A novel entropy-based fault diagnosis and inconsistency evaluation approach for lithium-ion battery energy storage systems

Qiu, Yishu; Cao, Wenbin; Peng, Peng; Jiang, Fangming

doi:10.1016/j.est.2021.102852

Cited by 31 publications

(8 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Unlike modelbased methods, data-driven battery FDD methods do not rely on an accurate battery model, while FDD is performed by real-time data such as voltage, current, and temperature generated during battery operation. Commonly employed data-driven diagnostic techniques encompass entropy analysis (EA) [28], statistical analysis (SA) [29], and machine learning (ML) [30]. Xia et al [31] introduced a fault detection method for lithium batteries based on the voltage profile correlation coefficient.…”

Section: Introductionmentioning

confidence: 99%

Early-Stage ISC Fault Detection for Ship Lithium Batteries Based on Voltage Variance Analysis

Gu,

Ni,

2024

Machines

View full text Add to dashboard Cite

With the progressive development of new energy technologies, high-power lithium batteries have been widely used in ship power systems due to their high-power density and low environmental pollution, and they have gradually become one of their main propulsion energy sources. However, the large-scale deployment of lithium batteries has also brought a series of safety problems to ship operations, especially the battery internal short circuit (ISC). Battery ISC faults are very hidden and unpredictable at the initial stage and often fail to be detected in time, ultimately leading to overheating, fire or even an explosion of the ship’s power system. Based on this, this paper proposes a fast and accurate method for early-stage ISC fault location and detection of lithium batteries. Initially, voltage variations across the lithium battery packs are quantified using curvilinear Manhattan distances to pinpoint faulty battery units. Subsequently, the localized characteristics of voltage variance among adjacent batteries are leveraged to detect an early-stage ISC fault. Simulation results indicate that the proposed method can quickly and accurately locate the position of 5 Ω, 10 Ω and 15 Ω ISC faulty batteries within the battery pack, as well as detect the abnormal batteries in a timely manner with considerable sensitivity and reliability.

show abstract

Section: Introductionmentioning

confidence: 99%

Early-Stage ISC Fault Detection for Ship Lithium Batteries Based on Voltage Variance Analysis

Gu,

Ni,

2024

Machines

View full text Add to dashboard Cite

show abstract

“…Through the online monitoring of the real‐time voltage fluctuation data of the vehicle, the abnormal cell was identified under the analysis method based on Shannon entropy. Qiu et al 32 used the difference between the theoretical maximum entropy of the random variable X and the actual Shannon entropy to measure the inconsistency, but the rationality of threshold selection needs to be considered when using this method for inconsistent fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

A method for battery fault diagnosis and early warning combining isolated forest algorithm and sliding window

Cheng,

Li,

2023

Energy Science & Engineering

View full text Add to dashboard Cite

The vehicle's power battery is composed of a large number of battery cells series or in parallel. Due to the manufacturing process error and the different use environments, there are differences between the battery cells, and the battery pack will have inconsistency problems, which will increase the safety hazard. Therefore, it is of great practical significance to identify and warn about the inconsistency of power batteries. Based on the data of the internet of vehicles platform, this paper proposes an improved isolated forest power battery abnormal monomer identification and early warning method, which uses the sliding window (SW) to segment the dataset and update the data of the diagnosis model in real‐time. The scores of normal battery cells and abnormal battery cells were analyzed, and then the fault threshold was determined to be 0.75. The results show that the recall ratio and precision ratio of the algorithm are 0.91 and 0.95, respectively, which is more suitable for inconsistent battery cell identification than other methods. If the SW size is 15, the warning effect is the best. Before the vehicle alarm occurs, the algorithm can realize early fault warnings, thus effectively avoiding the safety problems caused by inconsistency faults.

show abstract

“…This results in CV charging duration sequences and CV charging duration variation sequences. Shannon entropy processing [48] is applied to both sequences, resulting in a feature combination composed of CV charging durations, the Shannon entropy of the CV charging duration sequence, and the Shannon entropy of the CV charging duration variation sequence. The Pearson correlation coefficient was wielded to validate the correlation of the three extracted features with the battery's SOH.…”

Section: Introductionmentioning

confidence: 99%

State-of-Health Estimation of Lithium-Ion Battery Based on Constant Voltage Charging Duration

Chen,

Han

et al. 2023

Batteries

View full text Add to dashboard Cite

It is imperative to determine the State of Health (SOH) of lithium-ion batteries precisely to guarantee the secure functioning of energy storage systems including those in electric vehicles. Nevertheless, predicting the SOH of lithium-ion batteries by analyzing full charge–discharge patterns in everyday situations can be a daunting task. Moreover, to conduct this by analyzing relaxation phase traits necessitates a more extended idle waiting period. In order to confront these challenges, this study offers a SOH prediction method based on the features observed during the constant voltage charging stage, delving into the rich information about battery health contained in the duration of constant voltage charging. Innovatively, this study suggests using statistics of the time of constant voltage (CV) charging as health features for the SOH estimation model. Specifically, new features, including the duration of constant voltage charging, the Shannon entropy of the time of the CV charging sequence, and the Shannon entropy of the duration increment sequence, are extracted from the CV charging phase data. A battery’s State-of-Health estimation is then performed via an elastic net regression model. The experimentally derived results validate the efficacy of the approach as it attains an average mean absolute error (MAE) of only 0.64%, a maximum root mean square error (RMSE) of 0.81%, and an average coefficient of determination (R2) of 0.98. The above statement serves as proof that the suggested technique presents a substantial level of precision and feasibility for the estimation of SOH.

show abstract

A novel entropy-based fault diagnosis and inconsistency evaluation approach for lithium-ion battery energy storage systems

Cited by 31 publications

References 48 publications

Early-Stage ISC Fault Detection for Ship Lithium Batteries Based on Voltage Variance Analysis

Early-Stage ISC Fault Detection for Ship Lithium Batteries Based on Voltage Variance Analysis

A method for battery fault diagnosis and early warning combining isolated forest algorithm and sliding window

State-of-Health Estimation of Lithium-Ion Battery Based on Constant Voltage Charging Duration

Contact Info

Product

Resources

About