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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.

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