Optimal Energy Management Strategy for a Plug-in Hybrid Electric Vehicle Based on Road Grade Information

Liu, Yonggang; Li, Jie; Ye, Ming; Qin, Datong; Zhang, Yi; Lei, Zhenzhen

doi:10.3390/en10040412

Cited by 28 publications

(14 citation statements)

References 30 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The development and large-scale application of electrified vehicles in recent years have been a surging trend worldwide to better cope with the increasing air pollution and energy shortage as well as to achieve the technological upgrading of automotive sector. Particularly, the plug-in hybrid electric bus, which can improve the fuel economy, reduce the exhaust emissions, and also overcome the range anxiety during long-distance trips, has been extensively applied in the public transport field and exhibited a promising future [1,2].…”

Section: Ground and Literature Reviewmentioning

confidence: 99%

A Pontryagin Minimum Principle-Based Adaptive Equivalent Consumption Minimum Strategy for a Plug-in Hybrid Electric Bus on a Fixed Route

Xie

Xin

et al. 2017

Energies

View full text Add to dashboard Cite

When developing a real-time energy management strategy for a plug-in hybrid electric vehicle, it is still a challenge for the Equivalent Consumption Minimum Strategy to achieve near-optimal energy consumption, because the optimal equivalence factor is not readily available without the trip information. With the help of realistic speeding profiles sampled from a plug-in hybrid electric bus running on a fixed commuting line, this paper proposes a convenient and effective approach of determining the equivalence factor for an adaptive Equivalent Consumption Minimum Strategy. Firstly, with the adaptive law based on the feedback of battery SOC, the equivalence factor is described as a combination of the major component and tuning component. In particular, the major part defined as a constant is applied to the inherent consistency of regular speeding profiles, while the second part including a proportional and integral term can slightly tune the equivalence factor to satisfy the disparity of daily running cycles. Moreover, Pontryagin's Minimum Principle is employed and solved by using the shooting method to capture the co-state dynamics, in which the Secant method is introduced to adjust the initial co-state value. And then the initial co-state value in last shooting is taken as the optimal stable constant of equivalence factor. Finally, altogether ten successive driving profiles are selected with different initial SOC levels to evaluate the proposed method, and the results demonstrate the excellent fuel economy compared with the dynamic programming and PMP method.

show abstract

Section: Ground and Literature Reviewmentioning

confidence: 99%

A Pontryagin Minimum Principle-Based Adaptive Equivalent Consumption Minimum Strategy for a Plug-in Hybrid Electric Bus on a Fixed Route

Xie

Xin

et al. 2017

Energies

View full text Add to dashboard Cite

show abstract

“…Owing to regulations on fuel economy and emissions become more and more stringent, the development of electrified vehicles in recent years have been a surging trend [1,2]. Plug-in hybrid electric vehicles (PHEVs), which achieve a longer all-electric range compared to conventional hybrid electric vehicles and have no driver range anxiety compared to pure battery electric vehicles, become an important research direction in the field of electric vehicles [3,4].…”

Section: Introductionmentioning

confidence: 99%

Multi-Objective Optimization for Plug-In 4WD Hybrid Electric Vehicle Powertrain

et al. 2019

View full text Add to dashboard Cite

This paper focuses on the parameter optimization for the CVT (a continuously variable transmission) based plug-in 4WD (4-wheel drive) hybrid electric vehicle powertrain. First, the plug-in 4WD hybrid electric vehicle (plug-in 4WD HEV)’s energy management strategy based on the CD (charge depleting) and CS (charge sustain) mode is developed. Then, the multi-objective optimization’s mathematical model, which aims at minimizing the electric energy consumption under the CD stage, the fuel consumption under the CS stage and the acceleration time from 0–120 km/h, is established. Finally, the multi-objective parameter optimization problem is solved using an evolutionary based non-dominated sorting genetic algorithms-II (NSGA-II) approach. Some of the results are compared with the original scheme and the classical weight approach. Compared with the original scheme, the best compromise solution (i.e., electric energy consumption, fuel consumption and acceleration time) obtained using the NSGA-II approach are reduced by 1.21%, 6.18% and 5.49%, respectively. Compared with the weight approach, the Pareto optimal solutions obtained using NSGA-II approach are better distributed over the entire Pareto optimal front, as well as the best compromise solution is also better.

show abstract

“…As far as non-traditional fuel vehicles are concerned, scholars have conducted in-depth research on the power and economy of powertrain systems; considered the energy-based design of powertrains [4]; developed shifting control of automated mechanical transmissions without a clutch based on dynamic 2 of 21 coordination [5]; and designed control strategies based on road grade information [6] and multiple objectives [7], online learning of a hybrid electric vehicle control [8], a hybrid electric vehicle optimal control strategy [9], etc. However, few have studied the active control of vehicle power systems based on the vehicle cluster environment.…”

Section: Introductionmentioning

confidence: 99%

Active Control and Validation of the Electric Vehicle Powertrain System Using the Vehicle Cluster Environment

Long

Sui

et al. 2019

Energies

Self Cite

View full text Add to dashboard Cite

With the development of intelligent vehicle technologies, vehicles can obtain more and more information from various sensors. Many researchers have focused on the vertical and horizontal relationships between vehicles in a vehicle cluster environment and control of the vehicle power system. When the vehicle is driving in the cluster environment, the powertrain system should quickly respond to the driver’s dynamic demand, so as to achieve the purpose of quickly passing through the cluster environment. The vehicle powertrain system should be regarded as a separate individual to research its active control strategy in a vehicle cluster environment to improve the control effect. In this study, the driving characteristics of vehicles in a cluster environment have been analyzed, and a vehicle power-demanded prediction algorithm based on a vehicle-following model has been proposed in a cluster environment. Based on the vehicle power demand forecast and driver operation, an active control strategy of the vehicle powertrain system has been designed considering the passive control strategy of the powertrain system. The results show that the vehicle powertrain system can ensure a sufficient backup power with the active control proposed in the paper, and the motor efficiency is improved by 0.61% compared with that of the passive control strategy. Moreover, the overall efficiency of the powertrain system is increased by 0.6% and the effectiveness of the active control is validated using the vehicle cluster environment.

show abstract

Optimal Energy Management Strategy for a Plug-in Hybrid Electric Vehicle Based on Road Grade Information

Cited by 28 publications

References 30 publications

A Pontryagin Minimum Principle-Based Adaptive Equivalent Consumption Minimum Strategy for a Plug-in Hybrid Electric Bus on a Fixed Route

A Pontryagin Minimum Principle-Based Adaptive Equivalent Consumption Minimum Strategy for a Plug-in Hybrid Electric Bus on a Fixed Route

Multi-Objective Optimization for Plug-In 4WD Hybrid Electric Vehicle Powertrain

Active Control and Validation of the Electric Vehicle Powertrain System Using the Vehicle Cluster Environment

Contact Info

Product

Resources

About