论文标题
分析各种驱动周期上锂离子电池的电荷估计的Narxnn
Analysis of NARXNN for State of Charge Estimation for Li-ion Batteries on various Drive Cycles
论文作者
论文摘要
由于环境友好，电动汽车（电动汽车）的受欢迎程度迅速增加。锂离子电池是电动汽车技术的核心，并为电动汽车的重量和成本做出了贡献。电荷状态（SOC）是一个非常重要的指标，有助于预测EV的范围。需要在电池组中准确估算可用的电池容量，以便可以确定车辆中的可用范围。有多种技术可以估算SOC。在本文中，选择了数据驱动的方法，并探索了具有外源输入神经网络（NARXNN）的非线性自回归网络以准确估计SOC。 Narxnn已被证明优于文献中可用的常规机器学习技术。 NARXNN模型是在LA92，US06，UDDS和HWFET等各种EV驱动周期上开发和测试的，以测试其在现实世界情景上的性能。该模型显示出胜过传统的统计机器学习方法，并在1E-5范围内实现平均误差（MSE）。
Electric Vehicles (EVs) are rapidly increasing in popularity as they are environment friendly. Lithium Ion batteries are at the heart of EV technology and contribute to most of the weight and cost of an EV. State of Charge (SOC) is a very important metric which helps to predict the range of an EV. There is a need to accurately estimate available battery capacity in a battery pack such that the available range in a vehicle can be determined. There are various techniques available to estimate SOC. In this paper, a data driven approach is selected and a Nonlinear Autoregressive Network with Exogenous Inputs Neural Network (NARXNN) is explored to accurately estimate SOC. NARXNN has been shown to be superior to conventional Machine Learning techniques available in the literature. The NARXNN model is developed and tested on various EV Drive Cycles like LA92, US06, UDDS and HWFET to test its performance on real world scenarios. The model is shown to outperform conventional statistical machine learning methods and achieve a Mean Squared Error (MSE) in the 1e-5 range.
