State of Charge Estimation of Lithium-Ion Battery Based on Back Propagation Neural Network and AdaBoost Algorithm
The accurate estimation of the state of charge (SOC) of lithium-ion batteries is critical in battery energy storage systems. This paper introduces a novel approach, the AdaBoost–BPNN model, to overcome the limitations of traditional data-driven estimation methods, such as a low estimation accuracy a...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-11-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/16/23/7824 |
| _version_ | 1827592309490843648 |
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| author | Bingzi Cai Mutian Li Huawei Yang Chunsheng Wang Yougen Chen |
| author_facet | Bingzi Cai Mutian Li Huawei Yang Chunsheng Wang Yougen Chen |
| author_sort | Bingzi Cai |
| collection | DOAJ |
| description | The accurate estimation of the state of charge (SOC) of lithium-ion batteries is critical in battery energy storage systems. This paper introduces a novel approach, the AdaBoost–BPNN model, to overcome the limitations of traditional data-driven estimation methods, such as a low estimation accuracy and poor generalization ability. The proposed model employs a back propagation neural network (BPNN) for the preliminary estimation. Subsequently, an AdaBoost–BPNN model is developed as a strong learner using the AdaBoost integration algorithm. Each BPNN sub-model serves as a weak learner within the AdaBoost framework. The final output of the strong learner is obtained by combining the individual outputs from the weak learners using weighting factors. This adaptive adjustment of weighting factors enhances the accuracy of SOC estimation. The proposed SOC estimation algorithm is evaluated and validated through experimental analysis. Throughout the paper, theoretical analysis is conducted, and the proposed AdaBoost–BPNN model is validated and verified using experimental results. The results demonstrate that the AdaBoost–BPNN model outperforms traditional methods in accurately estimating SOC under various conditions, including constant current-constant voltage (CCCV) charging, dynamical stress testing (DST), US06, a federal urban driving schedule (FUDS), and pulse discharge conditions. |
| first_indexed | 2024-03-09T01:51:46Z |
| format | Article |
| id | doaj.art-53781f1611954e5aa4be0cafb5d0231a |
| institution | Directory Open Access Journal |
| issn | 1996-1073 |
| language | English |
| last_indexed | 2024-03-09T01:51:46Z |
| publishDate | 2023-11-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj.art-53781f1611954e5aa4be0cafb5d0231a2023-12-08T15:14:57ZengMDPI AGEnergies1996-10732023-11-011623782410.3390/en16237824State of Charge Estimation of Lithium-Ion Battery Based on Back Propagation Neural Network and AdaBoost AlgorithmBingzi Cai0Mutian Li1Huawei Yang2Chunsheng Wang3Yougen Chen4Huizhou Power Supply Bureau, Guangdong Power Grid Corporation, Huizhou 516000, ChinaSchool of Automation, Central South University, Changsha 410083, ChinaDepartment of Electrical and Computer Engineering, Florida State University, Tallahassee, FL 32304, USASchool of Automation, Central South University, Changsha 410083, ChinaSchool of Automation, Central South University, Changsha 410083, ChinaThe accurate estimation of the state of charge (SOC) of lithium-ion batteries is critical in battery energy storage systems. This paper introduces a novel approach, the AdaBoost–BPNN model, to overcome the limitations of traditional data-driven estimation methods, such as a low estimation accuracy and poor generalization ability. The proposed model employs a back propagation neural network (BPNN) for the preliminary estimation. Subsequently, an AdaBoost–BPNN model is developed as a strong learner using the AdaBoost integration algorithm. Each BPNN sub-model serves as a weak learner within the AdaBoost framework. The final output of the strong learner is obtained by combining the individual outputs from the weak learners using weighting factors. This adaptive adjustment of weighting factors enhances the accuracy of SOC estimation. The proposed SOC estimation algorithm is evaluated and validated through experimental analysis. Throughout the paper, theoretical analysis is conducted, and the proposed AdaBoost–BPNN model is validated and verified using experimental results. The results demonstrate that the AdaBoost–BPNN model outperforms traditional methods in accurately estimating SOC under various conditions, including constant current-constant voltage (CCCV) charging, dynamical stress testing (DST), US06, a federal urban driving schedule (FUDS), and pulse discharge conditions.https://www.mdpi.com/1996-1073/16/23/7824AdaBoost algorithmback propagation neural networklithium-ion batterystate of charge |
| spellingShingle | Bingzi Cai Mutian Li Huawei Yang Chunsheng Wang Yougen Chen State of Charge Estimation of Lithium-Ion Battery Based on Back Propagation Neural Network and AdaBoost Algorithm Energies AdaBoost algorithm back propagation neural network lithium-ion battery state of charge |
| title | State of Charge Estimation of Lithium-Ion Battery Based on Back Propagation Neural Network and AdaBoost Algorithm |
| title_full | State of Charge Estimation of Lithium-Ion Battery Based on Back Propagation Neural Network and AdaBoost Algorithm |
| title_fullStr | State of Charge Estimation of Lithium-Ion Battery Based on Back Propagation Neural Network and AdaBoost Algorithm |
| title_full_unstemmed | State of Charge Estimation of Lithium-Ion Battery Based on Back Propagation Neural Network and AdaBoost Algorithm |
| title_short | State of Charge Estimation of Lithium-Ion Battery Based on Back Propagation Neural Network and AdaBoost Algorithm |
| title_sort | state of charge estimation of lithium ion battery based on back propagation neural network and adaboost algorithm |
| topic | AdaBoost algorithm back propagation neural network lithium-ion battery state of charge |
| url | https://www.mdpi.com/1996-1073/16/23/7824 |
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