Prediction and Diagnosis of Electric Vehicle Battery Fault Based on Abnormal Voltage: Using Decision Tree Algorithm Theories and Isolated Forest

Battery voltage is a pivotal parameter for evaluating battery health and safety. The precise prediction of battery voltage and the implementation of anomaly detection are imperative for ensuring the secure and dependable operation of battery systems. Nevertheless, during the actual operation of elec...

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Veröffentlicht in:	Processes Jg. 12; H. 1; S. 136
Hauptverfasser:	Zhang, Zhaosheng, Dong, Shiji, Li, Da, Liu, Peng, Wang, Zhenpo
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.01.2024
Schlagworte:	Algorithms Anomalies Batteries Carbon Circuits Copper Decision making Decision theory Decision trees Electric potential Electric vehicles Electrodes Electrolytes Fault diagnosis Fuzzy logic Heat Lithium Parameters Predictions Product safety Traffic safety Voltage
ISSN:	2227-9717, 2227-9717
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Abstract	Battery voltage is a pivotal parameter for evaluating battery health and safety. The precise prediction of battery voltage and the implementation of anomaly detection are imperative for ensuring the secure and dependable operation of battery systems. Nevertheless, during the actual operation of electric vehicles, battery performance is subject to the influence of the vehicle's operational state and battery characteristic parameters, introducing challenges to safety alerts. In order to address these challenges and achieve precise battery voltage prediction, this paper comprehensively considers the battery characteristics and driving behavior of electric vehicles in both charging and operational states. Mathematical processing, including averaging and variance calculation, is applied to the battery characteristic parameter data and driving behavior data. By integrating historical voltage data and employing a modified gradient boosting decision tree algorithm (GBDT), a fast and accurate online voltage prediction method is proposed. Hyperparameter optimization is employed to minimize prediction voltage errors. The accuracy and timeliness of the predictions are validated through a comprehensive evaluation and comparison of the forecasted voltages. To diagnose anomalies in battery voltage, the paper proposes a fault diagnosis method that combines the Isolation Forest and Boxplot techniques. Finally, utilizing authentic electric vehicle data for validation, the research underscores the capability of the proposed method to achieve accurate voltage predictions six minutes in advance and provide effective fault diagnosis. This investigation carries substantial practical implications for fortifying battery management and optimizing the performance of electric vehicles.
AbstractList	Battery voltage is a pivotal parameter for evaluating battery health and safety. The precise prediction of battery voltage and the implementation of anomaly detection are imperative for ensuring the secure and dependable operation of battery systems. Nevertheless, during the actual operation of electric vehicles, battery performance is subject to the influence of the vehicle's operational state and battery characteristic parameters, introducing challenges to safety alerts. In order to address these challenges and achieve precise battery voltage prediction, this paper comprehensively considers the battery characteristics and driving behavior of electric vehicles in both charging and operational states. Mathematical processing, including averaging and variance calculation, is applied to the battery characteristic parameter data and driving behavior data. By integrating historical voltage data and employing a modified gradient boosting decision tree algorithm (GBDT), a fast and accurate online voltage prediction method is proposed. Hyperparameter optimization is employed to minimize prediction voltage errors. The accuracy and timeliness of the predictions are validated through a comprehensive evaluation and comparison of the forecasted voltages. To diagnose anomalies in battery voltage, the paper proposes a fault diagnosis method that combines the Isolation Forest and Boxplot techniques. Finally, utilizing authentic electric vehicle data for validation, the research underscores the capability of the proposed method to achieve accurate voltage predictions six minutes in advance and provide effective fault diagnosis. This investigation carries substantial practical implications for fortifying battery management and optimizing the performance of electric vehicles.
Audience	Academic
Author	Zhang, Zhaosheng Liu, Peng Li, Da Dong, Shiji Wang, Zhenpo
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Cites_doi	10.1149/2.0311410jes 10.1109/ICARCV.2006.345303 10.1016/j.jpowsour.2012.03.015 10.1016/j.conengprac.2016.08.002 10.1016/j.apenergy.2015.10.168 10.1016/j.arcontrol.2004.12.002 10.1109/TPEL.2015.2439578 10.1016/j.measurement.2018.09.007 10.1016/j.measurement.2020.108052 10.1109/ICDM.2008.17 10.1016/j.jpowsour.2011.04.024 10.1016/j.patrec.2017.09.009 10.1016/S0378-7753(01)00703-0 10.1016/j.jpowsour.2012.12.032 10.1016/j.jclepro.2018.03.259 10.1149/2.1381910jes 10.1109/CVCI47823.2019.8951717 10.1214/aos/1013203451 10.1117/12.2586332 10.1155/2015/631263 10.1016/j.apenergy.2019.113574 10.1016/j.est.2018.01.010 10.1109/QRS-C.2019.00098 10.1016/j.est.2022.106584 10.3390/en8054400 10.1016/j.microrel.2016.07.051 10.1016/j.jpowsour.2011.03.035 10.1016/j.ensm.2020.10.020 10.1016/j.apenergy.2019.113381 10.1109/MIE.2020.2964814 10.1016/j.apenergy.2018.01.068 10.1109/ITEC.2014.6861806 10.3390/en9050387 10.1109/TIE.2017.2787586 10.3390/pr7010038 10.1149/1.1379740 10.1109/TIA.2018.2874588 10.1109/iSPEC48194.2019.8975122 10.1016/j.energy.2019.02.147 10.1016/j.apenergy.2019.114170 10.20944/preprints201705.0116.v1 10.1109/TIE.2007.896496 10.1109/ACCESS.2017.2780258 10.1109/TPEL.2022.3150026 10.1002/cjoc.200890286 10.1016/j.energy.2016.08.094 10.1109/TCST.2009.2026285
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References	Li (ref_36) 2023; 60 Leising (ref_3) 2001; 148 Feng (ref_13) 2016; 115 Duan (ref_21) 2018; 16 Hong (ref_43) 2019; 251 ref_19 Hu (ref_40) 2016; 63 ref_17 Meng (ref_49) 2016; 31 Kang (ref_39) 2020; 259 Jindal (ref_15) 2019; 166 Li (ref_30) 2019; 174 Ren (ref_14) 2021; 34 Held (ref_18) 2016; 64 ref_24 ref_20 Tobar (ref_46) 2018; 105 Xiong (ref_2) 2017; 6 Isermann (ref_22) 2005; 29 Hu (ref_16) 2020; 14 ref_27 Sun (ref_48) 2015; 8 Gong (ref_28) 2016; 52 Wu (ref_7) 2015; 2015 Wang (ref_31) 2019; 131 Chemali (ref_50) 2018; 65 Yang (ref_26) 2018; 187 Larsson (ref_10) 2014; 161 Li (ref_12) 2019; 254 ref_34 ref_33 Friedman (ref_41) 2001; 29 Dey (ref_25) 2016; 56 Liu (ref_32) 2017; 185 ref_38 ref_37 Li (ref_11) 2008; 26 Lee (ref_51) 2008; 55 Orendorff (ref_9) 2011; 196 Kim (ref_29) 2012; 210 Saito (ref_4) 2001; 97 Zhang (ref_1) 2020; 164 Hwang (ref_23) 2010; 18 Li (ref_35) 2022; 37 ref_44 ref_42 Zhao (ref_45) 2019; 55 Lin (ref_5) 2013; 230 Cai (ref_8) 2011; 196 ref_6 Chen (ref_47) 2018; 213
References_xml	– volume: 161 start-page: A1611 year: 2014 ident: ref_10 article-title: Abuse by External Heating, Overcharge and Short Circuiting of Commercial Lithium-Ion Battery Cells publication-title: J. Electrochem. Soc. doi: 10.1149/2.0311410jes – ident: ref_17 doi: 10.1109/ICARCV.2006.345303 – volume: 210 start-page: 243 year: 2012 ident: ref_29 article-title: Fail-safe design for large capacity lithium-ion battery systems publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2012.03.015 – volume: 56 start-page: 37 year: 2016 ident: ref_25 article-title: Model-based real-time thermal fault diagnosis of Lithium-ion batteries publication-title: Control Eng. Pract. doi: 10.1016/j.conengprac.2016.08.002 – volume: 185 start-page: 2033 year: 2017 ident: ref_32 article-title: Sensor fault detection and isolation for a lithium-ion battery pack in electric vehicles using adaptive extended Kalman filter publication-title: Appl. Energy doi: 10.1016/j.apenergy.2015.10.168 – volume: 29 start-page: 71 year: 2005 ident: ref_22 article-title: Model-based fault-detection and diagnosis—Status and applications publication-title: Annu. Rev. Control doi: 10.1016/j.arcontrol.2004.12.002 – volume: 31 start-page: 2226 year: 2016 ident: ref_49 article-title: Lithium Polymer Battery State-of-Charge Estimation Based on Adaptive Unscented Kalman Filter and Support Vector Machine publication-title: IEEE Trans. Power Electron. doi: 10.1109/TPEL.2015.2439578 – volume: 131 start-page: 443 year: 2019 ident: ref_31 article-title: Model based insulation fault diagnosis for lithium-ion battery pack in electric vehicles publication-title: Measurement doi: 10.1016/j.measurement.2018.09.007 – volume: 164 start-page: 108052 year: 2020 ident: ref_1 article-title: Deep learning-based prognostic approach for lithium-ion batteries with adaptive time-series prediction and on-line validation publication-title: Measurement doi: 10.1016/j.measurement.2020.108052 – ident: ref_42 doi: 10.1109/ICDM.2008.17 – volume: 196 start-page: 7779 year: 2011 ident: ref_8 article-title: Experimental simulation of internal short circuit in Li-ion and Li-ion-polymer cells publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2011.04.024 – volume: 105 start-page: 200 year: 2018 ident: ref_46 article-title: Improving battery voltage prediction in an electric bicycle using altitude measurements and kernel adaptive filters publication-title: Pattern Recognit. Lett. doi: 10.1016/j.patrec.2017.09.009 – volume: 97 start-page: 693 year: 2001 ident: ref_4 article-title: Thermal behaviors of lithium-ion cells during overcharge publication-title: J. Power Sources doi: 10.1016/S0378-7753(01)00703-0 – volume: 63 start-page: 2645 year: 2016 ident: ref_40 article-title: Battery Health Prognosis for Electric Vehicles Using Sample Entropy and Sparse Bayesian Predictive Modeling publication-title: IEEE Trans. Ind. Electron. – volume: 230 start-page: 32 year: 2013 ident: ref_5 article-title: In situ high-energy X-ray diffraction to study overcharge abuse of 18650-size lithium-ion battery publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2012.12.032 – volume: 187 start-page: 950 year: 2018 ident: ref_26 article-title: A fractional-order model-based battery external short circuit fault diagnosis approach for all-climate electric vehicles application publication-title: J. Clean. Prod. doi: 10.1016/j.jclepro.2018.03.259 – volume: 166 start-page: A2165 year: 2019 ident: ref_15 article-title: Review-Understanding the Thermal Runaway Behavior of Li-Ion Batteries through Experimental Techniques publication-title: J. Electrochem. Soc. doi: 10.1149/2.1381910jes – ident: ref_6 doi: 10.1109/CVCI47823.2019.8951717 – volume: 29 start-page: 1189 year: 2001 ident: ref_41 article-title: Greedy Function Approximation: A Gradient Boosting Machine publication-title: Ann. Stat. doi: 10.1214/aos/1013203451 – ident: ref_37 doi: 10.1117/12.2586332 – volume: 2015 start-page: 631263 year: 2015 ident: ref_7 article-title: A Review on Fault Mechanism and Diagnosis Approach for Li-Ion Batteries publication-title: J. Nanomater. doi: 10.1155/2015/631263 – volume: 254 start-page: 113574 year: 2019 ident: ref_12 article-title: Lithium-ion battery overcharging thermal characteristics analysis and an impedance-based electro-thermal coupled model simulation publication-title: Appl. Energy doi: 10.1016/j.apenergy.2019.113574 – volume: 16 start-page: 160 year: 2018 ident: ref_21 article-title: Evaluation of battery inconsistency based on information entropy publication-title: J. Energy Storage doi: 10.1016/j.est.2018.01.010 – ident: ref_38 doi: 10.1109/QRS-C.2019.00098 – volume: 60 start-page: 106584 year: 2023 ident: ref_36 article-title: A weighted Pearson correlation coefficient based multi-fault comprehensive diagnosis for battery circuits publication-title: J. Energy Storage doi: 10.1016/j.est.2022.106584 – volume: 8 start-page: 4400 year: 2015 ident: ref_48 article-title: Online Internal Temperature Estimation for Lithium-Ion Batteries Based on Kalman Filter publication-title: Energies doi: 10.3390/en8054400 – volume: 64 start-page: 705 year: 2016 ident: ref_18 article-title: Safe cell, safe battery? Battery fire investigation using FMEA, FTA and practical experiments publication-title: Microelectron. Reliab. doi: 10.1016/j.microrel.2016.07.051 – volume: 196 start-page: 6554 year: 2011 ident: ref_9 article-title: Experimental triggers for internal short circuits in lithium-ion cells publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2011.03.035 – volume: 34 start-page: 563 year: 2021 ident: ref_14 article-title: Investigating the relationship between internal short circuit and thermal runaway of lithium-ion batteries under thermal abuse condition publication-title: Energy Storage Mater. doi: 10.1016/j.ensm.2020.10.020 – volume: 251 start-page: 113381 year: 2019 ident: ref_43 article-title: Fault prognosis of battery system based on accurate voltage abnormity prognosis using long short-term memory neural networks publication-title: Appl. Energy doi: 10.1016/j.apenergy.2019.113381 – volume: 14 start-page: 65 year: 2020 ident: ref_16 article-title: Advanced Fault Diagnosis for Lithium-Ion Battery Systems: A Review of Fault Mechanisms, Fault Features, and Diagnosis Procedures publication-title: IEEE Ind. Electron. Mag. doi: 10.1109/MIE.2020.2964814 – volume: 213 start-page: 375 year: 2018 ident: ref_47 article-title: Temperature rise prediction of lithium-ion battery suffering external short circuit for all-climate electric vehicles application publication-title: Appl. Energy doi: 10.1016/j.apenergy.2018.01.068 – volume: 52 start-page: 1759 year: 2016 ident: ref_28 article-title: A Data-Driven Bias-Correction-Method-Based Lithium-Ion Battery Modeling Approach for Electric Vehicle Applications publication-title: IEEE Trans. Ind. Appl. – ident: ref_20 doi: 10.1109/ITEC.2014.6861806 – ident: ref_27 doi: 10.3390/en9050387 – volume: 65 start-page: 6730 year: 2018 ident: ref_50 article-title: Long Short-Term Memory Networks for Accurate State-of-Charge Estimation of Li-ion Batteries publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2017.2787586 – ident: ref_24 doi: 10.3390/pr7010038 – volume: 148 start-page: A838 year: 2001 ident: ref_3 article-title: Abuse testing of lithium-ion batteries: Characterization of the overcharge reaction of LiCoO2 graphite cells publication-title: J. Electrochem. Soc. doi: 10.1149/1.1379740 – ident: ref_44 – volume: 55 start-page: 1922 year: 2019 ident: ref_45 article-title: A Compact Methodology Via a Recurrent Neural Network for Accurate Equivalent Circuit Type Modeling of Lithium-Ion Batteries publication-title: IEEE Trans. Ind. Appl. doi: 10.1109/TIA.2018.2874588 – ident: ref_34 doi: 10.1109/iSPEC48194.2019.8975122 – volume: 174 start-page: 33 year: 2019 ident: ref_30 article-title: Co-estimation of capacity and state-of-charge for lithium-ion batteries in electric vehicles publication-title: Energy doi: 10.1016/j.energy.2019.02.147 – volume: 259 start-page: 114170 year: 2020 ident: ref_39 article-title: Online multi-fault detection and diagnosis for battery packs in electric vehicles publication-title: Appl. Energy doi: 10.1016/j.apenergy.2019.114170 – ident: ref_33 doi: 10.20944/preprints201705.0116.v1 – volume: 55 start-page: 229 year: 2008 ident: ref_51 article-title: Soft computing for battery state-of-charge (BSOC)—Estimation in battery string systems publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2007.896496 – volume: 6 start-page: 1832 year: 2017 ident: ref_2 article-title: Critical Review on the Battery State of Charge Estimation Methods for Electric Vehicles publication-title: IEEE Access doi: 10.1109/ACCESS.2017.2780258 – volume: 37 start-page: 8513 year: 2022 ident: ref_35 article-title: Battery Thermal Runaway Fault Prognosis in Electric Vehicles Based on Abnormal Heat Generation and Deep Learning Algorithms publication-title: IEEE Trans. Power Electron. doi: 10.1109/TPEL.2022.3150026 – volume: 26 start-page: 1585 year: 2008 ident: ref_11 article-title: Effect of overdischarge on swelling and recharge performance of lithium ion cells publication-title: Chin. J. Chem. doi: 10.1002/cjoc.200890286 – volume: 115 start-page: 194 year: 2016 ident: ref_13 article-title: A 3D thermal runaway propagation model for a large format lithium-ion battery module publication-title: Energy doi: 10.1016/j.energy.2016.08.094 – ident: ref_19 – volume: 18 start-page: 636 year: 2010 ident: ref_23 article-title: A Survey of Fault Detection, Isolation, and Reconfiguration Methods publication-title: IEEE Trans. Control Syst. Technol. doi: 10.1109/TCST.2009.2026285
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SubjectTerms	Algorithms Anomalies Batteries Carbon Circuits Copper Decision making Decision theory Decision trees Electric potential Electric vehicles Electrodes Electrolytes Fault diagnosis Fuzzy logic Heat Lithium Parameters Predictions Product safety Traffic safety Voltage
Title	Prediction and Diagnosis of Electric Vehicle Battery Fault Based on Abnormal Voltage: Using Decision Tree Algorithm Theories and Isolated Forest
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