of CIEMAT model as per Equation (13). Deep-discharge experiments were carried out at room temperature, so that ∆Tis ignored.…”

Section: Linear Aging Filter Of Internal Resistance Based On Contactimentioning

confidence: 99%

“…However, in typical voltage models, such as Shepherd [7], CIEMAT [8,9] and Monegon [10], the remaining useful capacity itself is one of the parameters. The interpretation of polynomials and nonlinear functions of deep-discharge curves needs brute-force numeric efforts, including generic [11][12][13] and artificial neural-network [14] intelligent algorithms. The remaining useful capacity can also be directly calculated with floating service time by the linear Kalman filter, but the turning point from stability to deterioration is required [15,16].…”

Section: Introductionmentioning

confidence: 99%

Failure Warning at the End of Service-Life of Lead–Acid Batteries for Backup Applications

et al. 2020

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The prediction of remaining useful life is an important function of battery management systems. Existing research typically focused on factors that determine the quantity of the remaining useful capacity, and are able to determine the remaining useful capacity several years before battery failure to counter hysteresis of variables of lead–acid batteries. These techniques are not suitable at the end of service-life for backup batteries. This paper proposes a linear-superposition–voltage-aging model with three improvements. First, the estimation of the deep-discharge of the proposed voltage model does not require the remaining useful capacity. Second, the internal resistance of the deep-discharge is predicted from the contacting resistance of electrochemical impedance spectroscopy. Third, a morphology correction factor of internal resistance is about to saturate at the end of battery service-life. The model accurately forecasts battery failure at the end of service-life in two groups of accelerated-aging experiments. The proposed method in this paper focuses on the factors that determine quality of remaining useful capacity to counter hysteresis of variables of lead–acid batteries and judge battery failure at the end of service-life.

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“…The GA has been preferred in this field of research where structural details of the GA algorithm implementation can be found in [98][99][100]. Nevertheless, PSO compared to GA is faster since it involves no mutation or crossover functions and usually outperforms GA for unconstrained non-linear problems such as Li-ion ECMs [101].…”

Section: Parameter Extraction With Heuristic Optimizationmentioning

confidence: 99%

Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model

et al. 2019

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Md.Sazzad.Hosen@vub.be (M.S.H.); Mohsen.Akbarzadeh.Sokkeh@vub.be (M.A.S.); Shovon.Goutam@vub.be (S.G); Joris.Jaguemont@vub.be (J.J.); Maitane.Berecibar@vub.be (M.B.); Joeri.van.mierlo@vub.be (J.V.M.) 2 Flanders Make,Abstract: A lithium-ion battery cell's electrochemical performance can be obtained through a series of standardized experiments, and the optimal operation and monitoring is performed when a model of the Li-ions is generated and adopted. With discrete-time parameter identification processes, the electrical circuit models (ECM) of the cells are derived. Over their wide range, the dual-polarization (DP) ECM is proposed to characterize two prismatic cells with different anode electrodes. In most of the studies on battery modeling, attention is paid to the accuracy comparison of the various ECMs, usually for a certain Li-ion, whereas the parameter identification methods of the ECMs are rarely compared. Hence in this work, three different approaches are performed for a certain temperature throughout the whole SoC range of the cells for two different load profiles, suitable for light-and heavy-duty electromotive applications. Analytical equations, least-square-based methods, and heuristic algorithms used for model parameterization are compared in terms of voltage accuracy, robustness, and computational time. The influence of the ECMs' parameter variation on the voltage root mean square error (RMSE) is assessed as well with impedance spectroscopy in terms of Ohmic, internal, and total resistance comparisons. Li-ion cells are thoroughly electrically characterized and the following conclusions are drawn: (1) All methods are suitable for the modeling, giving a good agreement with the experimental data with less than 3% max voltage relative error and 30 mV RMSE in most cases. (2) Particle swarm optimization (PSO) method is the best trade-off in terms of computational time, accuracy, and robustness. (3) Genetic algorithm (GA) lack of computational time compared to PSO and LS (4) The internal resistance behavior, investigated for the PSO, showed a positive correlation to the voltage error, depending on the chemistry and loading profile.

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Mathematical Modeling of Li-Ion Battery Using Genetic Algorithm Approach for V2G Applications

Cited by 112 publications

References 34 publications

Development of Internet of Things-Enabled Smart Battery Management System

Development of Internet of Things-Enabled Smart Battery Management System

Failure Warning at the End of Service-Life of Lead–Acid Batteries for Backup Applications

Comparative Study on Parameter Identification Methods for Dual-Polarization Lithium-Ion Equivalent Circuit Model

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