  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.
