Research Article
Comparative Study of Joint Estimation of State of Charge (SOC) and State of Health (SOH) of Lithium-ion Batteries Based on Different Tree Models
Issue:
Volume 12, Issue 2, April 2024
Pages:
23-35
Received:
29 November 2023
Accepted:
13 December 2023
Published:
29 April 2024
Abstract: The realization of accurate State of Health (SOH) and State of Charge (SOC) estimation is a prerequisite to ensure the safe use of energy storage batteries, which helps to further improve the energy utilization efficiency effectively. Data-driven methods are efficient, accurate, and do not rely on accurate battery models, which is a hot direction in battery state estimation research. However, the relationships between variables in the lithium-ion battery dataset are mostly nonlinear, which largely affects the prediction of the model. In addition, the model also has a series of defects, such as large computation, strong data dependence, and long consumption time. In this paper, a joint online estimation method of battery SOC-SOH based on tree modeling algorithm is proposed to solve the above problems. Based on NASA battery sample data, this study explores the changing law between SOC and discharge voltage and temperature under different State of Health (SOH). Subsequently, a combination of RFR, GBDT and XGBoost tree modeling algorithms are used for battery SOC-SOH estimation based on the above variation rules. The experimental results show that the R2 scores of the XGBoost algorithm in predicting both SOC and SOH are more than 0.995, indicating its good adaptability and feasibility.
Abstract: The realization of accurate State of Health (SOH) and State of Charge (SOC) estimation is a prerequisite to ensure the safe use of energy storage batteries, which helps to further improve the energy utilization efficiency effectively. Data-driven methods are efficient, accurate, and do not rely on accurate battery models, which is a hot direction i...
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Research Article
MST Initialization Based Intuitionistic Fuzzy c Means Clustering Using LINEX Hellinger Distance and Its Applications
Issue:
Volume 12, Issue 2, April 2024
Pages:
36-47
Received:
17 May 2024
Accepted:
5 June 2024
Published:
27 August 2024
Abstract: Due to the uncertainty and fuzziness of information, the traditional clustering analysis method sometimes cannot meet the requirement in practice. The clustering method based on intuitionistic fuzzy set has attracted more and more scholars attention nowadays. This paper discusses the intuitionistic fuzzy C-means clustering algorithm. There are a number of clustering techniques developed in the past using different distance/similarity measure. In this paper, we proposed a improved edge density minimal spanning tree initilization method using LINEX hellinger distance based weighted LINEX intuitionistic fuzzy c means clustering. IFCM considered an uncertainty parameter called hesitation degree and incorporated a new objective function which is based upon intutionistic fuzzy entropy in the conventional Fuzzy C-means. The clustering algorithm has membership and non membership degrees as intervals. Information regarding membership and typicality degrees of samples to all clusters is given by algorithm. Furthermore, the algorithm is extended for calculating membership and updating prototypes by minimizing the new objective function of weighted LINEX intuitionistic fuzzy c-means. Finally, the developed algorithms are illustrated through conducting experiments on random dataset, partition coefficient and validation function are used to evaluate the validity of clustering also this paper compares the results of proposed method with the results of existing basic intuitionistic fuzzy c-means.
Abstract: Due to the uncertainty and fuzziness of information, the traditional clustering analysis method sometimes cannot meet the requirement in practice. The clustering method based on intuitionistic fuzzy set has attracted more and more scholars attention nowadays. This paper discusses the intuitionistic fuzzy C-means clustering algorithm. There are a nu...
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,
Guiling Zhang
