State-of-Charge Estimation of Li-ion Battery Cell using Support Vector Regression and Gradient Boosting Techniques

Ipek, Eymen; Eren, Meryem; Yılmaz, Murat

doi:10.1109/acemp-optim44294.2019.9007188

Cited by 20 publications

(7 citation statements)

References 12 publications

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“…Another drawback of this method is that, the SOC-OCV curve is affected by many factors. Different rates of current applied for the discharge cycle when making SOC-OCV curve, causes the OCV-SOC curve shifts up or down depending on the applied current value (Ipek et al, 2019). This indicates that the SOC estimation using the SOC-OCV curve will be more accurate when the battery current value is close or equal to the current value on the SOC-OCV curve.…”

Section: Soc Estimation Resultsmentioning

confidence: 99%

Current Sensorless Microcontroller-Based Battery Management System with SOC and Active Cell Balancing

Ardiansyah

Cahyadi

Wahyunggoro

2021

ijqrm

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Battery management system (BMS) has become an important research topic following the trend and development of the electric vehicle. Although research on Active Cell Balancing, SOC, and current estimation has been carried out, the previous work mostly focused on comparing and developing methods. In this research, we demonstrate the process of designing BMS hardware using a low-cost microcontroller and without using a current sensor. The SOC simulation results produce an RMSE of 0.0832% for the 100% -10% SOC-OCV curve, and the current estimation simulation produces an RMSE of 0.2576 A, while for testing using a 6-ohm pulse load, the RMSE error value is 0.3960 A. The Active Cell Balancing method was successfully performed in simulation with Simulink. Furthermore, our simulation and test results suggest that complex battery models and multiple SOC-OCV curves can be used for better current and OCV estimation results. Our experimental results are also useful to develop a guideline to design a microcontroller-based BMS.

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Section: Soc Estimation Resultsmentioning

confidence: 99%

Current Sensorless Microcontroller-Based Battery Management System with SOC and Active Cell Balancing

Ardiansyah

Cahyadi

Wahyunggoro

2021

ijqrm

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“…However, achieving this precision comes at the cost of high computational complexity. For instance, other researchers [125] compared support vector regression (SVR) and the recent gradient boost algorithm extreme gradient boosting (XGBoost) for SOC estimation. They pointed out that to obtain precise predictions from SVR, the appropriate kernel function must be configured with workable parameter settings.…”

Section: Other Nn Variantsmentioning

confidence: 99%

Review of Various Machine Learning Approaches for Predicting Parameters of Lithium-Ion Batteries in Electric Vehicles

Shan,

Chin,

Mohan

et al. 2024

Batteries

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Battery management systems (BMSs) play a critical role in electric vehicles (EVs), relying heavily on two essential factors: the state of charge (SOC) and state of health (SOH). However, accurately estimating the SOC and SOH in lithium-ion (Li-ion) batteries remains a challenge. To address this, many researchers have turned to machine learning (ML) techniques. This study provides a comprehensive overview of both BMSs and ML, reviewing the latest research on popular ML methods for estimating the SOC and SOH. Additionally, it highlights the challenges involved. Beyond traditional models like equivalent circuit models (ECMs) and electrochemical battery models, this review emphasizes the prevalence of a support vector machine (SVM), fuzzy logic (FL), k-nearest neighbors (KNN) algorithm, genetic algorithm (GA), and transfer learning in SOC and SOH estimation.

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“…Chemali et al (2018), have investigated SoC estimation by using artificial neural networks (ANN) to develop BMSs used in Li-Ion batteries. Ipek et al (2019), have studied SoC estimation for Li-FePO4 batteries by using support vector machine (SVM) and DT-based eXtreme Gradient Boosting (XGBoost). Wang et al (2019), have used support vector regression (SVR) optimized by artificial bee colony (ABC) to determine the lifetime of Li-Ion batteries.…”

Section: Introductionmentioning

confidence: 99%

Syllogism of Li-FePO4 Battery Cell Voltage Parameter Guess Under Aperiodic Dynamic Current Profile by Some Data-Driven Techniques: A Error-Based Statistical Comparison Between Decision Tree, Support Vector, Bee Colony, and Neural Network

Çarkıt

ÇARKIT

2023

Black Sea Journal of Engineering and Science

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The various procedures are used in the literature for defining battery parameter change such as direct measurement methods, model-based methods, and data-driven methods, which contain the algorithms used in this paper also. The main aim of this study is to present a powerful and highly correct way of parameter forecasting of the A123 Systems 26650 cylindrical type Li-FePO4 battery cell. A few of the goal of this paper is to show the guessing performance of the artificial bee colony algorithm, which has a very limited number of applications on the battery parameter of literature, under the non-periodic dynamic charge/ discharge current profile. And then, a comparison has been made between artificial bee colony algorithm, artificial neural networks, support vector machine, and decision tree algorithm algorithms used in the paper. The load-connected terminal voltage is defined by considering the 100%-60% state of charge range in the primary usage areas of the batteries. A statistical comparison has been made by considering the maximum-minimum and average values of the real absolute errors, the maximum-minimum and average values of the squared errors, and R2 information regarding the results presented by the methods. Consequently, the regression values of the bee colony, neural network, support vector, and decision tree methods have been determined as 99.92%, 99.75%, 96.00% and 95.79%, respectively. Moreover, the mean value of the sum of squared errors of the methods has been calculated as 0.00202%, 0.00648%, 0.00998%, and 0.11%, respectively. As a new generation algorithm, artificial bee colony, which gave the most successful results according to the results obtained in the study, has been compared with two different methods selected from the existing literature, eXtreme Gradient Boosting and Smoothed eXtreme Gradient Boosting.

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State-of-Charge Estimation of Li-ion Battery Cell using Support Vector Regression and Gradient Boosting Techniques

Cited by 20 publications

References 12 publications

Current Sensorless Microcontroller-Based Battery Management System with SOC and Active Cell Balancing

Current Sensorless Microcontroller-Based Battery Management System with SOC and Active Cell Balancing

Review of Various Machine Learning Approaches for Predicting Parameters of Lithium-Ion Batteries in Electric Vehicles

Syllogism of Li-FePO4 Battery Cell Voltage Parameter Guess Under Aperiodic Dynamic Current Profile by Some Data-Driven Techniques: A Error-Based Statistical Comparison Between Decision Tree, Support Vector, Bee Colony, and Neural Network

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