Large-scale battery system modeling and analysis for emerging electric-drive vehicles

Authors:
Kun Li;Jie Wu;Yifei Jiang;Zyad Hassan;Qin Lv;Li Shang;Dragan Maksimovic
Affiliations:
University of Colorado at Boulder, Boulder, CO, USA;Tsinghua University, Beijing, China;University of Colorado at Boulder, Boulder, CO, USA;University of Colorado at Boulder, Boulder, CO, USA;University of Colorado at Boulder, Boulder, CO, USA;University of Colorado at Boulder, Boulder, CO, USA;University of Colorado at Boulder, Boulder, CO, USA
Venue:
Proceedings of the 16th ACM/IEEE international symposium on Low power electronics and design
Year:
2010

Citing 5
Cited 3

Efficient model order reduction via multi-node moment matching

Proceedings of the 2002 IEEE/ACM international conference on Computer-aided design
Battery Life Estimation of Mobile Embedded Systems

VLSID '01 Proceedings of the The 14th International Conference on VLSI Design (VLSID '01)
Balancing batteries, power, and performance: system issues in cpu speed-setting for mobile computing

Balancing batteries, power, and performance: system issues in cpu speed-setting for mobile computing
An analytical model for predicting the remaining battery capacity of lithium-ion batteries

IEEE Transactions on Very Large Scale Integration (VLSI) Systems
A model for battery lifetime analysis for organizing applications on a pocket computer

IEEE Transactions on Very Large Scale Integration (VLSI) Systems

Hybrid energy storage system integration for vehicles

Proceedings of the 16th ACM/IEEE international symposium on Low power electronics and design
Personalized driving behavior monitoring and analysis for emerging hybrid vehicles

Pervasive'12 Proceedings of the 10th international conference on Pervasive Computing
Real-time prediction of battery power requirements for electric vehicles

Proceedings of the ACM/IEEE 4th International Conference on Cyber-Physical Systems

Quantified Score

Hi-index	0.01

Visualization

Abstract

Emerging electric-drive vehicles demonstrate the potential for significant reduction of petroleum consumption and greenhouse gas emissions. Existing electric-drive vehicles typi- cally include a battery system consisting of thousands of Lithium-ion battery cells. Therefore, large-scale battery-system modeling and analysis is essential for battery system performance analysis, next-generation battery system design, and transportation electrification. This paper presents a modeling and analysis framework for large-scale Lithium-ion battery systems. The proposed solution models major run-time and long-term battery effects, and uses fast frequency-domain analysis techniques. It enables efficient and accurate characterization of large- scale battery system run-time charge-cycle energy efficiency and long-term cycle life. Our solution is validated against physical measurements using real-world user driving studies.