# A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach

^{1}

^{2}

^{3}

^{*}

## Abstract

**:**

## 1. Introduction

- Accurate definition of knee points in the discharge curves i.e., initial voltage drop and threshold value.
- Implementation of a simplified machine learning model for SOH estimation. The proposed trained models have the ability to accurately predict and estimate results for different current.
- Estimating the SOC using the discharge curve of the battery.

## 2. Background on Battery Characteristics

## 3. Battery Modeling Approach

#### Mathematical Model of a Battery

- ${R}_{o}$ representing an ohmic resistance due to the conduction of charge carriers through electrolyte and metallic conduction.
- a series of parallel resistor and capacitor connections (i.e., activation polarization) representing the charge transfer resistance and double layer capacitance respectively.

## 4. Machine Learning for SOH and SOC

#### 4.1. Knee-Point Calculation

#### 4.2. Feature Engineering

#### 4.3. Data Preprocessing

#### 4.4. Machine Learning

#### 4.5. SOC Estimation

## 5. Results and Discussions

## 6. Conclusions

## Author Contributions

## Funding

## Acknowledgments

## Conflicts of Interest

## References

- Dunn, B.; Kamath, H.; Tarascon, J. Electrical Energy Storage for the Grid: A Battery of Choices. Science
**2011**, 334, 928–935. [Google Scholar] [CrossRef] [PubMed] [Green Version] - Hu, X.; Zou, C.; Zhang, C.; Li, Y. Technological Developments in Batteries: A Survey of Principal Roles, Types, and Management Needs. IEEE Power Energy Mag.
**2017**, 15, 20–31. [Google Scholar] [CrossRef] - Kim, T.; Song, W.; Son, D.; Ono, L.; Qi, Y. Lithium-ion batteries: Outlook on present, future, and hybridized technologies. J. Mater. Chem.
**2019**, 7, 2942–2964. [Google Scholar] [CrossRef] - Stewart, P.; Bingham, C. Electrical Power and Energy Systems for Transportation Applications. Energies
**2016**, 9, 545. [Google Scholar] [CrossRef] [Green Version] - Kim, Y.; Hwang, C.; Kim, E.; Cho, C. State of Charge-Based Active Power Sharing Method in a Standalone Microgrid with High Penetration Level of Renewable Energy Sources. Energies
**2016**, 9, 480. [Google Scholar] [CrossRef] - Ren, H.; Zhao, Y.; Chen, S.; Wang, T. Design and implementation of a battery management system with active charge balance based on the SOC and SOH online estimation. Energy
**2019**, 166, 908–917. [Google Scholar] [CrossRef] - Tang, X.; Wang, Y.; Zou, C.; Yao, K.; Xia, Y.; Gao, F. A novel framework for Lithium-ion battery modeling considering uncertainties of temperature and aging. Energy Convers. Manag.
**2019**, 180, 162–170. [Google Scholar] [CrossRef] - Wei, Z.; Leng, F.; He, Z.; Zhang, W.; Li, K. Online State of Charge and State of Health Estimation for a Lithium-Ion Battery Based on a Data–Model Fusion Method. Energies
**2018**, 11, 1810. [Google Scholar] [CrossRef] [Green Version] - Zhang, M.; Fan, X. Review on the State of Charge Estimation Methods for Electric Vehicle Battery. World Electr. Veh. J.
**2020**, 11, 23. [Google Scholar] [CrossRef] [Green Version] - He, H.; Xiong, R.; Fan, J. Evaluation of Lithium-Ion Battery Equivalent Circuit Models for State of Charge Estimation by an Experimental Approach. Energies
**2011**, 4, 582–598. [Google Scholar] [CrossRef] - Watrin, N.; Blunier, B.; Miraoui, A. Review of adaptive systems for lithium batteries State-of-Charge and State-of-Health estimation. In Proceedings of the 2012 IEEE Transportation Electrification Conference and Expo (ITEC), Dearborn, MI, USA, 18–20 June 2012. [Google Scholar]
- Li, C.; Xiao, F.; Fan, Y. An Approach to State of Charge Estimation of Lithium-Ion Batteries Based on Recurrent Neural Networks with Gated Recurrent Unit. Energies
**2019**, 5, 1592. [Google Scholar] [CrossRef] [Green Version] - Xing, Y.; He, W.; Pecht, M.; Tsui, K.L. State of charge estimation of lithium-ion batteries using the open-circuit voltage at various ambient temperatures. Appl. Energy
**2014**, 113, 106–115. [Google Scholar] [CrossRef] - Ng, K.S.; Moo, C.S.; Chen, Y.P.; Hsieh, Y.C. Enhanced coulomb counting method for estimating state-of-charge and state-of-health of lithium-ion batteries. Appl. Energy
**2009**, 86, 1506–1511. [Google Scholar] [CrossRef] - Andre, D.; Meiler, M.; Steiner, K.; Walz, H.; Soczka-Guth, T.; Sauer, D.U. Characterization of high-power lithium-ion batteries by electrochemical impedance spectroscopy. II: Modelling. J. Power Sources
**2011**, 196, 5349–5356. [Google Scholar] [CrossRef] - Roscher, M.A.; Sauer, D.U. Dynamic electric behavior and open-circuit-voltage modeling of LiFePO4-based lithium ion secondary batteries. J. Power Sources
**2011**, 196, 331–336. [Google Scholar] [CrossRef] - Khalid, M.; Sheikh, S.S.; Janjua, A.K.; Khalid, H.A. Performance validation of electric vehicle’s battery management system under state of charge estimation for lithium-ion battery. In Proceedings of the 2018 International Conference on Computing, Electronic and Electrical Engineering (ICE Cube), Quetta, Pakistan, 12–13 November 2018; pp. 1–5. [Google Scholar] [CrossRef]
- Xiong, R.; Sun, F.C.; He, H.W. Data-driven State-of-Charge estimator for electric vehicles battery using robust extended Kalman filter. Int. J. Automot. Technol.
**2014**, 15, 89–96. [Google Scholar] [CrossRef] - Lee, J.; Nam, O.; Cho, B.H. Li-ion battery SOC estimation method based on the reduced order extended Kalman filtering. J. Power Sources
**2007**, 174, 9–15. [Google Scholar] [CrossRef] - Talha, M.; Asghar, F.; Kim, S.H. A Neural Network-Based Robust Online SOC and SOH Estimation for Sealed Lead–Acid Batteries in Renewable Systems. Arab. J. Sci. Eng.
**2018**, 44, 1869–1881. [Google Scholar] [CrossRef] - Anton, J.C.A.; Nieto, P.J.G.; Viejo, C.B.; Vilán, J.A.V. Support Vector Machines Used to Estimate the Battery State of Charge. IEEE Trans. Power Electron.
**2013**, 28, 5919–5926. [Google Scholar] [CrossRef] - Huang, S.C.; Tseng, K.H.; Liang, J.W.; Chang, C.L.; Pecht, M.G. An Online SOC and SOH Estimation Model for Lithium-Ion Batteries. Energies
**2017**, 10, 512. [Google Scholar] [CrossRef] - Vignarooban, K.; Chu, X.; Chimatapu, K.; Ganeshram, P.; Pollat, S.; Johnson, N.G.; Kannan, A.M. State of health determination of sealed lead acid batteries under various operating conditions. Sustain. Energy Technol. Assess.
**2016**, 18, 134–139. [Google Scholar] [CrossRef] - Ma, Z.; Yang, R.; Wang, Z. A novel data-model fusion state-of-health estimation approach for lithium-ion batteries. Appl. Energy
**2019**, 237, 836–847. [Google Scholar] [CrossRef] - Fang, Q.; Wei, X.; Lu, T.; Dai, H.; Zhu, J. A State of Health Estimation Method for Lithium-Ion Batteries Based on Voltage Relaxation Model. Energies
**2019**, 12, 1349. [Google Scholar] [CrossRef] [Green Version] - Chen, Z.; Mi, C.C.; Fu, Y.; Xu, J.; Gong, X. Online battery state of health estimation based on Genetic Algorithm for electric and hybrid vehicle applications. J. Power Sources
**2013**, 240, 184–192. [Google Scholar] [CrossRef] - Coleman, M.; Lee, C.K.; Zhu, C.; Hurley, W.G. State-of-Charge Determination From EMF Voltage Estimation: Using Impedance, Terminal Voltage, and Current for Lead-Acid and Lithium-Ion Batteries. IEEE Trans. Ind. Electron.
**2007**, 54, 2550–2557. [Google Scholar] [CrossRef] - Saxena, A.; Celaya, J.R.; Roychoudhury, I.; Saha, S.; Saha, B.; Goebel, K. Designing data-driven battery prognostic approaches for variable loading profiles: Some lessons learned. In Proceedings of the First European Conference of Prognostics and Health Management Society, PHM 2012, Dresden, Germany, 3–5 July 2012; pp. 72–732. [Google Scholar]
- Zhang, Y.; Shang, Y.; Cui, N.; Zhang, C. Parameters Identification and Sensitive Characteristics Analysis for Lithium-Ion Batteries of Electric Vehicles. Energies
**2017**, 11, 19. [Google Scholar] [CrossRef] [Green Version] - McKissock, B.; Loyselle, P.; Vogel, E. Guidelines on Lithium-ion Battery Use in Space Applications. NASA, National Aeronautics and Space Administration. 2009. Available online: Https://ntrs.nasa.gov/archive/nasa/casi.ntrs.nasa.gov/20090023862.pdf (accessed on 12 May 2020).
- Lee, K.T.; Dai, M.J.; Chuang, C.C. Temperature-Compensated Model for Lithium-Ion Polymer Batteries with Extended Kalman Filter State-of-Charge Estimation for an Implantable Charger. IEEE Trans. Ind. Electron.
**2018**, 65, 589–596. [Google Scholar] [CrossRef] - Rücker, F.; Bremer, I.; Linden, S.; Badeda, J.; Sauer, D.U. Development and Evaluation of a Battery Lifetime Extending Charging Algorithm for an Electric Vehicle Fleet. Energy Procedia
**2016**, 99, 285–291. [Google Scholar] [CrossRef] [Green Version] - Gao, L.; Liu, S.; Dougal, R.A. Dynamic lithium-ion battery model for system simulation. IEEE Trans. Components Packag. Technol.
**2002**, 25, 495–505. [Google Scholar] [CrossRef] [Green Version] - Fleischer, C.; Waag, W.; Heyn, H.M.; Sauer, D.U. On-line adaptive battery impedance parameter and state estimation considering physical principles in reduced order equivalent circuit battery models part 2. Parameter and state estimation. J. Power Sources
**2014**, 262, 457–482. [Google Scholar] [CrossRef] - Zhang, X.; Zhang, W.; Lei, G.A. A Review of Li-ion Battery Equivalent Circuit Models. Trans. Electr. Electron. Mater.
**2016**, 17, 311–316. [Google Scholar] [CrossRef] [Green Version] - Alavi, S.M.; Mahdi, A.; Jacob, P.E.; Payne, S.J.; Howey, D.A. Structural identifiability analysis of fractional order models with applications in battery systems. arXiv
**2015**, arXiv:1511.01402. [Google Scholar] - Mihajlovic, V.; Grundlehner, B.; Vullers, R.; Penders, J. Wearable, Wireless EEG Solutions in Daily Life Applications: What are we Missing? IEEE J. Biomed. Health Inform.
**2015**, 19, 6–21. [Google Scholar] [CrossRef] [PubMed] - Macdonald, J.R.; Johnson, W.B. Chapter 1—Fundamentals of impedance spectroscopy. In Impedance Spectroscopy: Theory, Experiment, and Applications; John Wiley and Sons, Inc.: New Jersey, NJ, USA, 2018; pp. 1–20. [Google Scholar] [CrossRef]
- Buller, S.; Thele, M.; De Doncker, R.W.; Karden, E. Impedance-Based Simulation Models of Supercapacitors and Li-Ion Batteries for Power Electronic Applications. IEEE Trans. Ind. Appl.
**2005**, 41, 742–747. [Google Scholar] [CrossRef] - Westerhoff, U.; Kroker, T.; Kurbach, K.; Kurrat, M. Electrochemical impedance spectroscopy based estimation of the state of charge of lithium-ion batteries. J. Energy Storage
**2016**, 8, 244–256. [Google Scholar] [CrossRef] - NASA-Battery Data-Set, National Aeronautics and Space Administration. Available online: Https://ti.arc.nasa.gov/tech/dash/groups/pcoe/prognostic-data-repository/battery (accessed on 12 May 2020).
- Curve Knee Estimation, IBM-Wattson. Available online: Https://dataplatform.cloud.ibm.com/analytics/notebooks/5\4d79c2a-f155-40ec-93ec-ed05b58afa39/ (accessed on 12 May 2020).

**Figure 2.**Discharge curves for different current, (i.e., $1.0$ A and $2.0$ A) and for different temperatures ($297.15$ K and $277.15$ K).

**Figure 5.**Relationship between ${d}_{CD}$ and the other two features such as ${d}_{AC}$ and ${d}_{DB}$ for one of the batteries.

**Figure 6.**Contour plot showing the effect of cycle number on Arm Length and Pseudo Linear Region at $297.15$ K.

**Figure 7.**Contour plot showing the effect of cycle number on Arm Length and Pseudo Linear Region at $297.15$ K.

**Figure 8.**Contour plot showing the effect of cycle number on Arm Length and Pseudo Linear Region at $297.15$ K.

**Figure 9.**Contour plot showing the effect of cycle number on Arm Length and Pseudo Linear Region at $277.15$ K.

**Figure 10.**Contour plot showing the effect of cycle number on Arm Length and Pseudo Linear Region at $277.15$ K.

**Figure 11.**Contour plot showing the effect of cycle number on Arm Length and Pseudo Linear Region at $277.15$ K.

S.No | Battery | Current | Temperature |
---|---|---|---|

1 | B0005 | $2.0$ A | $297.15$ K |

2 | B0006 | $2.0$ A | $297.15$ K |

3 | B0007 | $2.0$ A | $297.15$ K |

4 | B0045 | $1.0$ A | $277.15$ K |

5 | B0046 | $1.0$ A | $277.15$ K |

6 | B0047 | $1.0$ A | $277.15$ K |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Sheikh, S.S.; Anjum, M.; Khan, M.A.; Hassan, S.A.; Khalid, H.A.; Gastli, A.; Ben-Brahim, L.
A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach. *Energies* **2020**, *13*, 3658.
https://doi.org/10.3390/en13143658

**AMA Style**

Sheikh SS, Anjum M, Khan MA, Hassan SA, Khalid HA, Gastli A, Ben-Brahim L.
A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach. *Energies*. 2020; 13(14):3658.
https://doi.org/10.3390/en13143658

**Chicago/Turabian Style**

Sheikh, Shehzar Shahzad, Mahnoor Anjum, Muhammad Abdullah Khan, Syed Ali Hassan, Hassan Abdullah Khalid, Adel Gastli, and Lazhar Ben-Brahim.
2020. "A Battery Health Monitoring Method Using Machine Learning: A Data-Driven Approach" *Energies* 13, no. 14: 3658.
https://doi.org/10.3390/en13143658