State of Health Estimation of Lithium-Ion Batteries Using Fusion Health Indicator by PSO-ELM Model
The accurate estimation of the State of Health (SOH) of lithium-ion batteries is essential for ensuring their safe and reliable operation, as direct measurement is not feasible. This paper presents a novel SOH estimation method that integrates Particle Swarm Optimization (PSO) with an Extreme Learni...
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| Format: | Article |
| Language: | English |
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MDPI AG
2024-10-01
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| Series: | Batteries |
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| Online Access: | https://www.mdpi.com/2313-0105/10/11/380 |
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| author | Jun Chen Yan Liu Jun Yong Cheng Yang Liqin Yan Yanping Zheng |
| author_facet | Jun Chen Yan Liu Jun Yong Cheng Yang Liqin Yan Yanping Zheng |
| author_sort | Jun Chen |
| collection | DOAJ |
| description | The accurate estimation of the State of Health (SOH) of lithium-ion batteries is essential for ensuring their safe and reliable operation, as direct measurement is not feasible. This paper presents a novel SOH estimation method that integrates Particle Swarm Optimization (PSO) with an Extreme Learning Machine (ELM) to improve prediction accuracy. Health Indicators (HIs) are first extracted from the battery’s charging curve, and correlation analysis is conducted on seven indirect HIs using Pearson and Spearman coefficients. To reduce dimensionality and eliminate redundancy, Principal Component Analysis (PCA) is applied, with the principal component contributing over 94% used as a fusion HI to represent battery capacity degradation. PSO is then employed to optimize the weights (<i>ε</i>) between the input and hidden layers, as well as the hidden layer bias (<i>u</i>) in the ELM, treating these parameters as particles in the PSO framework. This optimization enhances the ELM’s performance, addressing instability issues in the standard algorithm. The proposed PSO-ELM model demonstrates superior accuracy in SOH prediction compared with ELM and other methods. Experimental results show that the mean absolute error (MAE) is 0.0034, the mean absolute percentage error (MAPE) is 0.467%, and the root mean square error (RMSE) is 0.0043, providing a valuable reference for battery safety and reliability assessments. |
| format | Article |
| id | doaj-art-58f77906c0fc4c1fa2e205608182e04e |
| institution | OA Journals |
| issn | 2313-0105 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Batteries |
| spelling | doaj-art-58f77906c0fc4c1fa2e205608182e04e2025-08-20T02:26:59ZengMDPI AGBatteries2313-01052024-10-01101138010.3390/batteries10110380State of Health Estimation of Lithium-Ion Batteries Using Fusion Health Indicator by PSO-ELM ModelJun Chen0Yan Liu1Jun Yong2Cheng Yang3Liqin Yan4Yanping Zheng5College of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaCollege of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaState Grid Dangtu County Power Supply Company, Maanshan 243100, ChinaState Key Laboratory of Space Power-Sources, Shanghai Institute of Space Power-Sources, Shanghai 200245, ChinaState Key Laboratory of Space Power-Sources, Shanghai Institute of Space Power-Sources, Shanghai 200245, ChinaCollege of Automobile and Traffic Engineering, Nanjing Forestry University, Nanjing 210037, ChinaThe accurate estimation of the State of Health (SOH) of lithium-ion batteries is essential for ensuring their safe and reliable operation, as direct measurement is not feasible. This paper presents a novel SOH estimation method that integrates Particle Swarm Optimization (PSO) with an Extreme Learning Machine (ELM) to improve prediction accuracy. Health Indicators (HIs) are first extracted from the battery’s charging curve, and correlation analysis is conducted on seven indirect HIs using Pearson and Spearman coefficients. To reduce dimensionality and eliminate redundancy, Principal Component Analysis (PCA) is applied, with the principal component contributing over 94% used as a fusion HI to represent battery capacity degradation. PSO is then employed to optimize the weights (<i>ε</i>) between the input and hidden layers, as well as the hidden layer bias (<i>u</i>) in the ELM, treating these parameters as particles in the PSO framework. This optimization enhances the ELM’s performance, addressing instability issues in the standard algorithm. The proposed PSO-ELM model demonstrates superior accuracy in SOH prediction compared with ELM and other methods. Experimental results show that the mean absolute error (MAE) is 0.0034, the mean absolute percentage error (MAPE) is 0.467%, and the root mean square error (RMSE) is 0.0043, providing a valuable reference for battery safety and reliability assessments.https://www.mdpi.com/2313-0105/10/11/380lithium-ion batteryState of Healthfusion health indicatorsPSO-ELM |
| spellingShingle | Jun Chen Yan Liu Jun Yong Cheng Yang Liqin Yan Yanping Zheng State of Health Estimation of Lithium-Ion Batteries Using Fusion Health Indicator by PSO-ELM Model Batteries lithium-ion battery State of Health fusion health indicators PSO-ELM |
| title | State of Health Estimation of Lithium-Ion Batteries Using Fusion Health Indicator by PSO-ELM Model |
| title_full | State of Health Estimation of Lithium-Ion Batteries Using Fusion Health Indicator by PSO-ELM Model |
| title_fullStr | State of Health Estimation of Lithium-Ion Batteries Using Fusion Health Indicator by PSO-ELM Model |
| title_full_unstemmed | State of Health Estimation of Lithium-Ion Batteries Using Fusion Health Indicator by PSO-ELM Model |
| title_short | State of Health Estimation of Lithium-Ion Batteries Using Fusion Health Indicator by PSO-ELM Model |
| title_sort | state of health estimation of lithium ion batteries using fusion health indicator by pso elm model |
| topic | lithium-ion battery State of Health fusion health indicators PSO-ELM |
| url | https://www.mdpi.com/2313-0105/10/11/380 |
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