论文标题
电池模型的强大数据驱动错误补偿
Robust Data-Driven Error Compensation for a Battery Model
论文作者
论文摘要
- 这项工作已提交给IFAC以获取可能的出版物 - 牵引电池的模型是整个汽车传动系统开发的必不可少的工具。令人惊讶的是，当今收集的大量电池数据尚未用于更准确和可靠的模拟。主要是，常规电池操作期间的非均匀激发阻止了此类测量值的利用。因此，需要方法，该方法可以基于大型数据集启用强大的模型。因此，引入了一个数据驱动的错误模型，以增强现有的有力动机模型。神经网络补偿现有的动态误差，并根据对基础数据的描述进一步限制。本文试图验证一般设置的有效性和鲁棒性，并另外评估单级支持向量机作为训练数据分布的拟议模型。根据五个数据集，显示出在边界之外逐渐限制数据驱动的误差补偿，从而导致相似的改进和总体鲁棒性的增加。
- This work has been submitted to IFAC for possible publication - Models of traction batteries are an essential tool throughout the development of automotive drivetrains. Surprisingly, today's massively collected battery data is not yet used for more accurate and reliable simulations. Primarily, the non-uniform excitation during regular battery operations prevent a consequent utilization of such measurements. Hence, there is a need for methods which enable robust models based on large datasets. For that reason, a data-driven error model is introduced enhancing an existing physically motivated model. A neural network compensates the existing dynamic error and is further limited based on a description of the underlying data. This paper tries to verify the effectiveness and robustness of the general setup and additionally evaluates a one-class support vector machine as the proposed model for the training data distribution. Based on a five datasets it is shown, that gradually limiting the data-driven error compensation outside the boundary leads to a similar improvement and an increased overall robustness.
