State of Charge Estimation in Lithium-Ion Batteries: A Neural Network Optimization Approach
The development of an accurate and robust state-of-charge (SOC) estimation is crucial for the battery lifetime, efficiency, charge control, and safe driving of electric vehicles (EV). This paper proposes an enhanced data-driven method based on a time-delay neural network (TDNN) algorithm for state o...
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| Published in | Electronics (Basel) Vol. 9; no. 9; p. 1546 |
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| Main Authors | , , , , , |
| Format | Journal Article |
| Language | English |
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01.09.2020
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| Abstract | The development of an accurate and robust state-of-charge (SOC) estimation is crucial for the battery lifetime, efficiency, charge control, and safe driving of electric vehicles (EV). This paper proposes an enhanced data-driven method based on a time-delay neural network (TDNN) algorithm for state of charge (SOC) estimation in lithium-ion batteries. Nevertheless, SOC accuracy is subject to the suitable value of the hyperparameters selection of the TDNN algorithm. Hence, the TDNN algorithm is optimized by the improved firefly algorithm (iFA) to determine the optimal number of input time delay (UTD) and hidden neurons (HNs). This work investigates the performance of lithium nickel manganese cobalt oxide (LiNiMnCoO2) and lithium nickel cobalt aluminum oxide (LiNiCoAlO2) toward SOC estimation under two experimental test conditions: the static discharge test (SDT) and hybrid pulse power characterization (HPPC) test. Also, the accuracy of the proposed method is evaluated under different EV drive cycles and temperature settings. The results show that iFA-based TDNN achieves precise SOC estimation results with a root mean square error (RMSE) below 1%. Besides, the effectiveness and robustness of the proposed approach are validated against uncertainties including noise impacts and aging influences. |
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| AbstractList | The development of an accurate and robust state-of-charge (SOC) estimation is crucial for the battery lifetime, efficiency, charge control, and safe driving of electric vehicles (EV). This paper proposes an enhanced data-driven method based on a time-delay neural network (TDNN) algorithm for state of charge (SOC) estimation in lithium-ion batteries. Nevertheless, SOC accuracy is subject to the suitable value of the hyperparameters selection of the TDNN algorithm. Hence, the TDNN algorithm is optimized by the improved firefly algorithm (iFA) to determine the optimal number of input time delay (UTD) and hidden neurons (HNs). This work investigates the performance of lithium nickel manganese cobalt oxide (LiNiMnCoO2) and lithium nickel cobalt aluminum oxide (LiNiCoAlO2) toward SOC estimation under two experimental test conditions: the static discharge test (SDT) and hybrid pulse power characterization (HPPC) test. Also, the accuracy of the proposed method is evaluated under different EV drive cycles and temperature settings. The results show that iFA-based TDNN achieves precise SOC estimation results with a root mean square error (RMSE) below 1%. Besides, the effectiveness and robustness of the proposed approach are validated against uncertainties including noise impacts and aging influences. |
| Author | Saad, Mohamad H. M. Hannan, M. A. Hussain, Aini Ayob, Afida Muttaqi, Kashem M. Hossain Lipu, M. S. |
| Author_xml | – sequence: 1 givenname: M. S. orcidid: 0000-0001-9060-4454 surname: Hossain Lipu fullname: Hossain Lipu, M. S. – sequence: 2 givenname: M. A. orcidid: 0000-0001-8367-4112 surname: Hannan fullname: Hannan, M. A. – sequence: 3 givenname: Aini orcidid: 0000-0001-7347-7879 surname: Hussain fullname: Hussain, Aini – sequence: 4 givenname: Afida orcidid: 0000-0002-7112-5148 surname: Ayob fullname: Ayob, Afida – sequence: 5 givenname: Mohamad H. M. surname: Saad fullname: Saad, Mohamad H. M. – sequence: 6 givenname: Kashem M. surname: Muttaqi fullname: Muttaqi, Kashem M. |
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| Cites_doi | 10.3390/electronics8111324 10.1016/j.apenergy.2017.07.056 10.3390/en10050679 10.3390/en13102638 10.1016/j.jpowsour.2018.07.116 10.1109/72.329697 10.1109/TIE.2017.2701766 10.3390/math7100959 10.1109/ACCESS.2018.2837156 10.1016/j.conengprac.2016.05.014 10.3390/en13112825 10.1038/s41598-020-66424-9 10.1016/j.jpowsour.2013.08.039 10.3390/electronics7110321 10.1016/j.est.2016.03.003 10.1016/j.apenergy.2016.08.016 10.1016/j.apenergy.2012.08.031 10.1016/j.jocs.2017.07.009 10.3390/en12030446 10.1109/TII.2019.2941747 10.1016/j.ijepes.2014.04.059 10.1016/j.asoc.2020.106438 10.1109/ACCESS.2019.2913078 10.1016/j.apenergy.2013.07.008 10.1016/j.epsr.2017.01.032 10.1016/j.apenergy.2015.11.072 10.1016/j.energy.2019.116538 10.1016/j.jpowsour.2013.09.135 10.1109/ACCESS.2018.2797976 10.1038/s41598-020-61464-7 10.3390/en11071820 10.3390/en11040995 10.1016/j.apenergy.2014.08.081 10.1109/TIE.2017.2733475 10.3390/en13133306 10.3390/electronics6040102 10.3390/en10070987 10.1109/TII.2017.2777460 10.1109/ACCESS.2018.2888881 10.3390/en10091313 10.1049/iet-pel.2013.0746 10.3390/en13133307 10.1016/j.rser.2019.109334 10.1109/TIA.2019.2902532 |
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| References | Huang (ref_3) 2018; 399 Naha (ref_37) 2020; 10 Xing (ref_38) 2014; 113 He (ref_17) 2014; 62 Lipu (ref_19) 2019; 55 ref_36 ref_11 ref_10 Xu (ref_32) 2019; 7 Du (ref_15) 2016; 54 Hannan (ref_35) 2020; 10 Liu (ref_14) 2017; 64 Hannan (ref_28) 2018; 6 ref_16 Lipu (ref_22) 2018; 6 Awadallah (ref_25) 2016; 6 Wu (ref_39) 2017; 10 Liu (ref_21) 2020; 16 Hagan (ref_29) 1994; 5 Chaoui (ref_26) 2017; 146 Chen (ref_44) 2014; 246 Xiao (ref_24) 2019; 7 Xiong (ref_13) 2018; 65 Charkhgard (ref_18) 2014; 7 Duong (ref_43) 2017; 204 Hu (ref_8) 2019; 114 Zhang (ref_9) 2014; 248 ref_20 Yang (ref_12) 2016; 164 ref_41 Wang (ref_34) 2014; 135 ref_40 ref_1 ref_2 ref_27 Lv (ref_30) 2018; 14 Hussain (ref_7) 2019; 12 Xia (ref_31) 2018; 26 He (ref_42) 2013; 101 Ball (ref_33) 2020; 94 Zheng (ref_45) 2016; 180 ref_5 Bian (ref_23) 2020; 191 ref_4 ref_6 |
| References_xml | – ident: ref_10 doi: 10.3390/electronics8111324 – volume: 204 start-page: 560 year: 2017 ident: ref_43 article-title: Accurate approach to the temperature effect on state of charge estimation in the LiFePO4 battery under dynamic load operation publication-title: Appl. Energy doi: 10.1016/j.apenergy.2017.07.056 – ident: ref_41 doi: 10.3390/en10050679 – ident: ref_2 doi: 10.3390/en13102638 – volume: 399 start-page: 274 year: 2018 ident: ref_3 article-title: Recycling of lithium-ion batteries: Recent advances and perspectives publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2018.07.116 – volume: 5 start-page: 989 year: 1994 ident: ref_29 article-title: Training feedforward networks with the Marquardt algorithm publication-title: IEEE Trans. Neural Netw. doi: 10.1109/72.329697 – volume: 64 start-page: 8128 year: 2017 ident: ref_14 article-title: A State of Charge Estimation Method Based on H∞ Observer for Switched Systems of Lithium-Ion Nickel-Manganese-Cobalt Batteries publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2017.2701766 – ident: ref_27 doi: 10.3390/math7100959 – volume: 6 start-page: 28150 year: 2018 ident: ref_22 article-title: State of Charge Estimation for Lithium-ion Battery Using Recurrent NARX Neural Network Model Based Lighting Search Algorithm publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2837156 – volume: 54 start-page: 81 year: 2016 ident: ref_15 article-title: An adaptive sliding mode observer for lithium-ion battery state of charge and state of health estimation in electric vehicles publication-title: Control Eng. Pract. doi: 10.1016/j.conengprac.2016.05.014 – ident: ref_5 doi: 10.3390/en13112825 – volume: 10 start-page: 9526 year: 2020 ident: ref_37 article-title: An Incremental Voltage Difference Based Technique for Online State of Health Estimation of Li-ion Batteries publication-title: Sci. Rep. doi: 10.1038/s41598-020-66424-9 – volume: 246 start-page: 667 year: 2014 ident: ref_44 article-title: A novel approach for state of charge estimation based on adaptive switching gain sliding mode observer in electric vehicles publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2013.08.039 – ident: ref_11 doi: 10.3390/electronics7110321 – volume: 6 start-page: 95 year: 2016 ident: ref_25 article-title: Accuracy improvement of SOC estimation in lithium-ion batteries publication-title: J. Energy Storage doi: 10.1016/j.est.2016.03.003 – volume: 180 start-page: 424 year: 2016 ident: ref_45 article-title: Co-estimation of state-of-charge, capacity and resistance for lithium-ion batteries based on a high-fidelity electrochemical model publication-title: Appl. Energy doi: 10.1016/j.apenergy.2016.08.016 – volume: 101 start-page: 808 year: 2013 ident: ref_42 article-title: A new model for State-of-Charge (SOC) estimation for high-power Li-ion batteries publication-title: Appl. Energy doi: 10.1016/j.apenergy.2012.08.031 – volume: 26 start-page: 488 year: 2018 ident: ref_31 article-title: A hybrid optimizer based on firefly algorithm and particle swarm optimization algorithm publication-title: J. Comput. Sci. doi: 10.1016/j.jocs.2017.07.009 – volume: 12 start-page: 446 year: 2019 ident: ref_7 article-title: Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation publication-title: Energies doi: 10.3390/en12030446 – volume: 16 start-page: 3767 year: 2020 ident: ref_21 article-title: Gaussian Process Regression with Automatic Relevance Determination Kernel for Calendar Aging Prediction of Lithium-Ion Batteries publication-title: IEEE Trans. Ind. Inform. doi: 10.1109/TII.2019.2941747 – volume: 62 start-page: 783 year: 2014 ident: ref_17 article-title: State of charge estimation for Li-ion batteries using neural network modeling and unscented Kalman filter-based error cancellation publication-title: Int. J. Electr. Power Energy Syst. doi: 10.1016/j.ijepes.2014.04.059 – volume: 94 start-page: 106438 year: 2020 ident: ref_33 article-title: Optimization of drop ejection frequency in EHD inkjet printing system using an improved Firefly Algorithm publication-title: Appl. Soft Comput. J. doi: 10.1016/j.asoc.2020.106438 – volume: 7 start-page: 54192 year: 2019 ident: ref_24 article-title: Accurate state-of-charge estimation approach for lithium-ion batteries by gated recurrent unit with ensemble optimizer publication-title: IEEE Access doi: 10.1109/ACCESS.2019.2913078 – volume: 113 start-page: 106 year: 2014 ident: ref_38 article-title: State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures publication-title: Appl. Energy doi: 10.1016/j.apenergy.2013.07.008 – volume: 146 start-page: 189 year: 2017 ident: ref_26 article-title: Aging prediction and state of charge estimation of a LiFePO4 battery using input time-delayed neural networks publication-title: Electr. Power Syst. Res. doi: 10.1016/j.epsr.2017.01.032 – volume: 164 start-page: 387 year: 2016 ident: ref_12 article-title: A comparative study of three model-based algorithms for estimating state-of-charge of lithium-ion batteries under a new combined dynamic loading profile publication-title: Appl. Energy doi: 10.1016/j.apenergy.2015.11.072 – volume: 191 start-page: 116538 year: 2020 ident: ref_23 article-title: Stacked bidirectional long short-term memory networks for state-of-charge estimation of lithium-ion batteries publication-title: Energy doi: 10.1016/j.energy.2019.116538 – volume: 248 start-page: 1028 year: 2014 ident: ref_9 article-title: A novel model of the initial state of charge estimation for LiFePO 4 batteries publication-title: J. Power Sources doi: 10.1016/j.jpowsour.2013.09.135 – volume: 6 start-page: 10069 year: 2018 ident: ref_28 article-title: Neural Network Approach for Estimating State of Charge of Lithium-ion Battery Using Backtracking Search Algorithm publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2797976 – volume: 10 start-page: 4687 year: 2020 ident: ref_35 article-title: Toward Enhanced State of Charge Estimation of Lithium-ion Batteries Using Optimized Machine Learning Techniques publication-title: Sci. Rep. doi: 10.1038/s41598-020-61464-7 – ident: ref_6 doi: 10.3390/en11071820 – ident: ref_20 doi: 10.3390/en11040995 – volume: 135 start-page: 81 year: 2014 ident: ref_34 article-title: A method for joint estimation of state-of-charge and available energy of LiFePO4batteries publication-title: Appl. Energy doi: 10.1016/j.apenergy.2014.08.081 – volume: 65 start-page: 1526 year: 2018 ident: ref_13 article-title: A Double-Scale, Particle-Filtering, Energy State Prediction Algorithm for Lithium-Ion Batteries publication-title: IEEE Trans. Ind. Electron. doi: 10.1109/TIE.2017.2733475 – ident: ref_4 doi: 10.3390/en13133306 – ident: ref_16 doi: 10.3390/electronics6040102 – volume: 10 start-page: 987 year: 2017 ident: ref_39 article-title: State-of-Charge Estimation with State-of-Health Calibration for Lithium-Ion Batteries publication-title: Energies doi: 10.3390/en10070987 – volume: 14 start-page: 3436 year: 2018 ident: ref_30 article-title: Levenberg–Marquardt Backpropagation Training of Multilayer Neural Networks for State Estimation of a Safety-Critical Cyber-Physical System publication-title: IEEE Trans. Ind. Inform. doi: 10.1109/TII.2017.2777460 – ident: ref_36 – volume: 7 start-page: 9205 year: 2019 ident: ref_32 article-title: Collision-Free Fuzzy Formation Control of Swarm Robotic Cyber-Physical Systems Using a Robust Orthogonal Firefly Algorithm publication-title: IEEE Access doi: 10.1109/ACCESS.2018.2888881 – ident: ref_40 doi: 10.3390/en10091313 – volume: 7 start-page: 2758 year: 2014 ident: ref_18 article-title: Hybrid state of charge estimation for lithium-ion batteries: Design and implementation publication-title: IET Power Electron. doi: 10.1049/iet-pel.2013.0746 – ident: ref_1 doi: 10.3390/en13133307 – volume: 114 start-page: 109334 year: 2019 ident: ref_8 article-title: State estimation for advanced battery management: Key challenges and future trends publication-title: Renew. Sustain. Energy Rev. doi: 10.1016/j.rser.2019.109334 – volume: 55 start-page: 4225 year: 2019 ident: ref_19 article-title: Extreme Learning Machine Model for State of Charge Estimation of Lithium-ion battery Using Gravitational Search Algorithm publication-title: IEEE Trans. Ind. Appl. doi: 10.1109/TIA.2019.2902532 |
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| SubjectTerms | Accuracy Aging Algorithms Aluminum oxide Back propagation Bias Charge efficiency Climate change Cobalt oxides Electric vehicles Fault diagnosis Heuristic methods Lithium Lithium-ion batteries Manganese Network management systems Neural networks Nickel Noise Optimization Optimization techniques Rechargeable batteries Root-mean-square errors State of charge Time lag |
| Title | State of Charge Estimation in Lithium-Ion Batteries: A Neural Network Optimization Approach |
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