A fusion method based on EEMD, PCA, improved LSTM, and GS-TR algorithm for SOH prediction of lithium-ion batteries

2021 ◽  
pp. 1-35
Author(s):  
Xiongbin Peng ◽  
Yuwu Li
Keyword(s):  
Short Term Memory ◽  
Pearson Correlation ◽  
Gaussian Function ◽  
Low Frequency ◽  
Trust Region ◽  
Lithium Ion ◽  
Ensemble Empirical Mode Decomposition ◽  
Fusion Method ◽  
Battery Capacity ◽  
The Impact

Abstract Aiming at the phenomenon of battery capacity regeneration, which leads to inaccurate prediction of lithium-ion battery state of health (SOH), a new fusion method based on ensemble empirical mode decomposition (EEMD), Pearson correlation analysis (PCA), and improved long short-term memory (LSTM) network and Gaussian function-trust region (GS-TR) algorithm is introduced to predict battery SOH. Firstly, adopt the EEMD method to process the battery SOH data to eliminate the impact of capacity recovery. Secondly, the decomposed data signals are classified by the PCA method, and the signals classified as high frequency and low frequency are respectively predicted by the improved LSTM algorithm and the GS-TR algorithm. Finally, the prediction results of the improved LSTM and GS-TR algorithms are integrated. The proposed fusion method avoids the complexity of the hybrid neural network model and improves the prediction efficiency. Based on the average results of the three data sets from NASA, the RMSE result of the proposed algorithm is reduced by 9.56% compared with the improved LSTM with the EEMD algorithm, and 37.57% compared with the improved LSTM without the EEMD algorithm. The results show that the proposed method has higher adaptability and prediction accuracy.

scholarly journals State-of-Health Estimate for the Lithium-Ion Battery Using Chi-Square and ELM-LSTM

2021 ◽  
Vol 12 (4) ◽  
pp. 228
Author(s):  
Jianfeng Jiang ◽  
Shaishai Zhao ◽  
Chaolong Zhang
Keyword(s):  
Neural Network ◽  
Lithium Ion Battery ◽  
Short Term Memory ◽  
Lithium Ion ◽  
Constant Current ◽  
State Of Health ◽  
Chi Square ◽  
Mean Temperature ◽  
Battery Capacity ◽  
Learning Machine

The state-of-health (SOH) estimation is of extreme importance for the performance maximization and upgrading of lithium-ion battery. This paper is concerned with neural-network-enabled battery SOH indication and estimation. The insight that motivates this work is that the chi-square of battery voltages of each constant current-constant voltage phrase and mean temperature could reflect the battery capacity loss effectively. An ensemble algorithm composed of extreme learning machine (ELM) and long short-term memory (LSTM) neural network is utilized to capture the underlying correspondence between the SOH, mean temperature and chi-square of battery voltages. NASA battery data and battery pack data are used to demonstrate the estimation procedures and performance of the proposed approach. The results show that the proposed approach can estimate the battery SOH accurately. Meanwhile, comparative experiments are designed to compare the proposed approach with the separate used method, and the proposed approach shows better estimation performance in the comparisons.

scholarly journals Impacts of Driving Conditions on EV Battery Pack Life Cycle

2020 ◽  
Vol 11 (1) ◽  
pp. 17 ◽  
Author(s):  
Huijun Liu ◽  
Fenfang Chen ◽  
Yuxiang Tong ◽  
Zihang Wang ◽  
Xiaoli Yu ◽  
...  
Keyword(s):  
Life Cycle ◽  
Test Procedure ◽  
Lithium Ion ◽  
Driving Cycle ◽  
Battery Pack ◽  
Battery Life ◽  
Ambient Temperatures ◽  
Battery Capacity ◽  
Driving Conditions ◽  
The Impact

The aging of lithium-ion batteries (LIBs) is a crucial issue and must be investigated. The aging rate of LIBs depends not only on the material and electrochemical performance but also on the working conditions. In order to assess the impact of vehicle driving conditions, including the driving cycle, ambient temperature, charging mode, and trip distance on the battery life cycle, this paper first establishes an electric vehicle (EV) energy flow model to solve the operating parameters of the battery pack while working. Then, a powertrain test is carried out to verify the simulation model. Based on the simulated data under different conditions, the battery capacity fade process is estimated by using a semi-empirical aging model. The mileage (Ф) traveled by the vehicle before the end of life (EOL) of the battery pack is then calculated and taken as the evaluation index. The results indicate that the Ф is higher when the vehicle drives the Japanese chassis dynamometer test cycle JC08 than in the New European Driving Cycle (NEDC) and the Federal Test Procedure (FTP-75). The Ф will be dramatically reduced at both low and high ambient temperatures. Fast charging can increase the Ф at low ambient temperatures, whereas long trip driving can always increase Ф to varying degrees.

scholarly journals A LSTM-STW and GS-LM Fusion Method for Lithium-Ion Battery RUL Prediction Based on EEMD

Energies ◽  
2020 ◽  
Vol 13 (9) ◽  
pp. 2380 ◽  
Author(s):  
Ling Mao ◽  
Jie Xu ◽  
Jiajun Chen ◽  
Jinbin Zhao ◽  
Yuebao Wu ◽  
...  
Keyword(s):  
Lithium Ion Battery ◽  
High Frequency ◽  
Short Term Memory ◽  
Time Window ◽  
Low Frequency ◽  
Lithium Ion ◽  
Remaining Useful Life ◽  
Marquardt Algorithm ◽  
Starting Point ◽  
Frequency Components

To address inaccurate prediction in remaining useful life (RUL) in current Lithium-ion batteries, this paper develops a Long Short-Term Memory Network, Sliding Time Window (LSTM-STW) and Gaussian or Sine function, Levenberg-Marquardt algorithm (GS-LM) fusion batteries RUL prediction method based on ensemble empirical mode decomposition (EEMD). Firstly, EEMD is used to decompose the original data into high-frequency and low-frequency components. Secondly, LSTM-STW and GS-LM are used to predict the high-frequency and low-frequency components, respectively. Finally, the LSTM-STW and GS-LM prediction results are effectively integrated in order to obtain the final prediction of the lithium-ion battery RUL results. This article takes the lithium-ion battery data published by NASA as input. The experimental results show that the method has higher accuracy, including the phenomenon of sudden capacity increase, and is less affected by the prediction starting point. The performance of the proposed method is better than other typical battery RUL prediction methods.

scholarly journals An Improved Model Equation Based on a Gaussian Function Trinomial for State of Charge Estimation of Lithium-ion Batteries

Energies ◽  
2019 ◽  
Vol 12 (7) ◽  
pp. 1366 ◽  
Author(s):  
Jinqing Linghu ◽  
Longyun Kang ◽  
Ming Liu ◽  
Bihua Hu ◽  
Zefeng Wang
Keyword(s):  
Computational Efficiency ◽  
Model Equation ◽  
Gaussian Function ◽  
Lithium Ion ◽  
State Of Charge ◽  
Estimation Accuracy ◽  
Soc Estimation ◽  
Proposed Model ◽  
Model Equations ◽  
The Impact

Establishing a model equation with high accuracy and high computational efficiency is very important for the estimation of battery state of charge (SOC). To ensure better SOC estimation results, most studies have focused on the improvement of the algorithm, while the impact of the model equation which may offset the benefits of advanced algorithms has been overlooked. To address this problem, this paper studies the widely used model equations and presents a new model equation based on a Gaussian function that improves the SOC estimation accuracy and computational efficiency. With the Worldwide harmonized Light Vehicles Test Cycle (WLTC) which is highly dynamic and more realistic than any other driving cycles, the proposed model equation is applied to different filtering algorithms to validate its performance in SOC estimation. The results indicate that the proposed model equation can greatly improve the accuracy of SOC estimation without an increase of computation. In addition, for the traditional polynomial-based model equations, the 6th-order power function polynomial has better performance in SOC estimation than polynomials with other orders.

scholarly journals A Frequency Decomposition-Based Hybrid Forecasting Algorithm for Short-Term Reactive Power

Energies ◽  
2021 ◽  
Vol 14 (20) ◽  
pp. 6606
Author(s):  
Jiabao Du ◽  
Changxi Yue ◽  
Ying Shi ◽  
Jicheng Yu ◽  
Fan Sun ◽  
...  
Keyword(s):  
High Frequency ◽  
Reactive Power ◽  
Short Term Memory ◽  
Low Frequency ◽  
Ensemble Empirical Mode Decomposition ◽  
Short Term ◽  
Prediction Ability ◽  
Frequency Decomposition ◽  
Mode Decomposition ◽  
Speed Up

This paper proposes a new frequency decomposition-based hybrid reactive power forecasting algorithm, EEMD-LSTM-RFR (ELR), which adopts a strategy of frequency decomposition prediction after ensemble empirical mode decomposition and then data reconstruction to improve the prediction ability of reactive power. This decomposition process can compress the high frequency of reactive power and benefits the following separate forecasting. Long short-term memory is proposed for the high-frequency feature of reactive power to deal with the forecasting difficulty caused by strong signal disturbance and randomness. In contrast, random forest regression is applied to the low-frequency part in order to speed up the forecasting. Four classical algorithms and four hybrid algorithms based on different signal decompositions are compared with the proposed algorithm, and the results show that the proposed algorithm outperforms those algorithms. The predicting index RMSE decreases to 0.687, while the fitting degree R2 gradually approaches 1 with a step-by-step superposition of high-frequency signals, indicating that the proposed decomposition-predicting reconstruction strategy is effective.

scholarly journals Temporal Convolution Network Based Joint Optimization of Acoustic-to-Articulatory Inversion

2021 ◽  
Vol 11 (19) ◽  
pp. 9056
Author(s):  
Guolun Sun ◽  
Zhihua Huang ◽  
Li Wang ◽  
Pengyuan Zhang
Keyword(s):  
Speech Synthesis ◽  
Short Term Memory ◽  
Pearson Correlation ◽  
Target Function ◽  
Model Parameters ◽  
Acoustic Feature ◽  
Inversion Model ◽  
Articulatory Features ◽  
Articulatory Inversion ◽  
The Impact

Articulatory features are proved to be efficient in the area of speech recognition and speech synthesis. However, acquiring articulatory features has always been a difficult research hotspot. A lightweight and accurate articulatory model is of significant meaning. In this study, we propose a novel temporal convolution network-based acoustic-to-articulatory inversion system. The acoustic feature is converted into a high-dimensional hidden space feature map through temporal convolution with frame-level feature correlations taken into account. Meanwhile, we construct a two-part target function combining prediction’s Root Mean Square Error (RMSE) and the sequences’ Pearson Correlation Coefficient (PCC) to jointly optimize the performance of the specific inversion model from both aspects. We also further conducted an analysis on the impact of the weight between the two parts on the final performance of the inversion model. Extensive experiments have shown that our, temporal convolution networks (TCN) model outperformed the Bi-derectional Long Short Term Memory model by 1.18 mm in RMSE and 0.845 in PCC with 14 model parameters when optimizing evenly with RMSE and PCC aspects.

scholarly journals A multi-scale fusion prediction method for lithium-ion battery capacity based on ensemble empirical mode decomposition and nonlinear autoregressive neural networks

2019 ◽  
Vol 15 (3) ◽  
pp. 155014771983963 ◽  
Author(s):  
Pei Wang ◽  
Xue Dan ◽  
Yong Yang
Keyword(s):  
Neural Networks ◽  
Lithium Ion Battery ◽  
Empirical Mode Decomposition ◽  
Prediction Method ◽  
Lithium Ion ◽  
Ensemble Empirical Mode Decomposition ◽  
Multi Scale ◽  
Mode Decomposition ◽  
Battery Capacity ◽  
Nonlinear Autoregressive

Lithium-ion battery has been widely used in various fields due to its excellent performance. How to accurately predict its current capacity throughout a battery full lifetime has been a key technology for power system management, assurance, and predictive maintenance. In order to overcome low precision problem in long-term prediction for lithium-ion battery capacity, this article proposes a multi-scale fusion prediction method based on ensemble empirical mode decomposition and nonlinear autoregressive models neural networks. The proposed method uses ensemble empirical mode decomposition to decompose the battery capacity measurement sequence to generate multiple intrinsic mode function components on different scales. Then, each component is predicted by nonlinear autoregressive neural networks; finally, the prediction results of each component are reconstructed to obtain the final battery capacity prediction sequence. Experimental results show that the proposed method has higher prediction accuracy and signal adaptability than single nonlinear autoregressive neural networks.

USING FIELD DATA FOR THE EVALUATION OF THE IMPACT OF ULTRA-LOW FREQUENCY MOTIONS ON THE FATIGUE OF MOORING LINES

2019 ◽  
Author(s):  
Guilherme Borzacchiello ◽  
Carl Albrecht ◽  
Fabricio N Correa ◽  
Breno Jacob ◽  
Guilherme da Silva Leal
Keyword(s):  
Field Data ◽  
Low Frequency ◽  
Mooring Lines ◽  
The Impact

scholarly journals Advances of 2nd Life Applications for Lithium Ion Batteries from Electric Vehicles Based on Energy Demand

2021 ◽  
Vol 13 (10) ◽  
pp. 5726
Author(s):  
Aleksandra Wewer ◽  
Pinar Bilge ◽  
Franz Dietrich
Keyword(s):  
Life Cycle ◽  
Lithium Ion Batteries ◽  
Electric Vehicles ◽  
Energy Demand ◽  
Lithium Ion ◽  
Life Cycles ◽  
Future Research ◽  
Research Areas ◽  
Study Results ◽  
The Impact

Electromobility is a new approach to the reduction of CO2 emissions and the deceleration of global warming. Its environmental impacts are often compared to traditional mobility solutions based on gasoline or diesel engines. The comparison pertains mostly to the single life cycle of a battery. The impact of multiple life cycles remains an important, and yet unanswered, question. The aim of this paper is to demonstrate advances of 2nd life applications for lithium ion batteries from electric vehicles based on their energy demand. Therefore, it highlights the limitations of a conventional life cycle analysis (LCA) and presents a supplementary method of analysis by providing the design and results of a meta study on the environmental impact of lithium ion batteries. The study focuses on energy demand, and investigates its total impact for different cases considering 2nd life applications such as (C1) material recycling, (C2) repurposing and (C3) reuse. Required reprocessing methods such as remanufacturing of batteries lie at the basis of these 2nd life applications. Batteries are used in their 2nd lives for stationary energy storage (C2, repurpose) and electric vehicles (C3, reuse). The study results confirm that both of these 2nd life applications require less energy than the recycling of batteries at the end of their first life and the production of new batteries. The paper concludes by identifying future research areas in order to generate precise forecasts for 2nd life applications and their industrial dissemination.

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