scholarly journals A Fast Online State of Health Estimation Method for Lithium-Ion Batteries Based on Incremental Capacity Analysis

Energies ◽  
2019 ◽  
Vol 12 (17) ◽  
pp. 3333 ◽  
Author(s):  
Shaofei Qu ◽  
Yongzhe Kang ◽  
Pingwei Gu ◽  
Chenghui Zhang ◽  
Bin Duan
Keyword(s):  
Lithium Ion Batteries ◽  
Estimation Error ◽  
Estimation Method ◽  
Lithium Ion ◽  
Computation Time ◽  
Absolute Error ◽  
Capacity Analysis ◽  
State Of Health ◽  
Important Challenge ◽  
Incremental Capacity

Efficient and accurate state of health (SoH) estimation is an important challenge for safe and efficient management of batteries. This paper proposes a fast and efficient online estimation method for lithium-ion batteries based on incremental capacity analysis (ICA), which can estimate SoH through the relationship between SoH and capacity differentiation over voltage (dQ/dV) at different states of charge (SoC). This method estimates SoH using arbitrary dQ/dV over a large range of charging processes, rather than just one or a limited number of incremental capacity peaks, and reduces the SoH estimation time greatly. Specifically, this method establishes a black box model based on fitting curves first, which has a smaller amount of calculation. Then, this paper analyzes the influence of different SoC ranges to obtain reasonable fitting curves. Additionally, the selection of a reasonable dV is taken into account to balance the efficiency and accuracy of the SoH estimation. Finally, experimental results validate the feasibility and accuracy of the method. The SoH estimation error is within 5% and the mean absolute error is 1.08%. The estimation time of this method is less than six minutes. Compared to traditional methods, this method is easier to obtain effective calculation samples and saves computation time.

scholarly journals Incremental Capacity Analysis as a State of Health Estimation Method for Lithium-Ion Battery Modules with Series-Connected Cells

Batteries ◽  
2020 ◽  
Vol 7 (1) ◽  
pp. 2
Author(s):  
Amelie Krupp ◽  
Ernst Ferg ◽  
Frank Schuldt ◽  
Karen Derendorf ◽  
Carsten Agert
Keyword(s):  
Lithium Iron Phosphate ◽  
Estimation Method ◽  
Lithium Ion ◽  
Iron Phosphate ◽  
Capacity Analysis ◽  
State Of Health ◽  
Incremental Capacity ◽  
Li Ion ◽  
Parallel Connections

Incremental capacity analysis (ICA) has proven to be an effective tool for determining the state of health (SOH) of Li-ion cells under laboratory conditions. This paper deals with an outstanding challenge of applying ICA in practice: the evaluation of battery series connections. The study uses experimental aging and characterization data of lithium iron phosphate (LFP) cells down to 53% SOH. The evaluability of battery series connections using ICA is confirmed by analytical and experimental considerations for cells of the same SOH. For cells of different SOH, a method for identifying non-uniform aging states on the modules’ IC curve is presented. The findings enable the classification of battery modules with series and parallel connections based on partial terminal data.

Screening of retired lithium-ion batteries using incremental capacity charging curve based residual capacity estimation method for facilitating sustainable circular lithium-ion battery system

2021 ◽  
pp. 1-16
Author(s):  
Honglei Li ◽  
Liang Cong ◽  
Huazheng Ma ◽  
Weiwei Liu ◽  
Yelin Deng ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Estimation Method ◽  
Lithium Ion ◽  
Capacity Fade ◽  
Characteristic Parameters ◽  
Capacity Estimation ◽  
Capacity Curve ◽  
Residual Capacity ◽  
Remaining Capacity ◽  
Incremental Capacity

Abstract The rapidly growing deployment of lithium-ion batteries in electric vehicles is associated with a great waste of natural resource and environmental pollution caused by manufacturing and disposal. Repurposing the retired lithium-ion batteries can extend their useful life, creating environmental and economic benefits. However, the residual capacity of retired lithium-ion batteries is unknown and can be drastically different owing to various working history and calendar life. The main objective of this paper is to develop a fast and accurate capacity estimation method to classify the retired batteries by the remaining capacity. The hybrid technique of adaptive genetic algorithm and back propagation neural network is developed to estimate battery remaining capacity using the training set comprised of the selected characteristic parameters of incremental capacity curve of battery charging. Also, the paper investigated the correlation between characteristic parameters with capacity fade. The results show that capacity estimation errors of the proposed neural network are within 3%. Peak intensity of the incremental capacity curve has strong correlation with capacity fade. The findings also show that the translation of peak of the incremental capacity curve is strongly related with internal resistance.

scholarly journals Ultrasound simulation technique as state-of-health estimation method of lithium-ion batteries

2021 ◽  
Author(s):  
J.P. Gaviria-Cardona ◽  
Michael Guzman-De Las Salas ◽  
Nicolas Montoya-Escobar ◽  
Whady Florez-Escobar ◽  
Raul Valencia-Cardona ◽  
...  
Keyword(s):  
Lithium Ion Batteries ◽  
Estimation Method ◽  
Lithium Ion ◽  
Simulation Technique ◽  
State Of Health ◽  
Ultrasound Simulation
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