Supervisory Power Management Control Algorithms for Hybrid Electric Vehicles: A Survey

Malikopoulos, Andreas A.

doi:10.1109/tits.2014.2309674

Cited by 194 publications

(88 citation statements)

References 142 publications

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“…In contrast, the EV mode runs using the EM when the battery has a higher SOC. This is a basic rule-based power management strategy, which is also called the CD-CS strategy [20,21].…”

Section: Hev Mode Transitionmentioning

confidence: 99%

A Control Strategy for Mode Transition with Gear Shifting in a Plug-In Hybrid Electric Vehicle

Sim¹,

Oh²,

Kang³

et al. 2017

Energies

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Abstract:The mode transition from electric propulsion mode to hybrid propulsion mode is important with regard to the power management strategy of plug-in hybrid electric vehicles (PHEVs). This is because mode transitions can occur frequently depending on the power management strategies and driving cycles, and because inadequate mode transitions worsen the fuel efficiency and drivability. A pre-transmission parallel PHEV uses a clutch between the internal combustion engine (ICE) and the electric motor (EM) to connect or disconnect the power source of the ICE for a mode transition. The mode transition requires additional energy consumption for clutch speed synchronization, and is accompanied by a drivetrain shock due to clutch engagement. This paper proposes a control strategy for the mode transition with gear-shifting to resolve the problems of energy consumption and drivetrain shock. Through the development of a PHEV performance simulator, we analyze the mode transition characteristics and propose a control strategy considering the vehicle acceleration and gear state. The control strategy reduces the duration required for the mode transition by moving the start time of the mode transition. This helps to improve energy efficiency while maintaining adequate drivability.

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“…In contrast, the EV mode runs using the EM when the battery has a higher SOC. This is a basic rule-based power management strategy, which is also called the CD-CS strategy [20,21].…”

Section: Hev Mode Transitionmentioning

confidence: 99%

A Control Strategy for Mode Transition with Gear Shifting in a Plug-In Hybrid Electric Vehicle

Sim¹,

Oh²,

Kang³

et al. 2017

Energies

View full text Add to dashboard Cite

show abstract

“…Hao et al [23] employed the DIvided RECTangle (DIRECT) algorithm to optimize the extracted seven key parameters based on the optimization model of the key parameters from the perspective of fuel economy. More other control algorithms for HEVs can be referred to [24,25].…”

Section: Introductionmentioning

confidence: 99%

Control Strategy Optimization for Parallel Hybrid Electric Vehicles Using a Memetic Algorithm

Cheng

Lai

2017

Energies

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Abstract:Hybrid electric vehicle (HEV) control strategy is a management approach for generating, using, and saving energy. Therefore, the optimal control strategy is the sticking point to effectively manage hybrid electric vehicles. In order to realize the optimal control strategy, we use a robust evolutionary computation method called a "memetic algorithm (MA)" to optimize the control parameters in parallel HEVs. The "local search" mechanism implemented in the MA greatly enhances its search capabilities. In the implementation of the method, the fitness function combines with the ADvanced VehIcle SimulatOR (ADVISOR) and is set up according to an electric assist control strategy (EACS) to minimize the fuel consumption (FC) and emissions (HC, CO, and NO x ) of the vehicle engine. At the same time, driving performance requirements are also considered in the method. Four different driving cycles, the new European driving cycle (NEDC), Federal Test Procedure (FTP), Economic Commission for Europe + Extra-Urban driving cycle (ECE + EUDC), and urban dynamometer driving schedule (UDDS) are carried out using the proposed method to find their respectively optimal control parameters. The results show that the proposed method effectively helps to reduce fuel consumption and emissions, as well as guarantee vehicle performance.

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“…However, the prediction of MPC is usually computed for a relatively short time horizon in the future due to its computational cost. In recent years, MPC has been used for hybrid electric vehicle (HEV) and plug-in HEV (PHEV) applications, as shown in the comprehensive survey of power management topics [6]. In [6], dynamic programming-based MPC relies on dynamic models of the vehicle, most often linear empirical models obtained by system identification [25].…”

Section: Introductionmentioning

confidence: 99%

“…In recent years, MPC has been used for hybrid electric vehicle (HEV) and plug-in HEV (PHEV) applications, as shown in the comprehensive survey of power management topics [6]. In [6], dynamic programming-based MPC relies on dynamic models of the vehicle, most often linear empirical models obtained by system identification [25]. In the references cited above, the significance of a MPC controller for HEV applications has been highlighted.…”

Section: Introductionmentioning

confidence: 99%

High-Fidelity Battery Model for Model Predictive Control Implemented into a Plug-In Hybrid Electric Vehicle

et al. 2017

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Abstract:Power management strategies have impacts on fuel economy, greenhouse gasses (GHG) emission, as well as effects on the durability of power-train components. This is why different off-line and real-time optimal control approaches are being developed. However, real-time control seems to be more attractive than off-line control because it can be directly implemented for managing power and energy flows inside an actual vehicle. One interesting illustration of these power management strategies is the model predictive control (MPC) based algorithm. Inside a MPC, a cost function is optimized while system constraints are validated in real time. The MPC algorithm relies on dynamic models of the vehicle and the battery. The complexity and accuracy of the battery model are usually neglected to benefit the development of new cost functions or better MPC algorithms. The contribution of this manuscript consists of developing and evaluating a high-fidelity battery model of a plug-in hybrid electric vehicle (PHEV) that has been used for MPC. Via empirical work and simulation, the impact of a high-fidelity battery model has been evaluated and compared to a simpler model in the context of MPC. It is proven that the new battery model reduces the absolute voltage, state of charge (SoC), and battery power loss error by a factor of 3.2, 1.9 and 2.1 on average respectively, compared to the simpler battery model.

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Supervisory Power Management Control Algorithms for Hybrid Electric Vehicles: A Survey

Cited by 194 publications

References 142 publications

A Control Strategy for Mode Transition with Gear Shifting in a Plug-In Hybrid Electric Vehicle

A Control Strategy for Mode Transition with Gear Shifting in a Plug-In Hybrid Electric Vehicle

Control Strategy Optimization for Parallel Hybrid Electric Vehicles Using a Memetic Algorithm

High-Fidelity Battery Model for Model Predictive Control Implemented into a Plug-In Hybrid Electric Vehicle

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