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An Improved State of Charge and State of Power Estimation Method Based on Genetic Particle Filter for Lithium-ion Batteries
Open AccessArticle

A New Lithium-Ion Battery SOH Estimation Method Based on an Indirect Enhanced Health Indicator and Support Vector Regression in PHMs

1
School of Mechanical Engineering, Hefei University of Technology, Hefei 230009, China
2
Key Laboratory of Industrial Safety and Emergency Technology of Anhui Province, Hefei 230009, China
*
Author to whom correspondence should be addressed.
Energies 2020, 13(4), 830; https://doi.org/10.3390/en13040830
Received: 2 January 2020 / Revised: 8 February 2020 / Accepted: 13 February 2020 / Published: 14 February 2020
(This article belongs to the Special Issue Testing and Management of Lithium-Ion Batteries)
An accurate lithium-ion battery state of health (SOH) estimate is a key factor in guaranteeing the reliability of electronic equipment. This paper proposes a new method that is based on an indirect enhanced health indicator (HI) and uses support vector regression (SVR) to estimate SOH values. First, three original features that can describe the dynamic changes of the battery charging and discharging processes are extracted. Considering the coupling relationship between pairs of the original health indicators, we use the differential evolution (DE) algorithm to optimize their corresponding feature parameters and combine them to form an enhanced health indicator. Second, this paper modifies the kernel function of the SVR model to describe the trend of SOH as the number of cycles increases, with simultaneous hyperparameters optimization via DE algorithm. Third, the proposed model and other published methods are compared in terms of accuracy on the same NASA datasets. We also evaluated the generalization performance of the model in dynamic discharging experiments. The simulation results demonstrate that the proposed method can provide more accurate SOH estimation values. View Full-Text
Keywords: lithium-ion battery; state of health; estimation; improved support vector regression; differential evolution lithium-ion battery; state of health; estimation; improved support vector regression; differential evolution
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MDPI and ACS Style

Liu, Z.; Zhao, J.; Wang, H.; Yang, C. A New Lithium-Ion Battery SOH Estimation Method Based on an Indirect Enhanced Health Indicator and Support Vector Regression in PHMs. Energies 2020, 13, 830.

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