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

Abstract

Emerging electric-drive vehicles demonstrate the potential for significant reduction of petroleum consumption and greenhouse gas emissions. Existing electric-drive vehicles typically include a battery system consisting of thousands of Lithium-ion battery cells. Therefore, large-scale batterysystem 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 largescale 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.

Hybrid energy storage system integration for vehicles

“…Such intensive use causes significant battery self-heating, accelerating temperature-dependent aging effects, hence battery long-term capacity degradation. Using the battery system model we recently developed [11] and the daily commute driving profiles of ten users, our study shows that user driving behavior has significant impact on ESS lifetime. Targeting the worst-case driving scenario would dramatically increase the ESS cost.…”

“…Building upon our recent work on ESS modeling [11] that considers major run-time and long-term effects, this framework optimizes the ESS design by incorporating complementary energy storage technologies and conducts statistical optimization for ESS cost and lifetime. Specifically, we present an ESS design and optimization solution that considers the variances due to battery system manufacture tolerance and user-specific driving behavior, as well as statistical optimization techniques to optimize ESS cost, yet providing statistical lifetime guarantee.…”

“…(w, p)) with arbitrary distributions. Given a sample point composed of a specific value for each basic random variable, the aging effect of the system can be calculated through our model presented in [11]. To compute the aging distribution is more complicated.…”

Real-time prediction of battery power requirements for electric vehicles

“…To manage large-scale batteries for EVs in the presence of nonlinear, complex battery dynamics [13], existing BMSes [4,5,6,14,15,16] usually consist of the battery status monitor, the battery manager, and the battery configurator as shown in Fig. 5.…”

“…To do this efficiently, processing units in the BMSes need accurate and appropriate physical state information around/in the battery cells. While existing approaches assume that the physical states, such as the cell discharging rate (i.e., the required EV load), are constant or change slowly with time [5,6], this assumption is not realistic as EVs' power requirements usually change abruptly and significantly. That is, simple measurement of past power consumption cannot accurately predict power demands in future for efficient battery management.…”