An Energy Management Controller to Optimally Trade Off Fuel Economy and Drivability for Hybrid Vehicles

Opila, Daniel F.; Wang, Xiaoyong; McGee, Ryan; Gillespie, R. Brent; Cook, J.A.; Grizzle, Jessy W.

doi:10.1109/tcst.2011.2168820

Cited by 159 publications

(109 citation statements)

References 39 publications

(80 reference statements)

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“…The optimal torque control strategy will impose the engine's speed to follow the electric motor's speed fluctuation while at the same time operating at optimal torque for that rotating speed to ensure good efficiency. It has a good transient operation than other control strategies, but the battery charge will deplete faster and shorten the car's autonomy (Opila et al, 2012;Moura et al, 2011).…”

Section: Hev Control Methodsmentioning

confidence: 99%

Fuel consumption evaluation of a hybrid electric car over aggressive cycles for thermal engine optimization

Asus

2018

Int. j. adv. appl. sci.

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This paper investigates fuel consumption of a thermal engine in a hybrid electric vehicle system over aggressive cycles in order to optimize its energy consumption. The model of the system is first developed using data obtained from experiments over two aggressive driving cycles and then used to validate the model and further used as a platform to test other control strategies. The car's actual control strategy operates on high torque region of the engine to sustain battery charge and caused high fuel consumption. Based on previous researches, there are two optimal control strategies that can be implemented for a series hybrid electric vehicle system; the dynamic programming control method and the optimal torque control method. Result analysis shows that the operations of the engine are different between the two control methods; it is concentrated on high speed region for the dynamic programming control method and at the middle of the engine map for the optimal torque control method.

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Section: Hev Control Methodsmentioning

confidence: 99%

Fuel consumption evaluation of a hybrid electric car over aggressive cycles for thermal engine optimization

Asus

2018

Int. j. adv. appl. sci.

View full text Add to dashboard Cite

show abstract

“…The motor efficiency can be written as Equation (5), which is a function of speed and torque. The maximum and minimum torque is a function of speed as shown in Equations (6) and (7). The relationship between the actual motor torque and the required torque is described as Equation (8).…”

Section: Electrical Machine Modelmentioning

confidence: 99%

“…However, DP cannot be implemented directly on a real vehicle because it is impossible to know the specific driving conditions in advance (e.g., speed, road slope, etc.). To address this problem, a stochastic dynamic programming (SDP) algorithm is proposed by establishing a driver power demand sequence over different driving cycles based on the Markov chain to obtain a state transfer matrix of the driver's power demand [6,7]. However, SDP presents computational issues in real-time applications.…”

Section: Introductionmentioning

confidence: 99%

A Supervisory Control Algorithm of Hybrid Electric Vehicle Based on Adaptive Equivalent Consumption Minimization Strategy with Fuzzy PI

Zhang

Liu

et al. 2016

Energies

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This paper presents a new energy management system based on equivalent consumption minimization strategy (ECMS) for hybrid electric vehicles. The aim is to enhance fuel economy and impose state of charge (SoC) charge-sustainability. First, the relationship between the equivalent factor (EF) of ECMS and the co-state of pontryagin's minimum principle (PMP) is derived. Second, a new method of implementing the adaptation law using fuzzy proportional plus integral (PI) controller is developed to adjust EF for ECMS in real-time. This adaptation law is more robust than one with constant EF due to the variation of EF as well as driving cycle. Finally, simulations for two driving cycles using ECMS are conducted as opposed to the commonly used rule-based (RB) control strategy, indicating that the proposed adaptation law can provide a promising blend in terms of fuel economy and charge-sustainability. The results confirm that ECMS with Fuzzy PI adaptation law is more robust than ECMS with constant EF as well as PI adaptation law and it achieves significant improvements compared with RB in terms of fuel economy, which is enhanced by 4.44% and 14.7% for china city bus cycle and economic commission of Europe (ECE) cycle, respectively.

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“…These indices reflect a vehicle's powertrain response characteristics to the driver's maneuvers: sluggishness, surge and oscillation [10]. The most commonly used vehicle powertrain response indices are the duration and the responsiveness and smoothness of the vehicle's longitudinal acceleration [11,12]. Given that the external resistance can be considered to be constant during the mode transition, the responsiveness and smoothness of the vehicle's longitudinal acceleration are equivalent to those of the propulsion torque exerted on the drivetrain by the hybrid power source.…”

Section: Introductionmentioning

confidence: 99%

Drivability-Related Discrete-Time Model Predictive Control of Mode Transition in Pre-Transmission Parallel Hybrid Powertrains

Guo

Yan

2016

Energies

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During the mode transition from the pure electric propulsion mode to the hybrid propulsion mode, clutch-based pre-transmission parallel hybrid electric vehicles are subject to drivability issues. These issues originate from the fact that in the clutch-based pre-transmission parallel hybrid powertrain (CPPHP) configuration, the clutch connects the engine and the motor. Without a carefully designed mode transition control that coordinates the engine torque, clutch torque and motor torque, torque sluggishness and surges occur during the mode transition, and residual torque oscillation occurs after the mode transition. In this paper, a discrete-time model predictive control (DMPC)-based controller is proposed to address these drivability-related issues. Modeling improvements and novel drivability-related indices and constraints are all taken into consideration in the design of the discrete-time model predictive controller. Furthermore, by using discrete-time Laguerre functions and introducing the equilibrium state and the ranking of constraints, an explicit solution of the discrete-time model predictive controller is obtained. The calculation results demonstrate that the proposed controller can ensure a smooth and rapidly decaying torque difference during the mode transition, alleviating the residual torque oscillation after the mode transition and guaranteeing that the mode transition is completed within an acceptable duration.

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An Energy Management Controller to Optimally Trade Off Fuel Economy and Drivability for Hybrid Vehicles

Cited by 159 publications

References 39 publications

Fuel consumption evaluation of a hybrid electric car over aggressive cycles for thermal engine optimization

Fuel consumption evaluation of a hybrid electric car over aggressive cycles for thermal engine optimization

A Supervisory Control Algorithm of Hybrid Electric Vehicle Based on Adaptive Equivalent Consumption Minimization Strategy with Fuzzy PI

Drivability-Related Discrete-Time Model Predictive Control of Mode Transition in Pre-Transmission Parallel Hybrid Powertrains

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