论文标题
使用深度学习的新型SOC估算
A Novel SOC Estimation for Hybrid Energy Pack using Deep Learning
论文作者
论文摘要
估计电动汽车混合储能系统（EV）中复合储能设备的电荷状态（SOC）对于改善EV的性能至关重要。电动汽车的复杂充电和排放电流使准确的SOC估计成为挑战。本文提出了一种基于锂离子电池 - 植物能力镜的HESS EV的新型基于学习的SOC估计方法，基于非线性自动回应，具有外源输入神经网络（NARXNN）。 Narxnn用于捕获和克服电动汽车中锂离子电池和超级电容器的复杂非线性行为。结果表明，所提出的方法平均提高了SOC估计准确性91.5％，误差值低于0.1％，并将消费时间降低了11.4％。因此，验证了所提出方法的有效性和鲁棒性。
Estimating the state of charge (SOC) of compound energy storage devices in the hybrid energy storage system (HESS) of electric vehicles (EVs) is vital in improving the performance of the EV. The complex and variable charging and discharging current of EVs makes an accurate SOC estimation a challenge. This paper proposes a novel deep learning-based SOC estimation method for lithium-ion battery-supercapacitor HESS EV based on the nonlinear autoregressive with exogenous inputs neural network (NARXNN). The NARXNN is utilized to capture and overcome the complex nonlinear behaviors of lithium-ion batteries and supercapacitors in EVs. The results show that the proposed method improved the SOC estimation accuracy by 91.5% on average with error values below 0.1% and reduced consumption time by 11.4%. Hence validating both the effectiveness and robustness of the proposed method.
