An Improved Rainflow Algorithm Combined with Linear Criterion for the Accurate Li-ion Battery Residual Life Prediction

Huang, Junhan; Wang, Shunli; Xu, Wenhua; Vilas, Carlos; Fan, Yongcun; Chen, Xianpei

doi:10.20964/2021.07.29

Cited by 8 publications

(8 citation statements)

References 32 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The rain-flow counting method is considered to be an effective approach for mechanical fault diagnosis. 28 The battery does not discharge to a specific DOD level throughout operation but many various levels, so the rain-flow counting method is used to extract the cycle number from the SOC profile in this paper. This process consists of three stages: data reconstruction, extracting the cycle number, and calculating the DOD of each cycle.…”

Section: Battery Degradation Modelmentioning

confidence: 99%

See 1 more Smart Citation

Optimization of battery energy storage system size and power allocation strategy for fuel cell ship

Cao

Geng

2023

Energy Science & Engineering

View full text Add to dashboard Cite

The fuel cell system (FCS) is commonly combined with an energy storage system (ESS) for enhancing the performance of the ship. Consequently, the battery ESS size and power allocation strategy are critical for the hybrid energy system. This paper focuses on designing a method to solve these two problems. First, a battery degradation model is employed to assess the ESS lifetime. Subsequently, the sizing problem and the optimal power allocation are integrated into a cost‐minimization problem, which is solved by a double‐loop optimization approach. The inside loop utilizes the battery degradation model to calculate ESS lifetime. In the outside loop, a power allocation strategy based on the hybrid Particle Swarm Optimization algorithm and Gray Wolf Optimization algorithm is presented. Finally, the power allocation strategy is extended to real‐time implementation by the equivalent consumption minimization strategy (ECMS) and an improved ECMS is proposed to make the FCS operates near the maximal efficiency point. Compared with ECMS, the operating cost reduces by 0.26%. The result indicates that the proposed method can optimize the ESS size efficiently, and the power allocation strategy can assure the stable operation of the fuel cell ship.

show abstract

Section: Battery Degradation Modelmentioning

confidence: 99%

“…The operation cost consists of hydrogen consumption and ESS loss. The initial cost of the ESS is calculated by the Equation (28). In this paper, both ESSs have the same rated size and rated power.…”

Section: Power Allocation Strategymentioning

confidence: 99%

Optimization of battery energy storage system size and power allocation strategy for fuel cell ship

Cao

Geng

2023

Energy Science & Engineering

View full text Add to dashboard Cite

show abstract

“…The Pearson correlation rate is used to reflect the degree of correlation between the proposed features and components, and its calculation method is shown in Equation (6). σX and σY represent the SD of X and Y , respectively, and cov X, Y ð Þ represents the covariance of X and Y .…”

Section: Extract Featuresmentioning

confidence: 99%

“…[1][2][3][4][5] Lithium-ion batteries (LIBs) have attracted much attention as an environmentally friendly renewable energy source. 6 They play a key role in various industries such as electric vehicles, aerospace, and renewable energy systems as power cores. 7,8 During the use of LIBs, the electrode materials are gradually deactivated with the increase of using times.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A state‐of‐health estimation method considering capacity recovery of lithium batteries

Guo

Zhu³

et al. 2022

Intl J of Energy Research

View full text Add to dashboard Cite

At present, the rapid development of new energy sources makes lithium-ion batteries (LIBs) widely used, but LIBs will inevitably age during using. State of health (SOH) is a direct reflection to the aging of LIBs, so it is necessary to estimate the SOH. During the aging process of the LIBs, the phenomenon of capacity recovery will occur if the battery is standing for too long. Existing SOH estimation methods based on neural network do not propose countermeasures for the phenomenon, but in fact, capacity recovery is inevitable and it has a great impact on SOH estimation. According to this vacancy, this paper proposes a SOH estimation method based on double bi-directional long shortterm memory (DBiLSTM) model, which can accurately estimate recovered capacity and improve accuracy of SOH estimation. First, the capacity of LIB is decomposed at multiple scales using wavelet analysis, and the smooth and fluctuating components are obtained. Then six features are proposed based on the changes in the battery after aging. The proposed features are decomposed into new features suitable for the two components. Finally, the smooth component and the fluctuation component are estimated synchronously, and the estimated results are reconstructed to obtain the final estimated SOH. The method proposed in this paper is verified in the NASA dataset and compared with the bi-directional long short-term memory (BiLSTM) model. Comparing with the direct estimation by BiLSTM, the root mean square error (RMSE) is reduced by at least 0.0084 and the mean absolute percentage error (MAPE) is reduced by at least 0.52% when the battery capacity fluctuates greatly. The experimental results show that the proposed method can significantly improve the accuracy of SOH estimation when the capacity fluctuates greatly.Abbreviations: BiLSTM, bi-directional long short-term memory; CC, constant current; CNN, convolution neural network; CV, constant voltage; DBiLSTM, double bi-directional long short-term memory; FN, Nth feature; FN HF , the high frequency components of the Nth feature; FN LF , the low frequency component of the Nth feature; LIB, lithium-ion battery; LSTM, long short-term memory; MAPE, mean absolute percentage error; MLP, multi-layer perceptron; PCoE, prognostics center of excellence; RMSE, root mean square error; SOH, state of health; SVR, support vector regression.

show abstract

An application-oriented lithium-ion battery degradation modelling framework for ageing prediction

Horstkötter¹,

Bäker²

2023

Journal of Energy Storage

View full text Add to dashboard Cite

An Improved Rainflow Algorithm Combined with Linear Criterion for the Accurate Li-ion Battery Residual Life Prediction

Cited by 8 publications

References 32 publications

Optimization of battery energy storage system size and power allocation strategy for fuel cell ship

Optimization of battery energy storage system size and power allocation strategy for fuel cell ship

A state‐of‐health estimation method considering capacity recovery of lithium batteries

An application-oriented lithium-ion battery degradation modelling framework for ageing prediction

Contact Info

Product

Resources

About