Lithium-Ion batteries modeling using NARX Nonlinear model

Boujoudar, Younes; Elmoussaoui, Hassan; Lamhamdi, Tijani

doi:10.1109/wits.2019.8723705

Cited by 6 publications

(5 citation statements)

References 40 publications

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“…Computer modeling of charge-discharge processes was carried out in the LT SPICE application program package [9]. For this, based on the analysis of the known schemes for replacing the electrical circuits of the STB [10][11][12], an equivalent electrical circuit of both one section (Fig. 3.)…”

Section: Computer Simulation Of Chargedischarge Processes In the Stbmentioning

confidence: 99%

Computer modeling of charge-discharge processes in series-connected sections in storage batteries

Kogut,

Demyanchuk,

Prokopiv

2023

Phys. Chem. Sol. State

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A theoretical analysis of the initial charge and discharge processes in consecutive connected sections of the battery under the action of rectangular pulses was carried out, in particular, their influence on the uniformity of energy accumulation and return. The non-uniformity of energy accumulation and return between sections is shown. The equivalent electrical circuit of both single and serially connected battery sections is given. On the basis of equivalent electrical circuits, computer simulation of charge-discharge processes was carried out in the LT SPICE application program package.

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Section: Computer Simulation Of Chargedischarge Processes In the Stbmentioning

confidence: 99%

Computer modeling of charge-discharge processes in series-connected sections in storage batteries

Kogut,

Demyanchuk,

Prokopiv

2023

Phys. Chem. Sol. State

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“…The battery state of charge (SOC) represents the fundamental challenge of battery management systems (BMSs), which represents the residual capacity of the battery. However, this state is not directly measured [18]. For this reason, several methods to estimate SOC have been proposed in the literature [18,19].…”

Section: Battery Storage Systemmentioning

confidence: 99%

“…However, this state is not directly measured [18]. For this reason, several methods to estimate SOC have been proposed in the literature [18,19]. In this work, we used an ANN model to estimate the battery state of charge, due to its effectiveness and robustness compared to other methods.…”

Section: Battery Storage Systemmentioning

confidence: 99%

Real-Time Energy Management for DC Microgrids Using Artificial Intelligence

et al. 2021

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Microgrids are defined as an interconnection of several renewable energy sources in order to provide the load power demand at any time. Due to the intermittence of renewable energy sources, storage systems are necessary, and they are generally used as a backup system. Indeed, to manage the power flows along the entire microgrid, an energy management strategy (EMS) is necessary. This paper describes a microgrid energy management system, which is composed of solar panels and wind turbines as renewable sources, Li-ion batteries, electrical grids as backup sources, and AC/DC loads. The proposed EMS is based on the maximum extraction of energy from the renewable sources, by making them operate under Maximum Power Point Tracking (MPPT) mode; both of those MPPT algorithms are implemented with a multi-agent system (MAS). In addition, management of the stored energy is performed through the optimal control of battery charging and discharging using artificial neural network controllers (ANNCs). The main objective of this system is to maintain the power balance in the microgrid and to provide a configurable and a flexible control for the different scenarios of all kinds of variations. All the system’s components were modeled in MATLAB/Simulink, the MAS system was developed using Java Agent Development Framework (JADE), and Multi-Agent Control using Simulink with Jade extension (MACSIMJX) was used to insure the communication between Simulink and JADE.

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“…Boujoudar et al [11]. Conducted a battery test on Lithium-Ion batteries to store the data in a learning database from the driving cycles simulation and collected through a profile of dynamical stress testing (DST) and it will also be used for validation.…”

Section: Related Workmentioning

confidence: 99%

Memristive Equivalent Circuit Model for Battery

Mogadem

2021

Sustainability

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The design of mathematical models is based on conservation laws and also on the fundamental principles of modeling: structure, parameters, and physical meaning. Those kinds of modeling should have specific capabilities to deal with different working conditions and environments coping with challenges that include but are not limited to battery capacity, life-cycle, or the attempts to manipulate the current profiles during operation. Introducing memristive elements in batteries will be ideal to satisfy these fundamentals and goals of modeling, whereas addressing the recycling and sustainability concerns on the environmental impact by the placement of TiO2 memristor into this model can promote a recovery hierarchy via recycling and dispatching a slight amount to disposal as the previous focus was mainly concentrated on availability. As for battery materials, modeling, performing, and manufacturing all have proliferated to grasp the possible sustainability challenges inherited in these systems. This paper investigated electrochemical impedance spectroscopy to study this model and the dynamic behavior inside the battery. We found a solution to address the existing battery limitations that elucidate the battery degradation without affecting the performance, correspondingly by employing the dampest least-squares combination with nonlinear autoregressive exogenous for identifying such model and its associated parameters because of its embedded memory and fast convergence to diminish the influence of the vanishing gradient. Lastly, we found that this model is proven to be efficient and accurate compared to actual experimented data to validate our theory and show the value of the proposed model in real life while assuming Normal Gaussian distribution of data error with outstanding results; the auto-correlations were within the 95% confidence limit, the best validation was 2.7877, and an overall regression of 0.99993 was achieved.

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Lithium-Ion batteries modeling using NARX Nonlinear model

Cited by 6 publications

References 40 publications

Computer modeling of charge-discharge processes in series-connected sections in storage batteries

Computer modeling of charge-discharge processes in series-connected sections in storage batteries

Real-Time Energy Management for DC Microgrids Using Artificial Intelligence

Memristive Equivalent Circuit Model for Battery

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