A study of external surface pressure effects on the properties for lithium‐ion pouch cells

Summary State of health (SOH) prediction is always a research hotspot in the field of lithium‐ion batteries (LIBs). Machine learning (ML) methods have received widespread attention for their high prediction accuracy. However, the existing studies only focus on extracting features from simple constant current charge‐discharge curves or using features that require pre‐processing, while the actual discharge current is random and can affect battery aging. Besides, the online sequential extreme learning machine (OSELM) currently used in ML lacks a more efficient online learning and update mechanism in terms of prediction. Therefore, this paper firstly extracts effective features from the random discharge data and conducts a mechanism analysis to verify its rationality. Then, we propose a drift detection based on the Bernstein inequality (BI‐DD) algorithm and use it to guide the OSELM to save learning time. The experimental results show the OSELM based on the BI‐DD can perform good learning for SOH prediction in a shorter time. The learning time can be reduced by up to 88.87% and the mean absolute error (MAE) does not exceed 1%, which is a promising SOH prediction method. Highlights Extract aging features from random discharge data and the rationality of the extracted features is analyzed according to the mechanism. A drift detection algorithm based on Bernstein inequality (BI‐DD) is proposed. An OSELM based on concept drift detection is proposed and for SOH online learning and prediction.

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

State of health prediction for lithium‐ion batteries with a novel online sequential extreme learning machine method

Tian

Qin

2020

“…Li-ion battery is extensively used in many fields, including consumer electronics and energy storage systems, because of its merits such as high energy/power density, long lifetime, and low self-discharge rate. 1,2 To guarantee the reliability of a battery system, real-time battery states are monitored and managed by battery management system (BMS). 3 In the states of battery, state of health (SoH) reflects the degradation degree and represents the energy store and supply capability of a Li-ion battery relative to its initial life.…”

Section: Introductionmentioning

confidence: 99%

“…Li‐ion battery is extensively used in many fields, including consumer electronics and energy storage systems, because of its merits such as high energy/power density, long lifetime, and low self‐discharge rate 1,2 . To guarantee the reliability of a battery system, real‐time battery states are monitored and managed by battery management system (BMS) 3 .…”

Section: Introductionmentioning

confidence: 99%

Li‐ion battery state of health estimation through Gaussian process regression with Thevenin model

Lyu

Gao

2020

This paper proposes a model-based and data-driven joint method to estimate the state of health of Li-ion batteries. To accurately quantify battery degradation, a novel resistance-based aging feature is defined from the Thevenin model, and the defined aging feature is approximately linear with capacity degradation. An orthogonal experimental design and a two-way analysis of variance are used to validate the robustness of the defined aging feature. Considering the influence of temperature on battery performance, Box-Cox transformation is introduced to improve the aging feature linearity at low temperatures. Then, an estimator for state of health is established by using Gaussian process regression. Battery aging experiments are conducted to illustrate the estimation effect of the proposed method. The experimental results show that the proposed method has high estimation accuracy at different temperatures. Using the same aging feature, the backpropagation network and support vector regression are implemented to verify the generality of the estimation framework.

“…Excessive diffusion stress can lead to various failure modes, such as cracking, breaking, and pulverization of particles, separation between particles, cracking and breaking of active layers, delamination between active layers and current collectors, etc 6 . These failure modes will result in a variety of failure phenomena of the battery, such as decay of capacity, increase of internal resistance, degradation of rate performance, and decline of cycle life 7,8 . So the diffusion stress can significantly affect the performance and life of an LIB.…”

Section: Introductionmentioning

confidence: 99%

Stress and its influencing factors in positive particles of lithium‐ion battery during charging

Wang

et al. 2020

Summary In this paper, the stress in positive particles of a Li‐ion battery during charging is obtained. The effects of the charging rates, charging modes, and structural parameters of the positive electrode on the stress are investigated. A mesoscopic electrochemical–mechanical coupling model for Li‐ion battery is built and verified. The SOC, strain, and stress distributions in positive particles during the constant current (CC)–constant voltage (CV) charging process are calculated by the model. The results show that the stress in positive particles quickly increases at the CC charging stage, especially when the state of charge (SOC) of the battery exceeds 80%. Then it slowly increases at the CV charging stage. Under the CC–CV charging mode, the charging rate has little effect on the stress in positive particles at the end of charging. But the distance between particles, the particle radius, and the electrode thickness can affect the stress at the end of charging. The conclusions obtained could provide references for the design and optimization of the charging strategy and mesoscopic microstructure of LIBs.