Exploiting the Electrochemical Impedance Spectroscopy Frequency Profiles for State-of-Health Predication of Lithium-Ion Battery
被引:2
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作者:
Al-Hiyali, Mohammed Isam
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Univ Teknol PETRONAS, Dept Elect & Elect Engn, Seri Iskandar 32610, Perak, Malaysia
AL Mansour Univ Coll, Med Instruments Technol Engn Dept, Baghdad 10068, IraqUniv Teknol PETRONAS, Dept Elect & Elect Engn, Seri Iskandar 32610, Perak, Malaysia
Al-Hiyali, Mohammed Isam
[1
,2
]
Kannan, Ramani
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Univ Teknol PETRONAS, Dept Elect & Elect Engn, Seri Iskandar 32610, Perak, MalaysiaUniv Teknol PETRONAS, Dept Elect & Elect Engn, Seri Iskandar 32610, Perak, Malaysia
Kannan, Ramani
[1
]
Alharthi, Yahya Z.
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Univ Hafr Albatin, Coll Engn, Dept Elect Engn, Hafar al Batin 39524, Saudi ArabiaUniv Teknol PETRONAS, Dept Elect & Elect Engn, Seri Iskandar 32610, Perak, Malaysia
Alharthi, Yahya Z.
[3
]
Shutari, Hussein
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Univ Sains Malaysia, Sch Elect & Elect Engn, Nibong Tebal 14300, Penang, MalaysiaUniv Teknol PETRONAS, Dept Elect & Elect Engn, Seri Iskandar 32610, Perak, Malaysia
Shutari, Hussein
[4
]
机构:
[1] Univ Teknol PETRONAS, Dept Elect & Elect Engn, Seri Iskandar 32610, Perak, Malaysia
[2] AL Mansour Univ Coll, Med Instruments Technol Engn Dept, Baghdad 10068, Iraq
[3] Univ Hafr Albatin, Coll Engn, Dept Elect Engn, Hafar al Batin 39524, Saudi Arabia
[4] Univ Sains Malaysia, Sch Elect & Elect Engn, Nibong Tebal 14300, Penang, Malaysia
Battery Management Systems (BMS) are essential for optimizing battery performance and extending lifespan through continuous monitoring and decision-making via control sensors. The State of Health (SOH) is one of the BMS metrics that provides valuable information on battery health and degradation. However, one of the main challenges in the BMS domain development is finding accurate and effective algorithms for battery SOH prediction, especially for electric vehicles and grid-connected energy storage systems. This study introduces a new SOH prediction method using wavelet-convolutional neural regression networks (CNRN) algorithms. The methodology involves extracting detailed frequency profiles from Electrochemical Impedance Spectroscopy (EIS) data, which are processed through wavelet transformation to capture both time and frequency domain features. These transformed profiles are then input into the CNRN model for SOH prediction. The results demonstrate improved SOH prediction accuracy with EIS frequency profiles, evidenced by a reduction in root mean square error (RMSE) compared to the standard EIS profile. This improvement is due to the fact that the wavelet-CNRN algorithm efficiently captures both the time and frequency features of the battery impedance. Moreover, the performance of the proposed algorithm demonstrated robustness in early end-of-life (EOL) prediction, thereby enhancing the reliability and safety of BMS functions.
机构:
Shanghai Univ Elect Power, Shanghai Key Lab Mat Protect & Adv Mat Elect Power, Shanghai 200090, Peoples R ChinaShanghai Univ Elect Power, Shanghai Key Lab Mat Protect & Adv Mat Elect Power, Shanghai 200090, Peoples R China
Shao, Bohan
Zhong, Jun
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Shenzhen Power Supply Bur Co Ltd, Shenzhen 518001, Peoples R ChinaShanghai Univ Elect Power, Shanghai Key Lab Mat Protect & Adv Mat Elect Power, Shanghai 200090, Peoples R China
Zhong, Jun
Tian, Jie
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Shenzhen Power Supply Bur Co Ltd, Shenzhen 518001, Peoples R ChinaShanghai Univ Elect Power, Shanghai Key Lab Mat Protect & Adv Mat Elect Power, Shanghai 200090, Peoples R China
Tian, Jie
Li, Yan
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Shenzhen Power Supply Bur Co Ltd, Shenzhen 518001, Peoples R ChinaShanghai Univ Elect Power, Shanghai Key Lab Mat Protect & Adv Mat Elect Power, Shanghai 200090, Peoples R China
Li, Yan
Chen, Xiyu
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Shanghai Univ Elect Power, Shanghai Key Lab Mat Protect & Adv Mat Elect Power, Shanghai 200090, Peoples R ChinaShanghai Univ Elect Power, Shanghai Key Lab Mat Protect & Adv Mat Elect Power, Shanghai 200090, Peoples R China
Chen, Xiyu
Dou, Weilin
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Shanghai Univ Elect Power, Shanghai Key Lab Mat Protect & Adv Mat Elect Power, Shanghai 200090, Peoples R ChinaShanghai Univ Elect Power, Shanghai Key Lab Mat Protect & Adv Mat Elect Power, Shanghai 200090, Peoples R China
Dou, Weilin
Liao, Qiangqiang
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Shanghai Univ Elect Power, Shanghai Key Lab Mat Protect & Adv Mat Elect Power, Shanghai 200090, Peoples R ChinaShanghai Univ Elect Power, Shanghai Key Lab Mat Protect & Adv Mat Elect Power, Shanghai 200090, Peoples R China
Liao, Qiangqiang
Lai, Chunyan
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Shanghai Univ Elect Power, Shanghai Key Lab Mat Protect & Adv Mat Elect Power, Shanghai 200090, Peoples R ChinaShanghai Univ Elect Power, Shanghai Key Lab Mat Protect & Adv Mat Elect Power, Shanghai 200090, Peoples R China
Lai, Chunyan
Lu, Taolin
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Shanghai Univ Elect Power, Shanghai Key Lab Mat Protect & Adv Mat Elect Power, Shanghai 200090, Peoples R ChinaShanghai Univ Elect Power, Shanghai Key Lab Mat Protect & Adv Mat Elect Power, Shanghai 200090, Peoples R China
Lu, Taolin
Xie, Jingying
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Shanghai Univ Elect Power, Shanghai Key Lab Mat Protect & Adv Mat Elect Power, Shanghai 200090, Peoples R ChinaShanghai Univ Elect Power, Shanghai Key Lab Mat Protect & Adv Mat Elect Power, Shanghai 200090, Peoples R China