Lithium battery state-of-health estimation and remaining useful lifetime prediction based on non-parametric aging model and particle filter algorithm

2022 ◽  
pp. 100156
Author(s):  
Xiaoyu Li ◽  
Changgui Yuan ◽  
Zhenpo Wang ◽  
Jiangtao He ◽  
Shike Yu
Keyword(s):  
Particle Filter ◽  
Lithium Battery ◽  
Lifetime Prediction ◽  
State Of Health ◽  
Aging Model ◽  
Particle Filter Algorithm ◽  
Useful Lifetime ◽  
Non Parametric

scholarly journals A State of Health Estimation Method for Lithium-Ion Batteries Based on Improved Particle Filter Considering Capacity Regeneration

Energies ◽  
2021 ◽  
Vol 14 (16) ◽  
pp. 5000
Author(s):  
Haipeng Pan ◽  
Chengte Chen ◽  
Minming Gu
Keyword(s):  
Particle Filter ◽  
Lithium Ion Batteries ◽  
Estimation Method ◽  
Lithium Ion ◽  
Degradation Process ◽  
State Of Health ◽  
Calendar Time ◽  
Regeneration Stage ◽  
Multi Stage ◽  
Particle Filter Algorithm

Accurately estimating the state of health (SOH) of a lithium-ion battery is significant for electronic devices. To solve the nonlinear degradation problem of lithium-ion batteries (LIB) caused by capacity regeneration, this paper proposes a new LIB degradation model and improved particle filter algorithm for LIB SOH estimation. Firstly, the degradation process of LIB is divided into the normal degradation stage and the capacity regeneration stage. A multi-stage prediction model (MPM) based on the calendar time of the LIB is proposed. Furthermore, the genetic algorithm is embedded into the standard particle filter to increase the diversity of particles and improve prediction accuracy. Finally, the method is verified with the LIB dataset provided by the NASA Ames Prognostics Center of Excellence. The experimental results show that the method proposed in this paper can effectively improve the accuracy of capacity prediction.

State-of-health estimation for lithium battery in electric vehicles based on improved unscented particle filter

2019 ◽  
Vol 11 (2) ◽  
pp. 024101 ◽  
Author(s):  
Enwei Shi ◽  
Fei Xia ◽  
Daogang Peng ◽  
Liang Li ◽  
Xiaokang Wang ◽  
...  
Keyword(s):  
Particle Filter ◽  
Electric Vehicles ◽  
Lithium Battery ◽  
State Of Health ◽  
Unscented Particle Filter

scholarly journals Unscented Particle Filter Algorithm Based on Divide-and-Conquer Sampling for Target Tracking

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 2236
Author(s):  
Sichun Du ◽  
Qing Deng
Keyword(s):  
Particle Filter ◽  
State Space ◽  
Adaptive Filtering ◽  
Divide And Conquer ◽  
Particle Swarm Algorithm ◽  
Tracking Accuracy ◽  
Filtering Algorithm ◽  
Maneuvering Target ◽  
Unscented Particle Filter ◽  
Particle Filter Algorithm

Unscented particle filter (UPF) struggles to completely cover the target state space when handling the maneuvering target tracing problem, and the tracking performance can be affected by the low sample diversity and algorithm redundancy. In order to solve this problem, the method of divide-and-conquer sampling is applied to the UPF tracking algorithm. By decomposing the state space, the descending dimension processing of the target maneuver is realized. When dealing with the maneuvering target, particles are sampled separately in each subspace, which directly prevents particles from degeneracy. Experiments and a comparative analysis were carried out to comprehensively analyze the performance of the divide-and-conquer sampling unscented particle filter (DCS-UPF). The simulation result demonstrates that the proposed algorithm can improve the diversity of particles and obtain higher tracking accuracy in less time than the particle swarm algorithm and intelligent adaptive filtering algorithm. This algorithm can be used in complex maneuvering conditions.

Lithium battery aging model based on Dakin’s degradation approach

2016 ◽  
Vol 325 ◽  
pp. 273-285 ◽  
Author(s):  
Issam Baghdadi ◽  
Olivier Briat ◽  
Jean-Yves Delétage ◽  
Philippe Gyan ◽  
Jean-Michel Vinassa
Keyword(s):  
Lithium Battery ◽  
Aging Model ◽  
Model Based

A quick evaluation method for the lifetime of the fuel cell MEA with the particle filter algorithm

2021 ◽  
pp. 1-14
Author(s):  
Luyan He ◽  
Zhigang Zhan ◽  
Hong Chen ◽  
Panxing Jiang ◽  
Yuan Yu ◽  
...  
Keyword(s):  
Fuel Cell ◽  
Particle Filter ◽  
Evaluation Method ◽  
Particle Filter Algorithm
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