Novel strategy based on improved Kalman filter algorithm for state of health evaluation of hybrid electric vehicles Li-ion batteries during short- and longer term operating conditions-Reference-Cited by-同舟云学术

Novel strategy based on improved Kalman filter algorithm for state of health evaluation of hybrid electric vehicles Li-ion batteries during short- and longer term operating conditions

Published:2021-05-19 Issue: Volume: Page:
ISSN:1598-2092
Container-title:Journal of Power Electronics
language:en
Short-container-title:J. Power Electron.

Author:

Ren Pu,Wang Shunli^ORCID,He Mingfang,Cao Wen

Funder

National Natural Science Foundation of China

Sichuan science and technology program

China Scholarship Council

Fund of Robot Technology Used for Special Environment Key Laboratory of Sichuan Province

Natural Science Foundation of and Southwest University of Science and Technology

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Control and Systems Engineering

Link

https://link.springer.com/content/pdf/10.1007/s43236-021-00253-5.pdf

Reference35 articles.

1. Kawahara, Y., et al.: Development of status detection method of lithium-ion rechargeable battery for hybrid electric vehicles. J. Power Sources 481, 228760 (2021)

2. Cai, L., et al.: Multiobjective optimization of data-driven model for lithium-ion battery SOH estimation with short-term feature. IEEE Trans. Power Electron. 35(11), 11855–11864 (2020)

3. Dai, H., et al.: A novel estimation method for the state of health of lithium-ion battery using prior knowledge-based neural network and Markov chain. IEEE Trans. Ind. Electron. 66(10), 7706–7716 (2019)

4. Wang, Y., Chen, Z.: A framework for state-of-charge and remaining discharge time prediction using unscented particle filter. Appl. Energy 260, 114324 (2020)

5. Wei, Z., et al.: Future smart battery and management: advanced sensing from external to embedded multi-dimensional measurement. J. Power Sources 489, 229462 (2021)

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