电池(电)
均方误差
自行车
贝叶斯概率
基线(sea)
贝叶斯分层建模
分层数据库模型
人口
计算机科学
统计
可靠性工程
工程类
贝叶斯推理
数据挖掘
人工智能
数学
地理
功率(物理)
物理
海洋学
人口学
考古
量子力学
社会学
地质学
作者
Zihao Zhou,David A. Howey
标识
DOI:10.1016/j.ifacol.2023.10.708
摘要
Accurate prediction of battery health is essential for real-world system management and lab-based experiment design. However, building a life-prediction model from different cycling conditions is still a challenge. Large lifetime variability results from both cycling conditions and initial manufacturing variability, and this—along with the limited experimental resources usually available for each cycling condition—makes data-driven lifetime prediction challenging. Here, a hierarchical Bayesian linear model is proposed for battery life prediction, combining both individual cell features (reflecting manufacturing variability) with population-wide features (reflecting the impact of cycling conditions on the population average). The individual features were collected from the first 100 cycles of data, which is around 5-10% of lifetime. The model is able to predict end of life with a root mean square error of 3.2 days and mean absolute percentage error of 8.6%, measured through 5-fold cross-validation, overperforming the baseline (non-hierarchical) model by around 12-13%.
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