Comparison of State of Charge Estimation Algorithms for Lithium Battery

Elmarghichi, Mouncef; Bouzi, Mostafa; Ettalabi, Naoufal; Derri, Mounir

doi:10.1007/978-981-15-6259-4_30

Cited by 9 publications

(8 citation statements)

References 7 publications

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“…SPKF effectively integrates current and voltage data to minimize capacity estimation errors and achieve accurate SOC estimation. Other approaches for SOC prediction can also be explored (refer to [39,40] for additional information). The result of this stage is the generation of the y and x vectors.…”

Section: Proposed Algorithmmentioning

confidence: 99%

Enhancing Battery Capacity Estimation Accuracy through the Neural Network Algorithm

El Marghichi,

Hilali,

Loulijat

2024

Period. Polytech. Elec. Eng. Comp. Sci.

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Accurate estimation of battery metrics, such as state of health (SOH), is crucial for effective battery management systems (BMS) due to capacity degradation over time. This paper proposes a methodology to enhance battery capacity estimation accuracy by addressing uncertainties related to state of charge (SOC) estimation and measurement. The methodology employs the Neural Network Algorithm (NNA), an optimization algorithm inspired by artificial neural networks (ANNs). The NNA generates an initial population of pattern solutions and iteratively updates them using a weight matrix, bias operator, and transfer function operator. By combining the advantages of ANNs and optimization techniques, the NNA aims to find an optimal solution considering interdependent variables and incorporating global and local feedbacks. Leveraging the capabilities of the NNA, our objective is to identify the candidate that minimizes a specified cost function, ensuring up-to-date cell capacity through a memory forgetting factor. The algorithm's precision was validated using NASA's Prognostic Data, demonstrating outstanding performance by surpassing two aggressive algorithms in terms of accuracy. In the most severe case scenario, the algorithm achieved a peak error of less than 0.4%. Furthermore, the algorithm consistently demonstrated predictive performance measures that were superior to those of the compared algorithms.

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Section: Proposed Algorithmmentioning

confidence: 99%

Enhancing Battery Capacity Estimation Accuracy through the Neural Network Algorithm

El Marghichi,

Hilali,

Loulijat

2024

Period. Polytech. Elec. Eng. Comp. Sci.

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“…Among the project's outcomes is the creation of new training materials of higher quality that focus on the most recent developments in vehicle technology. In terms of technology, the project exposes engineering students to contemporary EV components, such as traction machines, power converters [56], sensors, control units, super capacities, high voltage batteries [57], and communication networks [18]. Besides, the project focuses on the design tools for new control strategies, diagnostic and prognostic approaches, and energy management strategies.…”

Section: Motivation Objectives and General Approachmentioning

confidence: 99%

Design of a Customizable Test Bench of an Electric Vehicle Powertrain for Learning Purposes Using Model-Based System Engineering

et al. 2022

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Nowadays, electric vehicles attract significant attention because of the increasingly stringent exhaust emission policies all over the world. Moreover, with the fast expansion of the sustainable economy, the demand for electric vehicles is expanding. In the recent age, maintenance has seriously hampered the marketing and use of electric automobiles. As a result, the technique for maintaining electric vehicles is regarded as vital since it directly affects the security and availability for the end user and the passengers. Another key aspect of electric mobility is the integration of artificial intelligence in control, diagnostics, and prognostics. Meanwhile, a lot of research efforts are still devoted to developing and innovating electric traction systems, especially for diagnostic and prognostic purposes. Furthermore, topics covering important, current, and sustainability challenges should contain more than theoretical knowledge in high-quality education, particularly in engineering education. The purpose is to bridge the gap between the new technology and the learner’s circumstances through giving practical technical expertise and training in the sphere of overall engineering competences, to avoid non-standard, unskilled maintenance work. This article presents the first phase towards designing and developing a test bench of an electric vehicle’s powertrain used for research, learning and e-learning purposes, employing model-based systems engineering (MBSE) and systems modeling language (SysML) through the CESAM architecting and modeling framework. The aforementioned approach is used on our case study to build and present an operational viewpoint layout of the control, energy management, diagnostic, and prognostic test bench as part of the system’s initial phase of designing the system; the test bench layout proposed in this paper represents a flexible, low-cost, multidisciplinary downsized laboratory providing basic experiments related to e-mobility and covering numerous branches and study fields.

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“…8 Nowadays, the mainstream review papers focus on battery state of charge (SOC) estimation or independent analysis and evaluation of the balanced circuit. 9,10 Even though these review papers give certain suggestions for related researches, they can hardly provide comprehensive development suggestions because those determinants are analyzed independently. Besides, there is a good attempt such as Reference 11, in which a comprehensive discussion on the recycling process is provided, whereas the research gap on the systematic analysis of battery equalization still exists.…”

Section: Introductionmentioning

confidence: 99%

Series‐connected battery equalization system: A systematic review on variables, topologies, and modular methods

2021

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Series-connected lithium battery packs are widely adopted in industries such as electrical vehicles and large-scale energy storage systems. It is necessary to configure an equalization system for them to reduce the inconsistency of single cells, to ensure the battery pack cycle capacity. Although many novel active converters have been proposed for equalization, there still lacks systematic analyses on variables, topologies, and modular methods. Therefore, this paper provides a systematic review on the above aspects. Initially, an overall summary is present on current variables for equalization control. Second, the dominant structures are divided into cell-to-cell, cell-to-pack, and pack-to-pack topologies, and their characteristics are pointed out for comparison. And then, the modular methods, including single-layer-based, multi-layer-based, and hybrid integrations are evaluated via graph theory. Some key issues and potential research directions are given, which are expected to offer assistance on the selection of appropriate variables and topologies for equalization systems in the future.

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Comparison of State of Charge Estimation Algorithms for Lithium Battery

Cited by 9 publications

References 7 publications

Enhancing Battery Capacity Estimation Accuracy through the Neural Network Algorithm

Enhancing Battery Capacity Estimation Accuracy through the Neural Network Algorithm

Design of a Customizable Test Bench of an Electric Vehicle Powertrain for Learning Purposes Using Model-Based System Engineering

Series‐connected battery equalization system: A systematic review on variables, topologies, and modular methods

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