Battery management solutions for li-ion batteries based on artificial intelligence

Khawaja, Yara; Shankar, N. Gowri; Qiqieh, Issa; Alzubi, Jafar A.; Alzubi, Omar A.; Nallakaruppan, M.K.; Padmanaban, Sanjeevikumar

doi:10.1016/j.asej.2023.102213

Cited by 44 publications

(20 citation statements)

References 66 publications

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“…The third flock has the remaining vultures. Due to the directing effects of the best and second-best vultures, Equation (15) aims to choose the vulture the current iteration should advance towards and move towards the best solutions for the first and second groups. In each fitness iteration, the entire population is recalculated.…”

Section: African Vultures Optimizer Methodologymentioning

confidence: 99%

“…X t+1 i represents the location of the (i)th vulture at the (t + 1)th iteration, and the random numbers rand t p1 , rand t i2 , and rand t i3 are uniformly distributed in the range of [0, 1]. R t i is acquired by Equation (15), and F t is acquired by Equation (7). The problem's solution's upper and lower limits are represented by ub and lb, respectively, and D t i is calculated by Equation (20) to show the separation between the existing vulture and its ideal state.…”

Section: African Vultures Optimizer Methodologymentioning

confidence: 99%

“…One of the most significant difficulties of lithium-ion batteries is the highly nonlinear and complex electrochemical nature that makes an accurate parameter estimation a difficult task that may lead to inaccurate SOC estimation that causes overcharging and over-discharging that can damage the battery. Battery statuses, including state of charge (SOC), state of health (SOH), state of life (SOL), and state of power (SOP) [13], are tracked using the battery management system (BMS) to repair this harm [14,15]. The battery management system (BMS) is an electronic device with several features, including managing and monitoring the battery's critical status, fault diagnosis, thermal management, preventing the battery from running outside of its safe operating range, keeping an eye on its states (SOC, SOH, SOL, and SOP), computing and reporting the data, and controlling the environment [16].…”

Section: Introductionmentioning

confidence: 99%

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Parameter Identification of Lithium-Ion Battery Model Based on African Vultures Optimization Algorithm

et al. 2023

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This paper establishes a study for an accurate parameter modeling method for lithium-ion batteries. A precise state space model generated from an equivalent electric circuit is used to carry out the proposed identification process, where parameter identification is a nonlinear optimization process problem. The African vultures optimization algorithm (AVOA) is utilized to solve this problem by simulating African vultures’ foraging and navigating habits. The AVOA is used to implement this strategy and improve the quality of the solutions. Four scenarios are considered to take the effect of loading, fading, and dynamic analyses. The fitness function is selected as the integral square error between the estimated and measured voltage in these scenarios. Numerical simulations were executed on a 2600 mAhr Panasonic Li-ion battery to demonstrate the effectiveness of the suggested parameter identification technique. The proposed AVOA was fulfilled with high accuracy, the least error, and high closeness with the experimental data compared with different optimization algorithms, such as the Nelder–Mead simplex algorithm, the quasi-Newton algorithm, the Runge Kutta optimizer, the genetic algorithm, the grey wolf optimizer, and the gorilla troops optimizer. The proposed AVOA achieves the lowest fitness function level of the scenarios studied compared with relative optimization algorithms.

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Section: African Vultures Optimizer Methodologymentioning

confidence: 99%

Section: African Vultures Optimizer Methodologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Parameter Identification of Lithium-Ion Battery Model Based on African Vultures Optimization Algorithm

et al. 2023

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“…AI and ML are increasingly being used in the development of electrochemical storage systems. Some of the applications of AI and ML in its development include [113,114] (Fig. 12):…”

Section: Predictive Approch To Resolve Battery Problemsmentioning

confidence: 99%

Recent Advances in the Development of Li‐Air Batteries, Experimental and Predictive Approaches – Prospective, Challenges, and Opportunities

Jilani,

Awan,

Taqvi

et al. 2023

ChemBioEng Reviews

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The significance of lithium‐air batteries (LABs) has recently increased as they are a highly competitive alternative to lithium‐ion batteries (LIBs) for powering electric automobiles. The reason behind this is their excessive theoretical energy density. The advancement, breakthroughs, and drawbacks of LABs are examined. A comprehensive evaluation of various energy storage devices is provided, followed by an evaluation of several electrochemical storage technologies, their advantages, and drawbacks. Also, recent advances in LABs are reviewed and potential machine learning techniques are explored to address the existing obstacles hindering the widespread adoption of LABs.

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“…Nowadays, the electric vehicles still cannot surpass fuel vehicles in terms of endurance mileage, safety, and operating costs. Therefore, people have higher requirements for lithium-ion batteries, including higher energy density, better cycling performance, faster charging speed, excellent low-temperature performance and so on [1][2]. LNMO exhibits high working voltage (4.7 V) and low cost, which is considered a promising candidate for high-energy density LIBs.…”

Section: Introductionmentioning

confidence: 99%

Study on the (trimethoxysilyl) naphthalene electrolyte additive for enhancing electrochemical performance of LiNi0.5Mn1.5O4 cathode for Li-ion battery

Wei,

Mu,

et al. 2024

Ninth International Conference on Energy Materials and Electrical Engineering (ICEMEE 2023)

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The electrochemical performances of LiNi 0.5 Mn 1.5 O 4 (LNMO) have been improved by adding 0.5 wt.% (Trimethoxysilyl)naphthalene (TMSN) in carbonate electrolyte. The linear sweep voltammetry (LSV) test shows TMSN could preferentially undergo oxidative decomposition during charging process, which further inhibits the electrolyte decomposition. LNMO/Li half-cell presents better rate capabilities and an increased capacity retention of 89.3% after 200 cycles at 1 C by adding 0.5wt. % TMSN, indicating that TMSN can significantly reduce the HF content in the electrolytes and form a stable electrolyte/electrode interfacial on LNMO's surface.

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Battery management solutions for li-ion batteries based on artificial intelligence

Cited by 44 publications

References 66 publications

Parameter Identification of Lithium-Ion Battery Model Based on African Vultures Optimization Algorithm

Parameter Identification of Lithium-Ion Battery Model Based on African Vultures Optimization Algorithm

Recent Advances in the Development of Li‐Air Batteries, Experimental and Predictive Approaches – Prospective, Challenges, and Opportunities

Study on the (trimethoxysilyl) naphthalene electrolyte additive for enhancing electrochemical performance of LiNi0.5Mn1.5O4 cathode for Li-ion battery

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