Accurately measuring the cycle lifetime of commercial lithium-ion batteries is crucial for performance and technology development. We introduce a novel hybrid approach combining a physics-based equation with a self-attention model to predict the cycle lifetimes of commercial lithium iron phosphate graphite cells via early-cycle data. After fitting capacity loss curves to this physics-based equation, we then use a self-attention layer to reconstruct entire battery capacity loss curves. Our model exhibits comparable performances to existing models while predicting more information: the entire capacity loss curve instead of cycle life. This provides more robustness and interpretability: our model does not need to be retrained for a different notion of end-of-life and is backed by physical intuition.

翻译：准确测量商用锂离子电池的循环寿命对于性能提升和技术开发至关重要。我们提出了一种新型混合方法，将基于物理的方程与自注意力模型相结合，利用早期循环数据预测商用磷酸铁锂石墨电池的循环寿命。在将容量衰减曲线拟合至该物理方程后，我们采用自注意力层重构完整的电池容量衰减曲线。与现有模型相比，本模型在保持相当性能的同时能够预测更多信息：即完整的容量衰减曲线而非单一循环寿命。这增强了模型的鲁棒性与可解释性：无需针对不同的寿命终止定义重新训练模型，且具有物理直觉支撑。