An SOC and SOP Joint Estimation Method of Lithium-ion Batteries in Unmanned Aerial Vehicles

Author(s):  
Wanqing Cheng ◽  
Zhiheng Yi ◽  
Jun Liang ◽  
Yuchen Song ◽  
Datong Liu
2020 ◽  
Vol 1 (1) ◽  
pp. 107-120
Author(s):  
Peiqing Li ◽  
Huile Wang ◽  
Zixiao Xing ◽  
Kanglong Ye ◽  
Qipeng Li

PurposeThe operation state of lithium-ion battery for vehicle is unknown and the remaining life is uncertain. In order to improve the performance of battery state prediction, in this paper, a joint estimation method of state of charge (SOC) and state of health (SOH) for lithium-ion batteries based on multi-scale theory is designed.Design/methodology/approachIn this paper, a joint estimation method of SOC and SOH for lithium-ion batteries based on multi-scale theory is designed. The venin equivalent circuit model and fast static calibration method are used to fit the relationship between open-circuit voltage and SOC, and the resistance and capacitance parameters in the model are identified based on exponential fitting method. A battery capacity model for SOH estimation is established. A multi-time scale EKF filtering algorithm is used to estimate the SOC and SOH of lithium-ion batteries.FindingsThe SOC and SOH changes in dynamic operation of lithium-ion batteries are accurately predicted so that batteries can be recycled more effectively in the whole vehicle process.Originality/valueA joint estimation method of SOC and SOH for lithium-ion batteries based on multi-scale theory is accurately predicted and can be recycled more effectively in the whole vehicle process.


2019 ◽  
Vol 255 ◽  
pp. 113817 ◽  
Author(s):  
Quanqing Yu ◽  
Rui Xiong ◽  
Ruixin Yang ◽  
Michael G. Pecht

2019 ◽  
Vol 1 (1) ◽  
pp. e48 ◽  
Author(s):  
Yanjun Ma ◽  
Sum Wai Chiang ◽  
Xiaodong Chu ◽  
Jia Li ◽  
Lin Gan ◽  
...  

Energies ◽  
2020 ◽  
Vol 13 (4) ◽  
pp. 915
Author(s):  
Dongxu Guo ◽  
Geng Yang ◽  
Guangjin Zhao ◽  
Mengchao Yi ◽  
Xuning Feng ◽  
...  

Electrochemical impedance spectroscopy (EIS) is a powerful tool for investigating electrochemical systems, such as lithium-ion batteries or fuel cells, given its high frequency resolution. The distribution of relaxation times (DRT) method offers a model-free approach for a deeper understanding of EIS data. However, in lithium-ion batteries, the differential capacity caused by diffusion processes is non-negligible and cannot be decomposed by the DRT method, which limits the applicability of the DRT method to lithium-ion batteries. In this study, a joint estimation method with Tikhonov regularization is proposed to estimate the differential capacity and the DRT simultaneously. Moreover, the equivalence of the differential capacity and the incremental capacity is proven. Different types of commercial lithium-ion batteries are tested to validate the joint estimation method and to verify the equivalence. The differential capacity is shown to be a promising approach to the evaluation of the state-of-health (SOH) of lithium-ion batteries based on its equivalence with the incremental capacity.


Author(s):  
Honglei Li ◽  
Liang Cong ◽  
Huazheng Ma ◽  
Weiwei Liu ◽  
Yelin Deng ◽  
...  

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.


Energies ◽  
2018 ◽  
Vol 11 (9) ◽  
pp. 2221 ◽  
Author(s):  
Myeong-hwan Hwang ◽  
Hyun-Rok Cha ◽  
Sung Yong Jung

The practically applicable endurance estimation method for multirotor unmanned aerial vehicles (UAVs) using a battery as a power source is proposed. The method considers both hovering and steady-level flights. The endurance, thrust, efficiency, and battery discharge are determined with generally available data from the manufacturer. The effects of the drag coefficient related to vehicle shape and payload weight are examined at various forward flight speeds. As the drag coefficient increases, the optimum speed at the minimum required power and the maximum endurance are reduced. However, the payload weight causes an opposite effect, and the optimal flying speed increases with an increase in the payload weight. For more practical applications for common users, the value of S × Cd is determined from a preliminary flight test. Given this value, the endurance is numerically estimated and validated with the measured flight time. The proposed method can successfully estimate the flight time with an average error of 2.3%. This method would be useful for designers who plan various missions and select UAVs.


Author(s):  
Soo-Young Park ◽  
Sang-Won Ryu ◽  
Jae-Bum Park ◽  
Byung-Ki Kim ◽  
Mi-Young Kim ◽  
...  

2021 ◽  
Author(s):  
J.P. Gaviria-Cardona ◽  
Michael Guzman-De Las Salas ◽  
Nicolas Montoya-Escobar ◽  
Whady Florez-Escobar ◽  
Raul Valencia-Cardona ◽  
...  

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