Machine learning and neural network supported state of health simulation and forecasting model for lithium-ion battery

Abstract

As the intersection of disciplines deepens, the field of battery modeling is increasingly employing various artificial intelligence (AI) approaches to improve the efficiency of battery management and enhance the stability and reliability of battery operation. This paper reviews the value of AI methods in lithium-ion battery health management and in particular analyses the application of machine learning (ML), one of the many branches of AI, to lithium-ion battery state of health (SOH), focusing on the advantages and strengths of neural network (NN) methods in ML for lithium-ion battery SOH simulation and prediction. NN is one of the important branches of ML, in which the application of NNs such as backpropagation NN, convolutional NN, and long short-term memory NN in SOH estimation of lithium-ion batteries has received wide attention. Reports so far have shown that the utilization of NN to model the SOH of lithium-ion batteries has the advantages of high efficiency, low energy consumption, high robustness, and scalable models. In the future, NN can make a greater contribution to lithium-ion battery management by, first, utilizing more field data to play a more practical role in health feature screening and model building, and second, by enhancing the intelligent screening and combination of battery parameters to characterize the actual lithium-ion battery SOH to a greater extent. The in-depth application of NN in lithium-ion battery SOH will certainly further enhance the science, reliability, stability, and robustness of lithium-ion battery management.

Key words： machine learning; lithium-ion battery; state of health; neural network; artificial intelligence

Acknowledgements

This work was supported by the National Key R&D Program of China (Grant No. 2021YFB2401800), the Research Fund Program for Young Scholars (Chen Lai) of Beijing Institute of Technology, and the National Natural Science Foundation of China (Grant No. 52074037).

Competing interests

The authors declare that they have no competing interests.

Notations

AI	Artificial intelligence
ANN	Artificial neural network
BA	Bat algorithm
Ccurrent	Current maximum capacity of battery
Cinitial	Initial capacity of battery
EKF	Extended Kalman filter
DEGWO	Differential evolution grey wolf optimizer
GPR	Gaussian process regression
MAE	Mean absolute error
MAPE	Mean absolute percentage error
MIT	Massachusetts Institute of Technology
ML	Machine learning
MKRVM	Multiple relevance vector machine
MSE	Mean square error
NASA	National Aeronautics and Space Administration
OCV	Open circuit voltage
PF	Particle filter
R	Internal resistance measured at the current state
R0	Internal resistance of a new battery at the factory
Reol	Internal resistance at the end of battery life
R2	Coefficient of determination
RMSE	Root mean square error
PSO	Particle swarm optimization
RVM	Relevance vector machine
SOC	State of charge
SOH	State of health
SVM	Support vector machine
WLS	Weighted least squares
WOA	Whale optimization algorithm
yi	Measured value
ŷi	Predicted value

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