Battery (electricity)

Quantum mechanics

Algorithm

Control (management)

Engineering

State of charge

Lithium (medication)

Lithium-ion battery

Computer security

Endocrinology

Systems engineering

Power (physics)

Particle swarm optimization

Computer science

Estimation

Thermodynamics

Physics

Artificial intelligence

Charge (physics)

Medicine

Control theory (sociology)

Artificial neural network

State (computer science)

Key (lock)

Energy

State-of-charge (SOC) estimation of lithium-ion battery is one of the core functions of battery management system. In order to improve the estimation accuracy of SOC, this paper proposes a long short-term memory neural network based on particle swarm optimization (PSO-LSTM). Firstly, the key parameters of LSTM are optimized by PSO algorithm, so that the data characteristics of lithium-ion battery can match the network topology. In addition, random noise is added to the input layer of PSO-LSTM neural network to improve the anti-interference ability of the network. Finally, experiments show that the proposed method can achieve accurate estimation under different conditions. The estimates based on PSO-LSTM converge to the real state-of-charge within an error of 0.5%.

A method for state-of-charge estimation of lithium-ion batteries based on PSO-LSTM