Novel Segmented-Prediction-Based FCS-MPCC for Low-Control-Frequency EV EESMs with Uncertain Mutual Inductance Considered

Shaofeng Chen; Guobin Lin; Yunshu Liu; Chao Gong; Yaofei Han; Zhixun Ma

doi:10.1109/IECON51785.2023.10311958

Novel Segmented-Prediction-Based FCS-MPCC for Low-Control-Frequency EV EESMs with Uncertain Mutual Inductance Considered

Shaofeng Chen, Guobin Lin, Yunshu Liu, Chao Gong, Yaofei Han, Zhixun Ma

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Electrically excited synchronous motors (EESMs) without installing slip rings and brushes are drawing increasing attention in the electric vehicle (EV) propulsion systems. To improve the control performance of the EV EESMs with uncertain mutual inductance, which works under low control frequency (LCF), this paper proposes a novel segmented-prediction-based finite control set model predictive current control (FCS-MPCC) strategy. First, a sliding mode (SM) observer is constructed to identify the mutual inductance, with its stability and robustness against parameter mismatch analyzed. By using the estimated mutual inductance, the accurate EESM model used for FCS-MPCC is established, Second, the segmented prediction algorithms are developed to reduce the prediction errors caused by local linearization in the LPF situations. Finally, the proposed mutual inductance identification and high-performance control techniques are verified by experiment, which is conducted on a 580-W EESM drive system.

Original language	English
Title of host publication	IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society
Publisher	IEEE Computer Society
ISBN (Electronic)	9798350331820
DOIs	https://doi.org/10.1109/IECON51785.2023.10311958
State	Published - 2023
Externally published	Yes
Event	49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023 - Singapore, Singapore Duration: 16 Oct 2023 → 19 Oct 2023

Publication series

Name	IECON Proceedings (Industrial Electronics Conference)
ISSN (Print)	2162-4704
ISSN (Electronic)	2577-1647

Conference

Conference	49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023
Country/Territory	Singapore
City	Singapore
Period	16/10/23 → 19/10/23

Keywords

electrically excited synchronous motor
high accuracy
model predictive control
mutual inductance identification
sliding mode observer

Access to Document

10.1109/IECON51785.2023.10311958

Cite this

Chen, S., Lin, G., Liu, Y., Gong, C., Han, Y., & Ma, Z. (2023). Novel Segmented-Prediction-Based FCS-MPCC for Low-Control-Frequency EV EESMs with Uncertain Mutual Inductance Considered. In IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society (IECON Proceedings (Industrial Electronics Conference)). IEEE Computer Society. https://doi.org/10.1109/IECON51785.2023.10311958

@inproceedings{e11a2e95dd8e40aab9022901e4d0141a,

title = "Novel Segmented-Prediction-Based FCS-MPCC for Low-Control-Frequency EV EESMs with Uncertain Mutual Inductance Considered",

abstract = "Electrically excited synchronous motors (EESMs) without installing slip rings and brushes are drawing increasing attention in the electric vehicle (EV) propulsion systems. To improve the control performance of the EV EESMs with uncertain mutual inductance, which works under low control frequency (LCF), this paper proposes a novel segmented-prediction-based finite control set model predictive current control (FCS-MPCC) strategy. First, a sliding mode (SM) observer is constructed to identify the mutual inductance, with its stability and robustness against parameter mismatch analyzed. By using the estimated mutual inductance, the accurate EESM model used for FCS-MPCC is established, Second, the segmented prediction algorithms are developed to reduce the prediction errors caused by local linearization in the LPF situations. Finally, the proposed mutual inductance identification and high-performance control techniques are verified by experiment, which is conducted on a 580-W EESM drive system.",

keywords = "electrically excited synchronous motor, high accuracy, model predictive control, mutual inductance identification, sliding mode observer",

author = "Shaofeng Chen and Guobin Lin and Yunshu Liu and Chao Gong and Yaofei Han and Zhixun Ma",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023 ; Conference date: 16-10-2023 Through 19-10-2023",

year = "2023",

doi = "10.1109/IECON51785.2023.10311958",

language = "英语",

series = "IECON Proceedings (Industrial Electronics Conference)",

publisher = "IEEE Computer Society",

booktitle = "IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society",

}

Chen, S, Lin, G, Liu, Y, Gong, C, Han, Y & Ma, Z 2023, Novel Segmented-Prediction-Based FCS-MPCC for Low-Control-Frequency EV EESMs with Uncertain Mutual Inductance Considered. in IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society. IECON Proceedings (Industrial Electronics Conference), IEEE Computer Society, 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023, Singapore, Singapore, 16/10/23. https://doi.org/10.1109/IECON51785.2023.10311958

Novel Segmented-Prediction-Based FCS-MPCC for Low-Control-Frequency EV EESMs with Uncertain Mutual Inductance Considered. / Chen, Shaofeng; Lin, Guobin; Liu, Yunshu et al.
IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society, 2023. (IECON Proceedings (Industrial Electronics Conference)).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Novel Segmented-Prediction-Based FCS-MPCC for Low-Control-Frequency EV EESMs with Uncertain Mutual Inductance Considered

AU - Chen, Shaofeng

AU - Lin, Guobin

AU - Liu, Yunshu

AU - Gong, Chao

AU - Han, Yaofei

AU - Ma, Zhixun

PY - 2023

Y1 - 2023

N2 - Electrically excited synchronous motors (EESMs) without installing slip rings and brushes are drawing increasing attention in the electric vehicle (EV) propulsion systems. To improve the control performance of the EV EESMs with uncertain mutual inductance, which works under low control frequency (LCF), this paper proposes a novel segmented-prediction-based finite control set model predictive current control (FCS-MPCC) strategy. First, a sliding mode (SM) observer is constructed to identify the mutual inductance, with its stability and robustness against parameter mismatch analyzed. By using the estimated mutual inductance, the accurate EESM model used for FCS-MPCC is established, Second, the segmented prediction algorithms are developed to reduce the prediction errors caused by local linearization in the LPF situations. Finally, the proposed mutual inductance identification and high-performance control techniques are verified by experiment, which is conducted on a 580-W EESM drive system.

AB - Electrically excited synchronous motors (EESMs) without installing slip rings and brushes are drawing increasing attention in the electric vehicle (EV) propulsion systems. To improve the control performance of the EV EESMs with uncertain mutual inductance, which works under low control frequency (LCF), this paper proposes a novel segmented-prediction-based finite control set model predictive current control (FCS-MPCC) strategy. First, a sliding mode (SM) observer is constructed to identify the mutual inductance, with its stability and robustness against parameter mismatch analyzed. By using the estimated mutual inductance, the accurate EESM model used for FCS-MPCC is established, Second, the segmented prediction algorithms are developed to reduce the prediction errors caused by local linearization in the LPF situations. Finally, the proposed mutual inductance identification and high-performance control techniques are verified by experiment, which is conducted on a 580-W EESM drive system.

KW - electrically excited synchronous motor

KW - high accuracy

KW - model predictive control

KW - mutual inductance identification

KW - sliding mode observer

UR - http://www.scopus.com/inward/record.url?scp=85179512449&partnerID=8YFLogxK

U2 - 10.1109/IECON51785.2023.10311958

DO - 10.1109/IECON51785.2023.10311958

M3 - 会议稿件

AN - SCOPUS:85179512449

T3 - IECON Proceedings (Industrial Electronics Conference)

BT - IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society

PB - IEEE Computer Society

T2 - 49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

Y2 - 16 October 2023 through 19 October 2023

ER -

Chen S, Lin G, Liu Y, Gong C, Han Y, Ma Z. Novel Segmented-Prediction-Based FCS-MPCC for Low-Control-Frequency EV EESMs with Uncertain Mutual Inductance Considered. In IECON 2023 - 49th Annual Conference of the IEEE Industrial Electronics Society. IEEE Computer Society. 2023. (IECON Proceedings (Industrial Electronics Conference)). doi: 10.1109/IECON51785.2023.10311958

Novel Segmented-Prediction-Based FCS-MPCC for Low-Control-Frequency EV EESMs with Uncertain Mutual Inductance Considered

Abstract

Publication series

Conference

Keywords

Access to Document

Other files and links

Fingerprint

Cite this