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On-line remaining energy prediction: A case study in embedded battery management system

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  • Wang, Yujie
  • Chen, Zonghai
  • Zhang, Chenbin

Abstract

Modern electric vehicles (EVs) and hybrid electric vehicles (HEVs) require a reliable battery management system (BMS). The remaining energy and the state-of-energy (SoE) are very important indexes for the embedded BMS used in both EV and HEV applications. As a case study in the embedded BMS, this paper presents the implementation of remaining energy prediction based on the μC/OS-II real time operating system (RTOS). In considering that there are accumulated errors caused by inevitable drift noise of the current or voltage sensors, a model based SoE estimator is developed based on a first-order RC equivalent circuit model. Moreover, the Bayesian learning technique is used for SoE estimation to get accurate and robustness estimation results. Lastly, two different kinds of batteries are carried out under laboratory experiments and real road test to verify the robustness of the proposed SoE estimation approach. The results indicate that the maximum absolute estimation error (MAEE) and the root-mean square error (RMSE) are within 2% and 1% for both LiFePO4 and Li(Ni1/3Co1/3Mn1/3)O2 batteries.

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  • Wang, Yujie & Chen, Zonghai & Zhang, Chenbin, 2017. "On-line remaining energy prediction: A case study in embedded battery management system," Applied Energy, Elsevier, vol. 194(C), pages 688-695.
    • Handle: RePEc:eee:appene:v:194:y:2017:i:c:p:688-695
    DOI: 10.1016/j.apenergy.2016.05.081
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    Cited by:

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    5. Gao, Tianhan & Lu, Wei, 2024. "Reduced-order electrochemical models with shape functions for fast, accurate prediction of lithium-ion batteries under high C-rates," Applied Energy, Elsevier, vol. 353(PA).
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    8. Ingvild B. Espedal & Asanthi Jinasena & Odne S. Burheim & Jacob J. Lamb, 2021. "Current Trends for State-of-Charge (SoC) Estimation in Lithium-Ion Battery Electric Vehicles," Energies, MDPI, vol. 14(11), pages 1-24, June.
    9. Allafi, Walid & Uddin, Kotub & Zhang, Cheng & Mazuir Raja Ahsan Sha, Raja & Marco, James, 2017. "On-line scheme for parameter estimation of nonlinear lithium ion battery equivalent circuit models using the simplified refined instrumental variable method for a modified Wiener continuous-time model," Applied Energy, Elsevier, vol. 204(C), pages 497-508.
    10. Sun, Tao & Xu, Yuwen & Feng, Lihong & Xu, Bowen & Chen, Dizuo & Zhang, Fang & Han, Xuebing & Zhao, Lihui & Zheng, Yuejiu, 2022. "A vehicle-cloud collaboration strategy for remaining driving range estimation based on online traffic route information and future operation condition prediction," Energy, Elsevier, vol. 248(C).
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    13. Neha Bhushan & Saad Mekhilef & Kok Soon Tey & Mohamed Shaaban & Mehdi Seyedmahmoudian & Alex Stojcevski, 2022. "Overview of Model- and Non-Model-Based Online Battery Management Systems for Electric Vehicle Applications: A Comprehensive Review of Experimental and Simulation Studies," Sustainability, MDPI, vol. 14(23), pages 1-31, November.
    14. Xiaoli Sun & Zhengguo Li & Xiaolin Wang & Chengjiang Li, 2019. "Technology Development of Electric Vehicles: A Review," Energies, MDPI, vol. 13(1), pages 1-29, December.
    15. Wang, Yujie & Tian, Jiaqiang & Sun, Zhendong & Wang, Li & Xu, Ruilong & Li, Mince & Chen, Zonghai, 2020. "A comprehensive review of battery modeling and state estimation approaches for advanced battery management systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
    16. Jian Yang & Jaewook Jung & Samira Ghorbanpour & Sekyung Han, 2022. "Data–Driven Fault Diagnosis and Cause Analysis of Battery Pack with Real Data," Energies, MDPI, vol. 15(5), pages 1-19, February.
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