Hybrid VARMA and LSTM Method for Lithium-ion Battery State-of-Charge and Output Voltage Forecasting in Electric Motorcycle Applications

Abstract

Electric vehicles (EVs) have gained attention owing to their effectiveness in reducing oil demands and gas emissions. Of the electric components of an EV, a battery is considered as the major bottleneck. Among the various types of battery, lithium-ion batteries are widely employed to power EVs. To ensure the safe application of batteries in EVs, monitoring and control are performed using state estimation. The state of a battery includes the state-of-charge (SoC), state-of-health (SoH), state-of-power (SoP), and state-of-life (SoL). The SoC of a battery is the remaining usable percentage of its capacity. This mainly depends on variations of the operating condition of the EV in which the battery is applied. The SoC of a battery is reflected by its output voltage. That is, the SoC is considered to be zero when the output voltage of a battery drops below a cut-off voltage. This study proposes an SoC and output voltage forecasting method using a hybrid of the vector autoregressive moving average (VARMA) and long short-term memory (LSTM). This approach aims to estimate and forecast the SoC and output voltage of a battery when an EV is driven under the CVS-40 drive cycle. Forecasting using the hybrid VARMA and LSTM method achieves a lower root-mean-square error (RMSE) than forecasting with only VARMA or LSTM individually.

Keywords

• Battery output voltage
• lithium-ion battery
• neural network
• state-of-charge
• VARMA