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Summary & Details

Efficient Kalman Filter-Based Battery Pack SOC Estimation Using Bar Delta Filtering

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Format: PDF

Price: Non-members (tax incl.)：￥1,100 Members (tax incl.)：￥880

Publication code: 20266078

Paper/Info type: Proceedings (Autumn)

No.108-26

Pages: 1-7(Total 7 p)

Date of publication: Oct 2026

Publisher: JSAE

Language: English

Event: 2026 JSAE Annual Congress (Autumn)

Detailed Information

Author(J)	1) Seyedmehdi Hosseininasab, 2) Changwei Lin, 3) Lennart Bauer, 4) Tsuyoshi Horiba, 5) Yasutaka Ikawa
Author(E)	1) Seyedmehdi Hosseininasab, 2) Changwei Lin, 3) Lennart Bauer, 4) Tsuyoshi Horiba, 5) Yasutaka Ikawa
Affiliation(J)	1) FEV Europe, 2) FEV Europe, 3) FEV Europe, 4) FEV Japan, 5) FEV Japan
Affiliation(E)	1) FEV Europe, 2) FEV Europe, 3) FEV Europe, 4) FEV Japan, 5) FEV Japan
Abstract(E)	Accurate estimation of SOC in lithium-ion battery packs is essential for safe reliable and optimized BMS in electric vehicles and energy storage. This study presents an efficient framework combining a lightweight Kalman filter with the Bar-Delta method for series-connected LFP cells. A simulation model with realistic cell-to-cell variations evaluates performance. The method estimates pack average SOC and cell deviations with lower computational cost. Results show comparable accuracy to full Kalman filtering while significantly reducing computation, enabling real-time embedded applications. It is suitable for resource-constrained hardware and scalable pack monitoring deployments today.

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