Large‐signal stability analysis for hybridized vehicular power supply systems of rolling stocks

Abstract Hybridized vehicular power supply (HVPS) are acquiring widespread adoption in various applications such as providing power for passenger or heavy‐duty electric vehicles, more‐electric ships, electrified rolling stocks, and more. Stability analysis is a critical issue for HVPS, but it presents significant challenges due to their prominent high‐dimensional and nonlinear characteristics. This paper presents a large‐signal stability analysis scheme for HVPS of rolling stocks, enabling the assessment of system stability under sudden load changes. Based on the state‐space averaging method, a mathematical model for the large‐signal behavior of the system is established. Subsequently, the small‐signal stability parameter range of the system is determined by analyzing the eigenvalues of the Jacobian matrix. To further analyze the stability of the system during large‐signal disturbances, a nonlinear decoupling transformation approach is employed to decompose the original high‐order state equations into multiple lower‐order equations. For the decoupled lower‐order subsystems, their stability is analyzed using phase portrait, and the region of attraction (ROA) is determined using the inverse trajectory method. Based on the stability analysis results, the controller is enhanced to bring the points outside the ROA back within it, thereby mitigating the transient instability phenomenon of the system. The effectiveness of the proposed method was demonstrated using two case studies.

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Publication Details

Journal
International Journal of Circuit Theory and Applications
Published
2024-09-10
DOI
https://doi.org/10.1002/cta.4244
Primary Topic
Electric and Hybrid Vehicle Technologies
Type
article
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article

Large‐signal stability analysis for hybridized vehicular power supply systems of rolling stocks

Zheming Jin, Haoying Pei, Lijun Diao, Jia Zhang
International Journal of Circuit Theory and Applications
Electric and Hybrid Vehicle Technologies
article

Large‐signal stability analysis for hybridized vehicular power supply systems of rolling stocks

Zheming Jin, Haoying Pei, Lijun Diao, Jia Zhang
article en

Abstract

Abstract Hybridized vehicular power supply (HVPS) are acquiring widespread adoption in various applications such as providing power for passenger or heavy‐duty electric vehicles, more‐electric ships, electrified rolling stocks, and more. Stability analysis is a critical issue for HVPS, but it presents significant challenges due to their prominent high‐dimensional and nonlinear characteristics. This paper presents a large‐signal stability analysis scheme for HVPS of rolling stocks, enabling the assessment of system stability under sudden load changes. Based on the state‐space averaging method, a mathematical model for the large‐signal behavior of the system is established. Subsequently, the small‐signal stability parameter range of the system is determined by analyzing the eigenvalues of the Jacobian matrix. To further analyze the stability of the system during large‐signal disturbances, a nonlinear decoupling transformation approach is employed to decompose the original high‐order state equations into multiple lower‐order equations. For the decoupled lower‐order subsystems, their stability is analyzed using phase portrait, and the region of attraction (ROA) is determined using the inverse trajectory method. Based on the stability analysis results, the controller is enhanced to bring the points outside the ROA back within it, thereby mitigating the transient instability phenomenon of the system. The effectiveness of the proposed method was demonstrated using two case studies.

International Journal of Circuit Theory and ApplicationsVol. 53(6)
Beijing Jiaotong University (CN)
Openalex Percentile: Top 85%
Electric and Hybrid Vehicle Technologies
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Large‐signal stability analysis for hybridized vehicular power supply systems of rolling stocks — Zheming Jin, Haoying Pei, et al. · International Journal of Circuit Theory and Applications (2024) | TGRS Research Map | TGRS