Neural network adaptive modeling of battery discharge behavior

Gerard, Olivier; Patillon, J.-N.; d’Alché‐Buc, Florence

doi:10.1007/bfb0020299

Cited by 18 publications

(6 citation statements)

References 3 publications

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“…Aylor [78] presented a coulomb counting method for LA batteries. Gerard [79] developed an artificial neural network method for portable equipment battery state accurate estimation in 1997. Garche et al [80] proposed a Kalman filters method to estimation SOC in 2000.…”

Section: State Of Charge Estimation Methodsmentioning

confidence: 99%

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

et al. 2018

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Sate of charge (SOC) accurate estimation is one of the most important functions in a battery management system for battery packs used in electrical vehicles. This paper focuses on battery SOC estimation and its issues and challenges by exploring different existing estimation methodologies. The key technologies of lithium-ion battery state estimation methodologies of the electrical vehicles categorized under five groups, such as the conventional method, adaptive filter algorithm, learning algorithm, nonlinear observer, and the hybrid method, are explored in an in-depth analysis. Lithium-ion battery characteristic, battery model, estimation algorithm, and cell unbalancing are the most important factors that affect the accuracy and robustness of SOC estimation. Finally, this paper concludes with the challenges of SOC estimation and suggests other directions for possible research efforts.

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Section: State Of Charge Estimation Methodsmentioning

confidence: 99%

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

et al. 2018

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“…It is concluded that EKF provides the best solution for the long-term SoC estimation [51]. In [38] an application is presented, in which the 'state variables' of the battery are replaced with neural weights, aiming at providing the user of portable equipment with an accurate estimation of the remaining working time, i.e., how much time is left until the battery voltage reaches the cut-off value.…”

Section: Adaptive Systemsmentioning

confidence: 99%

State-of-the-art of battery state-of-charge determination

Pop

Bergveld

Notten

et al. 2005

Meas. Sci. Technol.

252

140

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From the early days of its discovery, humanity has depended on electricity, a phenomenon without which our technological advancements would not have been possible. With the increased need for mobility, people moved to portable power storage—first for wheeled applications, then for portable and finally nowadays wearable use. Several types of rechargeable battery systems, including those of lead–acid, nickel–cadmium, nickel–metal hydride, lithium ion and lithium-ion polymer exist in the market. The most important of them will be discussed in this review. Almost as long as rechargeable batteries have existed, systems able to give an indication about the state-of-charge (SoC) of a battery have been around. Several methods, including those of direct measurements, book-keeping and adaptive systems (Bergveld et al 2002 Battery Management Systems, Design by Modelling (Philips Research Book Series) vol 1 (Boston: Kluwer)) are known in the art for determining the SoC of a cell or battery of cells. An accurate SoC determination method and an understandable and reliable SoC display to the user will improve the performance and reliability, and will ultimately lengthen the lifetime of the battery. However, many examples of poor accuracy and reliability can be found in practice (Bergveld et al 2002, cited above). This review presents an overview on battery technology and the state-of-the-art of SoC methods. The goal of all the presented SoC indication methods is to design an SoC indication system capable of providing an accurate SoC indication under all realistic user conditions, including those of spread—in both battery and user behaviour, a large temperature and current range and ageing of the battery.

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“…A simple and useful method is used by Aylor which was implemented to lead-acid battery [4] as is often known as Ah or coulomb counting. Then, neural network based methods are proposed to improve [5] estimation performance. Kalman filters (KF) usage is introduced by Garche et al in 2000 to optimize battery performance and lifetime by upgrading safety management as well [6].…”

Section: Introductionmentioning

confidence: 99%

A novel method for SOC estimation of Li-ion batteries using a hybrid machine learning technique

Ipek¹,

Yılmaz²

2021

Turk J Elec Eng & Comp Sci

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The battery system is one of the key components of electric vehicles (EV) which has brought groundbreaking technologies. Since modern EVs have mostly Li-ion batteries, they need to be monitored and controlled to achieve safe and high-performance operation. Particularly, the battery management system (BMS) uses complex processing systems that perform measurements, estimation of the battery states, and protection of the system. State of charge (SOC) estimation is a major part of these processes which defines remaining capacity in the battery until the next charging operation as a proportion to the total battery capacity. Since SOC is not a parameter that can be measured, the fundamental challenge is an accurate estimation. There are different SOC estimation methods in the literature that promises high accuracy such as model-based estimations, adaptive filter based estimations, and a combination of these systems. Recently, artificial intelligence (AI) and particularly machine learning (ML) based systems are included in the battery state estimation both as a part of adaptive systems and standalone. Data-driven methods are promising approaches to battery state estimation which provide high accuracy. The purpose of this study is to present a novel and highly accurate way of SOC estimation of the Li-ion battery (LIB) cell with a considerably low parameterization and modeling effort. Therefore, a new approach is proposed to estimate SOC with reduced modeling and without performing parametrization. Based on discharge test data, XGBoost is used to estimate SOC under dynamic operating conditions and the estimation is reached 98.81% coefficient of determination. As a novel approach, exponential smoothing is performed in combination with XGBoost SOC estimation to improve the estimation performance of the model. The estimation accuracy is improved as approximately 0.62%.

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Neural network adaptive modeling of battery discharge behavior

Cited by 18 publications

References 3 publications

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

State of the Art of Lithium-Ion Battery SOC Estimation for Electrical Vehicles

State-of-the-art of battery state-of-charge determination

A novel method for SOC estimation of Li-ion batteries using a hybrid machine learning technique

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