Electric vehicles

Fully electric vehicles (EVs) have gained significant popularity and countries such as Norway are leading the world with over 90% EV market share in new car sales. However, older internal combustion engine (ICE) powered vehicles currently on today’s roads are expected to continue to operate until the end of their life cycle. As a result, a mixed vehicle fleet is expected to persist in the coming decade. Unfortunately, there has been an underlying assumption that the traditional internal combustion vehicles are expected to exhibit the same driving behavior when electrified vehicles are introduced in the mixed traffic fleet. Unlike ICE powered vehicles, EVs deliver immediate and strong deceleration via regenerative braking, and this could cause disturbances when the less capable ICE vehicles are following. These differences in driving dynamics may translate to substantial impacts to roadway capacity, especially when mixed with human driven ICE powered vehicles. Although ACC equipped EVs can adopt shorter headways and react quickly to speed changes, potentially improving roadway capacity, our empirically validated simulation study on ACC with ICE and electric powertrain suggestion that the increase in market penetration of EVs could result in greater capacity but mostly at higher EV market penetrations, because EVs mostly interact with other EVs and there would not be many ICE vehicles following EVs undergoing rapid regenerative braking. Conversely, at low market penetrations, there are numerous ICE vehicles interacting with a few EVs that undergo rapid deceleration, causing disturbances and negating the potential capacity benefit of EVs.

The increasing integration of renewable energy sources (RES) and electric vehicles (EVs) into microgrids presents both opportunities and challenges in terms of optimizing energy use and minimizing electricity costs. This dissertation explores the development of an advanced optimization framework using artificial intelligence (AI) to enhance battery operation in microgrids. The proposed solution leverages AI techniques to dynamically manage the charging and discharging of batteries, considering fluctuating energy demands, variable electricity pricing, and intermittent RES generation.
By employing a fuzzy logic-based control algorithm, the system intelligently allocates energy from solar power, grid electricity, and battery storage, while coordinating EV charging schedules to reduce peak demand charges. The optimization framework integrates predictive modeling for energy consumption and generation, alongside real-time data from weather forecasts and electricity markets, to make informed decisions. Additionally, the approach considers the trade-off between maximizing renewable energy usage and minimizing reliance on costly grid power during peak hours.

Today’s mainstream vehicles are partially automated via an Advanced Driver Assistance Feature (ADAS) known as Adaptive Cruise Control (ACC). ACC relies on data from onboard sensors to automatically adjust speed to maintain a safe following distance with the preceding vehicle. Contrary to expectations for automated vehicles, ACC may reduce capacity at bottlenecks because its delayed response and limited initial acceleration during queue discharge could increase the average headway. Fortunately, when ACC is paired with fully electric vehicles (EVs), EV’s unique powertrain characteristics such as instantaneous torque and aggressive regenerative braking could allow ACC to adopt shorter headways and accelerate more swiftly to maintain shorter headways during queue discharge, therefore reverse the negative impact on capacity. This has been verified in a series of car following field experiments. Field experiments demonstrate that EVs with ACC can achieve a capacity as high as 3333 veh/hr/lane when cruising in steady state conditions at typical freeway speeds (60 mph and 55 mph) and arterial speeds (45 mph and 35 mph). Furthermore, speed fluctuations and disturbances that may come from queues forming at or near the bottleneck do not reduce the capacity, unlike ACC-equipped internal combustion engine (ICE) vehicles, making ACC-equipped EVs outperform ICE vehicles with ACC, as well as human drivers.

Adaptive cruise control (ACC) system is the first widely offered automated functionality that regulates the longitudinal movement of the vehicle using onboard radar sensors, and they can maintain a safe following distance with the preceding vehicle. In most of the field experiments with ACC-equipped vehicles conducted with internal combustion engine vehicles, there is still a gap in research on how the automation systems such as ACC combined with electric powertrains will influence the traffic flow be examined.
This study refined and recalibrated an ACC car-following model for EVs and integrated it into AIMSUN to realistically simulate ACC-equipped vehicles and their impact on the fundamental diagram of traffic flow. Simulations were conducted for various ACC market penetrations, and fundamental diagrams were constructed for those market penetrations using detector measurements at various locations along the simulated segment. Overall, the capacity and the jam density increase as the EV with ACC market penetration rises. EVs with ACC can achieve higher capacities compared to ICEs with ACC.