Design of an energy management strategy for parallel hybrid electric vehicles using a logic threshold and instantaneous optimization method

Huang, Yuangjun; Yin, Chengliang; Zhang, J. W.

doi:10.1007/s12239-009-0059-4

Cited by 48 publications

(23 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Specifically speaking, compared with rule-based control strategy [32,33], the DP control strategy ratio of fuel cost is much lower: a reduction of almost 13.17% in fuel cost compared with ruled-based control strategy in the same CFDC at non-rush hour. This result proved control strategy based on DP has the best performance In rule-based control strategy, the fuel efficiency at rush hour is much more efficient than at non-rush hour (fuel cost 11.57% higher than DP compared with fuel cost 14.37% higher than DP).…”

Section: Global Optimal Energy Managementmentioning

confidence: 99%

Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles

Guo

Zhou

et al. 2017

Energies

View full text Add to dashboard Cite

This paper presents a freeway driving cycle (FDC) construction method based on traffic information. A float car collected different type of roads in California and we built a velocity fragment database. We selected a real freeway driving cycle (RFDC) and established the corresponding time traffic information tensor model by using the data in California Department of Transportation performance measure system (PeMS). The correlation of road velocity in the time dimension and spatial dimension are analyzed. According to the average velocity of road sections at different times, the kinematic fragments are stochastically selected in the velocity fragment database to construct a real-time FDC of each section. The comparison between construction freeway driving cycle (CFDC) and real freeway driving cycle (RFDC) show that the CFDC well reflects the RFDC characteristic parameters. Compared to its application in plug-in electric hybrid vehicle (PHEV) optimal energy management based on a dynamic programming (DP) algorithm, CFDC and RFDC fuel consumption are similar within approximately 5.09% error, and non-rush hour fuel economy is better than rush hour 3.51 (L/100 km) at non-rush hour, 4.29 (L/km) at rush hour)). Moreover, the fuel consumption ratio can be up to 13.17% in the same CFDC at non-rush hour.

show abstract

Section: Global Optimal Energy Managementmentioning

confidence: 99%

Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles

Guo

Zhou

et al. 2017

Energies

View full text Add to dashboard Cite

show abstract

“…During the actual operating process, the operating modes are determined by energy control strategy according to conditions such as the driving torque, the current operating mode of engine, battery SOC b , accumulator SOC acc , etc. [19,20].…”

Section: Energy Control Strategy For Hessmentioning

confidence: 99%

Hydraulic/electric synergy system (HESS) design for heavy hybrid vehicles

Sun

Lifu

Junqing

2010

Energy

View full text Add to dashboard Cite

“…However, they also discovered that only the average travel information is needed and the high level of detail of travel conditions is not needed. Huang et al [14] used a threshold logic control strategy that is optimized continuously during operation to control a PHEV. Lee et al [15] developed neuro-fuzzy technique to analyze and learn GPS data and control the hybrid vehicle to improve fuel economy and reduce emissions.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Energy Management Control for Hybrid Electric Powertrains

Zaher

Cetinkunt

2013

Journal of Control Science and Engineering

View full text Add to dashboard Cite

This paper focuses on embedded control of a hybrid powertrain concepts for mobile vehicle applications. Optimal robust control approach is used to develop a real-time energy management strategy. The main idea is to store the normally wasted mechanical regenerative energy in energy storage devices for later usage. The regenerative energy recovery opportunity exists in any condition where the speed of motion is in the opposite direction to the applied force or torque. This is the case when the vehicle is braking, decelerating, the motion is driven by gravitational force, or load driven. There are three main concepts for energy storing devices in hybrid vehicles: electric, hydraulic, and mechanical (flywheel). The real-time control challenge is to balance the system power demands from the engine and the hybrid storage device, without depleting the energy storage device or stalling the engine in any work cycle. In the worst-case scenario, only the engine is used and the hybrid system is completely disabled. A rule-based control algorithm is developed and is tuned for different work cycles and could be linked to a gain scheduling algorithm. A gain scheduling algorithm identifies the cycle being performed by the work machine and its position via GPS and maps both of them to the gains.

show abstract

Design of an energy management strategy for parallel hybrid electric vehicles using a logic threshold and instantaneous optimization method

Cited by 48 publications

References 8 publications

Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles

Freeway Driving Cycle Construction Based on Real-Time Traffic Information and Global Optimal Energy Management for Plug-In Hybrid Electric Vehicles

Hydraulic/electric synergy system (HESS) design for heavy hybrid vehicles

Real-Time Energy Management Control for Hybrid Electric Powertrains

Contact Info

Product

Resources

About