Incisively
estimating the state of charge (SOC) of lithium-ion
batteries is essential to ensure the safe and stable operation of
a battery management system. Neural network methods do not depend
on a specific lithium-ion battery model and are able to mirror the
lithium-ion battery’s nonlinear relationships, thus receiving
widespread attention; however, traditional neural network methods
exhibit a long training time and low accuracy in estimating SOC. This
paper presents an original algorithm of an improved particle swarm
optimization (IPSO) extreme learning machine (ELM) neural network,
improving the particle swarm algorithm using nonlinear inertia weights
to enhance the global optimization seeking capability of ELM for solving
the problem of poor precision of previous battery SOC estimation.
The lithium-ion battery voltage and current are the input variables
of the model, while SOC is used as the output variable. The results
of the experiments revealed that the root-mean-square estimation errors
of the proposed IPSO-ELM algorithm for SOC are within 0.31, 0.32,
and 0.14% of the root mean square under the hybrid pulse power characteristic
(HPPC), the Beijing bus dynamic stress test (BBDST), and the dynamic
stress test (DST) operating conditions. Compared with the prediction
results of the PSO-ELM and ELM neural networks, the simulation results
prove that the SOC optimization method in this paper possesses superior
precision and overcomes the shortcomings of traditional neural networks.