A Neural-Network-Embedded Equivalent Circuit Model for Lithium-ion Battery State Estimation

TL;DR

This paper introduces a hybrid neural network-embedded equivalent circuit model for lithium-ion batteries, significantly improving state of charge estimation accuracy under diverse and extreme conditions.

Contribution

It proposes a novel hybrid model embedding neural networks into classical ECMs, enhancing nonlinear fitting and adaptability for battery state estimation.

Findings

Achieves 29%-64% error reduction in SOC estimation

Effective under various temperatures and complex operating conditions

Demonstrates improved accuracy over traditional ECMs

Abstract

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.