论文标题
生命网络：与时间相关的温度和Tesla LifePo4 EV电池的数据驱动建模
LiFe-net: Data-driven Modelling of Time-dependent Temperatures and Charging Statistics Of Tesla's LiFePo4 EV Battery
论文作者
论文摘要
对电动汽车（EV）电池的温度进行建模是电动汽车制造的基本任务。电池组中的极端温度会影响其寿命和功率输出。尽管存在用于描述电池组中传热的理论模型，但它们在计算上的模拟却很昂贵。此外，很难从电池电池内获取数据测量结果。在这项工作中，我们提出了一个数据驱动的替代模型（Life-NET），该模型使用易于访问的驱动诊断来进行电池温度估算来克服这些限制。该模型将神经操作员与传统的数值集成方案结合在一起，以估计温度演变。此外，我们提出了基线模型的进一步变化：生命周期，该模型接受了正规机构和生命网训练，并受到了时间稳定性损失的训练。我们根据测试数据的概括误差比较了这些模型。结果表明，经过时间稳定性损失训练的生命网表现优于其他两个模型，并且可以估计看不见的数据的温度演变，平均相对误差为2.77％。
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.
