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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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    6. Zhang, Xu & Wang, Yujie & Wu, Ji & Chen, Zonghai, 2018. "A novel method for lithium-ion battery state of energy and state of power estimation based on multi-time-scale filter," Applied Energy, Elsevier, vol. 216(C), pages 442-451.
    7. 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).
    8. Zhu, Xiaoqing & Wang, Zhenpo & Wang, Yituo & Wang, Hsin & Wang, Cong & Tong, Lei & Yi, Mi, 2019. "Overcharge investigation of large format lithium-ion pouch cells with Li(Ni0.6Co0.2Mn0.2)O2 cathode for electric vehicles: Thermal runaway features and safety management method," Energy, Elsevier, vol. 169(C), pages 868-880.
    9. 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).
    10. Wang, Limei & Jin, Mengjie & Cai, Yingfeng & Lian, Yubo & Zhao, Xiuliang & Wang, Ruochen & Qiao, Sibing & Chen, Long & Yan, Xueqing, 2023. "Construction of electrochemical model for high C-rate conditions in lithium-ion battery based on experimental analogy method," Energy, Elsevier, vol. 279(C).
    11. Yunfeng Jiang & Louis J. Shrinkle & Raymond A. de Callafon, 2019. "Autonomous Demand-Side Current Scheduling of Parallel Buck Regulated Battery Modules," Energies, MDPI, vol. 12(11), pages 1-20, May.
    12. 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.
    13. 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.
    14. 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).
    15. Wang, Bin & Ma, Guangliang & Xu, Dan & Zhang, Le & Zhou, Jiahui, 2018. "Switching sliding-mode control strategy based on multi-type restrictive condition for voltage control of buck converter in auxiliary energy source," Applied Energy, Elsevier, vol. 228(C), pages 1373-1384.
    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.
    17. Wang, Yujie & Zhang, Xingchen & Chen, Zonghai, 2022. "Low temperature preheating techniques for Lithium-ion batteries: Recent advances and future challenges," Applied Energy, Elsevier, vol. 313(C).

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