Model improvement and SOC estimation based on aluminium ion batteries

Wang, Jia; Sun, Xiujuan; Lin, Meng; Wang, Chuanjiang; Du, Huiping; Chen, Pijian; Liu, Zhidong

doi:10.1088/2631-8695/abe7d2

Cited by 5 publications

(2 citation statements)

References 24 publications

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“…Various results are presented in this section under the following categories. Parameters and dynamics of hydrogen, fuel cell, electroluzer and battery are adopted from [38,39].…”

Section: Resultsmentioning

confidence: 99%

Design and control of LSTM-ANN controllers for an efficient energy management system in a smart grid based on hybrid renewable energy sources

Wesley,

Babu,

Kumar

2024

Eng. Res. Express

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Many isolated locations, including hilly areas, remote sites, and military camps, lack feasible access to the main power grid. In these conditions, locally established Microgrids can provide the necessary power supply. However, to meet the demands of these isolated areas, numerous individual Microgrids are required. Although some of these Microgrids might be integratable, geographical constraints may preclude full integration. Under these circumstances, the establishment of a smart grid – integrating multiple Microgrids with a battery management system – can potentially solve many power supply issues. A smart grid control system paired with a centralized battery management system is proposed in this paper. The system considers the use of multiple renewable energy sources at various locations for stable and reliable power generation. The proposed method incorporates a long short-term memory (LSTM)-based artificial neural network (ANN) to ensure a stable, high-quality power supply at different load buses. Additionally, this work introduces an artificial intelligence-based operating system designed to maintain energy management under various conditions. To enhance voltage quality, a 7-level aligned multilevel inverter is incorporated into the system. As compared with PI and Fuzzy controllers, the proposed method with LSTM-ANN controller is improved the power quality under sudden changes in the system which are also presented in results. The effectiveness of the proposed energy management system (EMS) is verified by using a hardware-in the-loop approach with OPAL-RT modules, yielding realistic results.

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“…Various results are presented in this section under the following categories. Parameters and dynamics of hydrogen, fuel cell, electroluzer and battery are adopted from [38,39].…”

Section: Resultsmentioning

confidence: 99%

Design and control of LSTM-ANN controllers for an efficient energy management system in a smart grid based on hybrid renewable energy sources

Wesley,

Babu,

Kumar

2024

Eng. Res. Express

View full text Add to dashboard Cite

show abstract

“…Various filter algorithms like EKF, UKF, and CKF were employed in the aforementioned studies, each with some algorithmic enhancements. However, Kalman filters exhibit limitations in certain aspects [21][22][23][24][25], particularly their sensitivity to inaccurate initial conditions and unknown perturbations, resulting in substantial estimation errors in the model.…”

Section: Introductionmentioning

confidence: 99%

Estimation of SOC in Lithium-Iron-Phosphate Batteries Using an Adaptive Sliding Mode Observer with Simplified Hysteresis Model during Electric Vehicle Duty Cycles

Chang,

Li,

Sun

et al. 2024

Batteries

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This paper develops a model for lithium-ion batteries under dynamic stress testing (DST) and federal urban driving schedule (FUDS) conditions that incorporates associated hysteresis characteristics of 18650-format lithium iron-phosphate batteries. Additionally, it introduces the adaptive sliding mode observer algorithm (ASMO) to achieve robust and swiftly accurate estimation of the state of charge (SOC) of lithium-iron-phosphate batteries during electric vehicle duty cycles. The established simplified hysteresis model in this paper significantly enhances the fitting accuracy during charging and discharging processes, compensating for voltage deviations induced by hysteresis characteristics. The SOC estimation, even in the face of model parameter changes under complex working conditions during electric vehicle duty cycles, maintains high robustness by capitalizing on the easy convergence and parameter insensitivity of ASMO. Lastly, experiments conducted under different temperatures and FUDS and DST conditions validate that the SOC estimation of lithium-iron-phosphate batteries, based on the adaptive sliding-mode observer and the simplified hysteresis model, exhibits enhanced robustness and faster convergence under complex working conditions and temperature variations during electric vehicle duty cycles.

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Reliability of electrode materials for supercapacitors and batteries in energy storage applications: a review

Ateş

Chebil

Yörük

et al. 2021

Ionics

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Model improvement and SOC estimation based on aluminium ion batteries

Cited by 5 publications

References 24 publications

Design and control of LSTM-ANN controllers for an efficient energy management system in a smart grid based on hybrid renewable energy sources

Design and control of LSTM-ANN controllers for an efficient energy management system in a smart grid based on hybrid renewable energy sources

Estimation of SOC in Lithium-Iron-Phosphate Batteries Using an Adaptive Sliding Mode Observer with Simplified Hysteresis Model during Electric Vehicle Duty Cycles

Reliability of electrode materials for supercapacitors and batteries in energy storage applications: a review

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