Research on Dynamic Coordination Active Mode Switching Control Strategy for Hybrid Electric Vehicle Based on Traffic Information

Ye, Ming; Gongye, Xiangyu; Liu, Yonggang; Xiao, Wang

doi:10.1109/access.2019.2932585

Cited by 6 publications

(2 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To address this, the work proposed by [20] discusses a two-stage approximate dynamic programming framework to determine the optimal charging strategy by utilizing predicted short-term future information and long-term estimations from historical data. In [21], the authors also establish an intelligent traffic scene with optimal traffic-light control to predict future driving-state information and driving models of hybrid EVs (HEV). Combining each available taxi's historical charging events and real-time GPS trajectories, the work in Reference [22] predicts a taxi's current operational state and recommends a suitable charging station for each cab to minimize its charging time.…”

Section: Introductionmentioning

confidence: 99%

Optimal Aggregation Design for Massive V2G Participation in Energy Market

Jin

Seo

et al. 2020

IEEE Access

View full text Add to dashboard Cite

As a new type of transportation, electric vehicles (EV) can effectively adjust the supply and demand balance of power systems using their vehicle-to-grid (V2G) characteristics. To better promote the participation of EV resources in the energy market and interact with power systems, we propose a novel framework of an electric vehicle aggregator (EVA) that can aggregate schedulable EVs within its jurisdiction to provide auxiliary services for the power grid. Due to EV charging behavior's uncertain nature, we employ a probability mass function (PMF) based model to provide more accurate forecasts of future EV behaviors. To reduce EVA operation costs and maximize the travel utility for EV users participating in this service, we develop an EVA optimization schedule model that combines a day-ahead optimization schedule and realtime optimization schedule. Finally, we create three case studies to verify the results of the proposed method. Matlab is used to simulate and analyze each case study concerning uncoordinated charging, coordinated charging while considering day-ahead optimization schedules, and an ensemble of coordinated charging activities that consider the day-ahead optimization schedule and real-time optimization schedule. Through comparative analysis, it is verified that the proposed strategy can effectively reduce EVAs' operating costs and meet the travel requirements of EV users. The impact of different degrees of error of EV plug-out information on the proposed method is also analyzed. INDEX TERMS Electric vehicles, vehicle-to-grid, electric vehicle aggregator, day-ahead optimization schedule, real-time optimization schedule

show abstract

Section: Introductionmentioning

confidence: 99%

Optimal Aggregation Design for Massive V2G Participation in Energy Market

Jin

Seo

et al. 2020

IEEE Access

View full text Add to dashboard Cite

show abstract

“…Millo et al, 2014). Li and Jiao (2020) proposed a control strategy to fast and smoothly start the engine for a better drivability, Wang et al (2015) proposed a control algorithm for proper engine start and stop while keeping the engine working in the high efficient area, and Ye et al (2019) proposed an engine start control strategy in decreasing the gear lash to reduce the fuel consumption. Almost all the studies focusing on the engine start expose that when the engine is asked to be started, the clutch switches from a disengagement state to an engagement state.…”

Section: Introductionmentioning

confidence: 99%

Model predictive control-based control strategy to reduce driving-mode switching times for parallel hybrid electric vehicle

Song

2020

Transactions of the Institute of Measurement and Control

View full text Add to dashboard Cite

Multi-power sources are included in hybrid electrical vehicles, which leads to multi-driving modes co-existing when driving the vehicle. However, the frequent driving mode switching (DMS) will probably need the engine to be started frequently, which can result in extra fuel consumption. So, avoiding unnecessary DMS should be fully considered when designing the control strategy. For solving this problem, a model predictive control (MPC) strategy integrating Markov chain driving intention identification is put forward. First, the component models of the powertrain system are established. Second, according to the real driving cycle data, a driving intention model based on the Markov chain is designed according to the real driving cycle data. Then the MPC-based control strategy aiming at reducing DMS times is proposed by integrating the cost of DMS. Finally, the proposed control strategy is contrasted with three other control strategies to verify its validity in reducing the mode switching times and improving the fuel economy.

show abstract

The fastest stabilization of second-order switched systems with all modes unstable via an optimal state-dependent switching rule revisited

Yuan

Zhou

2023

Applied Mathematics and Computation

View full text Add to dashboard Cite

Research on Dynamic Coordination Active Mode Switching Control Strategy for Hybrid Electric Vehicle Based on Traffic Information

Cited by 6 publications

References 26 publications

Optimal Aggregation Design for Massive V2G Participation in Energy Market

Optimal Aggregation Design for Massive V2G Participation in Energy Market

Model predictive control-based control strategy to reduce driving-mode switching times for parallel hybrid electric vehicle

The fastest stabilization of second-order switched systems with all modes unstable via an optimal state-dependent switching rule revisited

Contact Info

Product

Resources

About