Hybrid VARMA and LSTM Method for Lithium-ion Battery State-of-Charge and Output Voltage Forecasting in Electric Motorcycle Applications
- Authors
- Caliwag, Angela C.; Lim, Wansu
- Issue Date
- 2019
- Publisher
- IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
- Keywords
- Battery output voltage; lithium-ion battery; neural network; state-of-charge; VARMA
- Citation
- IEEE ACCESS, v.7, pp 59680 - 59689
- Pages
- 10
- Journal Title
- IEEE ACCESS
- Volume
- 7
- Start Page
- 59680
- End Page
- 59689
- URI
- https://scholarworks.bwise.kr/kumoh/handle/2020.sw.kumoh/25574
- DOI
- 10.1109/ACCESS.2019.2914188
- ISSN
- 2169-3536
- 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.
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