一种用于锂离子电池状态估计的神经网络嵌入等效电路模型
A Neural-Network-Embedded Equivalent Circuit Model for Lithium-ion Battery State Estimation
AI总结：
本文提出将神经网络作为虚拟电子元件嵌入经典等效电路模型以提升非线性拟合能力，并设计迭代离线训练策略，在真实数据集上使电池SOC估计误差降低29%-64%。
摘要：
等效电路模型（ECM）因其简单性、稳定性和可解释性而被广泛应用于电池建模和状态估计。然而，在冰冻环境温度和复杂的充放电行为等极端工况下，电池的电化学特性变得极其复杂且非线性，ECM可能会产生较大的估计误差。本文提出了一种混合电池模型，将神经网络作为“虚拟电子元件”嵌入到经典ECM中，以增强模型的非线性拟合能力和适应性。首先，介绍了所提出混合模型的结构，其中嵌入的神经网络旨在拟合经典ECM的残差。其次，设计了一种迭代离线训练策略，通过将电池状态空间方程合并到神经网络损失函数中来训练该混合模型。最后，基于所提出的混合模型实现了电池在线荷电状态（SOC）估计，以展示其应用价值。基于真实世界电池数据集的仿真结果表明，在不同环境温度的运行条件下，所提出的混合模型在SOC估计中可实现29%-64%的误差降低。
英文摘要：
Equivalent Circuit Model(ECM)has been widelyused in battery modeling and state estimation because of itssimplicity, stability and interpretability.However, ECM maygenerate large estimation errors in extreme working conditionssuch as freezing environmenttemperature andcomplexcharging/discharging behaviors,in whichscenariostheelectrochemical characteristics of the battery become extremelycomplex and nonlinear.In this paper,we propose a hybridbattery model by embeddingneural networks as 'virtualelectronic components' into the classical ECM to enhance themodel nonlinear-fitting ability and adaptability. First, thestructure of the proposed hybrid model is introduced, where theembedded neural networks are targeted to fit the residuals of theclassical ECM,Second, an iterative offline training strategy isdesigned to train the hybrid model by merging the battery statespace equation into the neural network loss function. Last, thebattery online state of charge (SOC)estimation is achieved basedon the proposed hybrid model to demonstrate its applicationvalue,Simulation results based on a real-world battery datasetshow that the proposed hybrid model can achieve 29%-64%error reduction for $OC estimation under different operatingconditions at varying environment temperatures.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
