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An Online Data-Driven Model Identification and Adaptive State of Charge Estimation Approach for Lithium-ion-Batteries Using the Lagrange Multiplier Method

Author

Listed:
  • Muhammad Umair Ali

    (School of Electrical Engineering, Pusan National University, San 30, ChangJeon 2 Dong, KeumJeong-gu, Pusan 46241, Korea)

  • Muhammad Ahmad Kamran

    (Department of Cogno-Mechatronics Engineering, Pusan National University, San 30, ChangJeon 2 Dong, KeumJeong-gu, Pusan 46241, Korea)

  • Pandiyan Sathish Kumar

    (School of Electrical Engineering, Pusan National University, San 30, ChangJeon 2 Dong, KeumJeong-gu, Pusan 46241, Korea)

  • Himanshu

    (School of Electrical Engineering, Pusan National University, San 30, ChangJeon 2 Dong, KeumJeong-gu, Pusan 46241, Korea)

  • Sarvar Hussain Nengroo

    (School of Electrical Engineering, Pusan National University, San 30, ChangJeon 2 Dong, KeumJeong-gu, Pusan 46241, Korea)

  • Muhammad Adil Khan

    (Department of Electrical and Computer Engineering, Air University, Islamabad 44000, Pakistan)

  • Altaf Hussain

    (School of Electrical Engineering, Pusan National University, San 30, ChangJeon 2 Dong, KeumJeong-gu, Pusan 46241, Korea)

  • Hee-Je Kim

    (School of Electrical Engineering, Pusan National University, San 30, ChangJeon 2 Dong, KeumJeong-gu, Pusan 46241, Korea)

Abstract

Reliable and accurate state of charge (SOC) monitoring is the most crucial part in the design of an electric vehicle (EV) battery management system (BMS). The lithium ion battery (LIB) is a highly complex electrochemical system, which performance changes with age. Therefore, measuring the SOC of a battery is a very complex and tedious process. This paper presents an online data-driven battery model identification method, where the battery parameters are updated using the Lagrange multiplier method. A battery model with unknown battery parameters was formulated in such a way that the terminal voltage at an instant time step is a linear combination of the voltages and load current. A cost function was defined to determine the optimal values of the unknown parameters with different data points measured experimentally. The constraints were added in the modified cost function using Lagrange multiplier method and the optimal value of update vector was determined using the gradient approach. An adaptive open circuit voltage (OCV) and SOC estimator was designed for the LIB. The experimental results showed that the proposed estimator is quite accurate and robust. The proposed method effectively tracks the time-varying parameters of a battery with high accuracy. During the SOC estimation, the maximum noted error was 1.28%. The convergence speed of the proposed method was only 81 s with a deliberate 100% initial error. Owing to the high accuracy and robustness, the proposed method can be used in the design of a BMS for real time applications.

Suggested Citation

  • Muhammad Umair Ali & Muhammad Ahmad Kamran & Pandiyan Sathish Kumar & Himanshu & Sarvar Hussain Nengroo & Muhammad Adil Khan & Altaf Hussain & Hee-Je Kim, 2018. "An Online Data-Driven Model Identification and Adaptive State of Charge Estimation Approach for Lithium-ion-Batteries Using the Lagrange Multiplier Method," Energies, MDPI, vol. 11(11), pages 1-19, October.
    • Handle: RePEc:gam:jeners:v:11:y:2018:i:11:p:2940-:d:178776
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    References listed on IDEAS

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    Cited by:

    1. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Muhammad Junaid Alvi & Hee-Je Kim, 2019. "Towards a Smarter Battery Management System for Electric Vehicle Applications: A Critical Review of Lithium-Ion Battery State of Charge Estimation," Energies, MDPI, vol. 12(3), pages 1-33, January.
    2. Muhammad Umair Ali & Amad Zafar & Sarvar Hussain Nengroo & Sadam Hussain & Gwan-Soo Park & Hee-Je Kim, 2019. "Online Remaining Useful Life Prediction for Lithium-Ion Batteries Using Partial Discharge Data Features," Energies, MDPI, vol. 12(22), pages 1-14, November.
    3. Yue Zhou & Hussein Obeid & Salah Laghrouche & Mickael Hilairet & Abdesslem Djerdir, 2020. "A Disturbance Rejection Control Strategy of a Single Converter Hybrid Electrical System Integrating Battery Degradation," Energies, MDPI, vol. 13(11), pages 1-19, June.
    4. Gaizka Saldaña & José Ignacio San Martín & Inmaculada Zamora & Francisco Javier Asensio & Oier Oñederra, 2019. "Analysis of the Current Electric Battery Models for Electric Vehicle Simulation," Energies, MDPI, vol. 12(14), pages 1-27, July.
    5. Sadam Hussain & Muhammad Umair Ali & Gwan-Soo Park & Sarvar Hussain Nengroo & Muhammad Adil Khan & Hee-Je Kim, 2019. "A Real-Time Bi-Adaptive Controller-Based Energy Management System for Battery–Supercapacitor Hybrid Electric Vehicles," Energies, MDPI, vol. 12(24), pages 1-24, December.

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