LiFe-net：特斯拉LiFePo4 EV电池随时间变化的温度和充电统计数据的数据驱动建模
LiFe-net: Data-driven Modelling of Time-dependent Temperatures and Charging Statistics Of Tesla's LiFePo4 EV Battery
摘要：
电动汽车（EV）电池温度建模是EV制造的一项基本任务。电池组中的极端温度会影响其寿命和功率输出。虽然存在用于描述电池组中的热传递的理论模型，但是它们的模拟在计算上昂贵。此外，难以从电池单元内获取数据测量。在这项工作中，我们提出了一种数据驱动的代理模型（LiFe-net），该模型使用易于访问的驾驶诊断来估计电池温度，以克服这些限制。该模型将神经算子与传统的数值积分方案相结合，以估计温度演变。此外，我们提出了基线模型的两个进一步的变化：用正则化器训练的LiFe网络和用时间稳定性损失训练的LiFe网络。我们比较了这些模型的泛化误差的测试数据。结果表明，在时间稳定性损失下训练的LiFe网络优于其他两种模型，可以估计未知数据的温度演变，平均相对误差为2.77%。
英文摘要：
Modelling the temperature of Electric Vehicle (EV) batteries is a fundamental task of EV manufacturing. Extreme temperatures in the battery packs can affect their longevity and power output. Although theoretical models exist for describing heat transfer in battery packs, they are computationally expensive to simulate. Furthermore, it is difficult to acquire data measurements from within the battery cell. In this work, we propose a data-driven surrogate model (LiFe-net) that uses readily accessible driving diagnostics for battery temperature estimation to overcome these limitations. This model incorporates Neural Operators with a traditional numerical integration scheme to estimate the temperature evolution. Moreover, we propose two further variations of the baseline model: LiFe-net trained with a regulariser and LiFe-net trained with time stability loss. We compared these models in terms of generalization error on test data. The results showed that LiFe-net trained with time stability loss outperforms the other two models and can estimate the temperature evolution on unseen data with a relative error of 2.77 % on average.
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