ABSTRACT
The transition to Electric Vehicles (EV) in place of traditional internal combustion engines is increasing societal demand for electricity. The ability to integrate the additional demand from EV charging into forecasting electricity demand is critical for maintaining the reliability of electricity generation and distribution. Load forecasting studies typically exclude households with home EV charging, focusing on offices, schools, and public charging stations. Moreover, they provide point forecasts which do not offer information about prediction uncertainty. Consequently, this paper proposes the Long Short-Term Memory Bayesian Neural Networks (LSTM-BNNs) for household load forecasting in presence of EV charging. The approach takes advantage of the LSTM model to capture the time dependencies and uses the dropout layer with Bayesian inference to generate prediction intervals. Results show that the proposed LSTM-BNNs achieve accuracy similar to point forecasts with the advantage of prediction intervals. Moreover, the impact of lockdowns related to the COVID-19 pandemic on the load forecasting model is examined, and the analysis shows that there is no major change in the model performance as, for the considered households, the randomness of the EV charging outweighs the change due to pandemic.
ABSTRACT
The COVID-19 pandemic has affected human behavior drastically in various ways, including commuter patterns and traffic volumes. This paper investigates how the COVID-19 outbreak has changed the user habits and utilization patterns at public electric vehicle service equipment (EVSE). More than 7,300 charging sessions collected at 54 public Level 2 charging stations across the State of Rhode Island were analyzed using a multi-method approach comparing charging events from two time periods, before and during the pandemic. The study shows that charging behavior has changed significantly since the COVID-19 outbreak. We found that the energy consumption, charging duration, distance from home, and charging frequency decreased significantly during the pandemic. Additionally, the study discovered a relationship between the observation period and the day of the charging session. During the pandemic, charging on Sundays has become significantly more important for users than charging between Monday and Friday. We provide important insights for policymakers about how the COVID-19 pandemic has changed electric vehicle user charging behavior and demand. © 2022 IISE Annual Conference and Expo 2022. All rights reserved.
ABSTRACT
EV market has risen despite COVID-19 pandemic. Wireless EV charging is safer operating, saves the environment, and is convenient with automated charging. Inductive wireless EV Charging (IPT) should be fewer power electronics components to improve the system's efficiency. This paper presents the fewer components constant current - constant voltage (CC-CV) IPT using frequency adaptive frequency control technique that does not need a primary dc-dc converter. The proposed CC-CV IPT charging building block consists of the front-end rectifier, CC-CV frequency controlled inverter, IPT coils, secondary high-frequency rectifier, and step resistive load battery simulator. The load current and voltage signals are detected and feedback to the PI control block that controls the frequency of the PWM gate drive of the inverter. The simulation and experimental results show the operating mode of 6A CC and 102 V CV chargings were performed. The wireless EV charging can be operated for the CC and CV chargings using variable frequency control. © 2022 IEEJ-IAS.
ABSTRACT
Electric cars sales have been rising almost steadily over the past decade. Uncontrolled charging has recently emerged as the main detrimental factor to this otherwise environmentally friendly and paradigm shifting technology due to the incurred impact on the energy grid. In addition, people are usually hesitant in allowing their vehicles to be controlled by external units;therefore, controlled charging strategies that offer users the option to have some control over their vehicles seems to be a sensible choice moving towards a gasoline-free vehicles market. This work investigated two price-sensitive charging strategies that allowed users to control the charging of their vehicle in order to receive cost benefits. These strategies were of a parametric nature;thus, the analysis focused on providing useful rules of thumb to guide users in choosing the most suitable strategy and the relevant parameters according to their driving profiles. The results show that when driving less than 40 km/h on average and employing a price-sensitive charging strategy with the proposed optimized parameters, electric car users may obtain 30–40% of the running cost reduction.
ABSTRACT
The COVID-19 pandemic has forced many governments around the world to implement strict lockdown measures and order citizens to stay at home, which has caused a major change in travel patterns. This study leveraged electric vehicle charging big data in Hefei, Anhui Province, China to estimate electric vehicle charging demand in the absence of the COVID-19 pandemic using multi-layer perceptron model, which quantified the impact of the COVID-19 pandemic. In addition, we employed the vector autoregressive model to investigate the dynamic relationships between the changes in charging demand and various explanatory factors. The results suggest that the daily average charging demand in Hefei decreased by 78.3% compared to the predicted value during the pandemic. Furthermore, according to the variance decomposition and impulse response function analysis, national confirmed COVID-19 cases play a dominant role in reducing charging demand. The number of daily hospitalizations and Migration Scale Index also have significant and robust effect on the decrease in charging demand. The Air Quality Index and Baidu Index are susceptible to external factors and do not have a direct impact on the change in charging demand. Findings support a better understanding of changes in travel behavior during the pandemic and provide policy makers with references to deal with similar events. © 2021 IEEE