论文标题
电池寿命早期预测的贝叶斯分层建模
Bayesian hierarchical modelling for battery lifetime early prediction
论文作者
论文摘要
对电池健康的准确预测对于实际系统管理和基于实验室的实验设计至关重要。但是，从不同的自行车条件下建立生命预测模型仍然是一个挑战。寿命的巨大变异性来自骑自行车条件和初始制造可变性，这 - 加上通常适用于每种循环条件的有限的实验资源 - 使数据驱动的终身预测具有挑战性。在这里，提出了一个用于电池寿命预测的层次贝叶斯线性模型，将两个单个细胞特征（反映制造可变性）与人口范围的特征相结合（反映了循环条件对人口平均水平的影响）。单个特征是从前100个数据周期收集的，该数据约占寿命的5-10％。该模型能够以3.2天的根平方误差为3.2天，而绝对百分比误差为8.6％，通过5倍交叉验证测量，过分表现基线（非分层）模型的平均百分比误差约为12-13％。
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%.
