Analysis and key findings from real-world electric vehicle field data

被引：20
|
作者：
Pozzato, Gabriele [1 ]
Allam, Anirudh [1 ]
Pulvirenti, Luca [1 ]
Negoita, Gianina Alina [2 ]
Paxton, William A. [2 ]
Onori, Simona [1 ]
机构：
[1] Stanford Univ, Energy Sci & Engn, 367 Panama Mall, Stanford, CA 94305 USA
[2] Volkswagen Grp Amer Inc, Innovat & Engn Ctr Calif, 500 Clipper Dr, Belmont, CA 94002 USA
来源：
JOULE | 2023年 / 7卷 / 09期
关键词：
LITHIUM-ION BATTERIES; STATE; SYSTEMS; HEALTH;
D O I：
10.1016/j.joule.2023.07.018
中图分类号：
O64 [物理化学（理论化学）、化学物理学];
学科分类号：
070304 ; 081704 ;
摘要：
Deploying battery state of health (SoH) estimation and forecasting algorithms are critical for ensuring the reliable performance of battery electric vehicles (EVs). SoH algorithms are designed and trained from data collected in the laboratory upon cycling cells under predefined loads and temperatures. Field battery pack data collected over 1 year of vehicle operation are used to define and extract performance/health indicators and correlate them to real driving characteristics (charging habits, acceleration, and braking) and season-dependent ambient temperature. Performance indicators (PIs) during driving and charging events are defined upon establishing a data pipeline to extract key battery management system (BMS) signals. This work shows the misalignment existing between laboratory testing and actual battery usage, and the opportunity that exists in enhancing battery experimental testing to deconvolute time and temperature to improve SoH estimation strategies.
引用
收藏
页码：2035 / 2053
页数：20
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