论文标题
电动汽车电池建模的基于机器学习的数字双胞胎
A Machine Learning-based Digital Twin for Electric Vehicle Battery Modeling
论文作者
论文摘要
与液态燃料相比，电动汽车（EV）的广泛采用受到目前能量和功率密度低的电池的限制，并且会随着时间的推移而衰老和性能恶化。因此，在EV寿命中监视电池电量状态（SOC）和健康状况（SOH）是一个非常相关的问题。这项工作提出了一个电池数字双结构结构，旨在在运行时准确反映电池动态。为了确保对非线性现象具有高度的正确性，数字双胞胎依赖于在电池演化痕迹的数据驱动模型中依靠：随着时间的推移训练的模型：一种SOH模型，反复执行以估计最大电池容量的退化和SOC模型的降低，并在定期进行了重新训练以反映衰老的影响。提出的数字双结构将在公共数据集上举例说明，以激发其采用并证明其有效性，并具有很高的准确性和推理以及与车载执行兼容的时间。
The widespread adoption of Electric Vehicles (EVs) is limited by their reliance on batteries with presently low energy and power densities compared to liquid fuels and are subject to aging and performance deterioration over time. For this reason, monitoring the battery State Of Charge (SOC) and State Of Health (SOH) during the EV lifetime is a very relevant problem. This work proposes a battery digital twin structure designed to accurately reflect battery dynamics at the run time. To ensure a high degree of correctness concerning non-linear phenomena, the digital twin relies on data-driven models trained on traces of battery evolution over time: a SOH model, repeatedly executed to estimate the degradation of maximum battery capacity, and a SOC model, retrained periodically to reflect the impact of aging. The proposed digital twin structure will be exemplified on a public dataset to motivate its adoption and prove its effectiveness, with high accuracy and inference and retraining times compatible with onboard execution.
