Benchmarking Hybrid Concepts: On-Line vs. Off-Line Fuel Economy Optimization for Different Hybrid Architectures

Dingel, Oliver; Pini, Nicola; Trivic, Igor; Ross, Joerg; Cavina, Nicolò; Cerofolini, Alberto; Rioli, Mauro

doi:10.4271/2013-24-0084

Cited by 6 publications

(8 citation statements)

References 9 publications

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“…[12][13][14][15][16][17][18] However, there are only few cases where DP has been applied for FW-based mechanical HVs and all these cases are exclusively for flywheel-based internal-combustion engine hybrid vehicles (FWICEHVs). [19][20][21] Jamzadeh and Frank 19 applied DP to find the optimal control policy for an FWICEHV over the Federal Urban Driving Cycle . In this case the internal-combustion engine (ICE) and the FW are coupled to the CVT, and the driver controls the vehicle torque by the CVT and has no control over the engine operation.…”

Section: Energy Management Strategymentioning

confidence: 99%

“…The speeds of the ICE and the FW are linked and there is no mode of operation where the FW and the ICE simultaneously motor the vehicle. Dingel et al 21 used DP to benchmark and compare the fuel savings for an HEV and an FWICEHV. It has been recognised by Van Berkel et al 20 and Dingel et al 21 that, unlike the case for an HEV, there is no unequivocal approach for applying DP to an FW-based mechanical HV and the process is more complex than for an HEV owing to many factors including the relatively many kinematic constraints, the small energy capacity of the FW and the slipping clutches.…”

Section: Energy Management Strategymentioning

confidence: 99%

“…In this case the ICE and the FW are connected using clutches and the CVT is downstream. 21 that unlike for an HEV, there is no univocal approach for applying DP to a FW based mechanical HV and the process is more complex than for an HEV due to many factors including the relatively many kinematic constraints, small energy capacity of the FW and slipping clutches. In case of a mechanically connected FWBEV, the optimization using DP is further complicated due to the fact that both the battery and the FW have state variables associated with them and there are additional options for achieving specific functions.…”

Section: Energy Management Strategymentioning

confidence: 99%

“…However, there are only few cases of DP being applied for FW based mechanical hybrid vehicles and all these cases are exclusively for FW based internal combustion engine hybrid vehicles (FWICEHV) [19][20][21] . Jamzadeh 19 applied DP to find the optimal control policy for a FWICEHV on the federal urban drive cycle (FUDC).…”

Section: Energy Management Strategymentioning

confidence: 99%

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Optimal energy management for a flywheel-assisted battery electric vehicle

Dhand

Pullen

2015

Proceedings of the Institution of Mechanical Engineers, Part D:

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This is the accepted version of the paper.This version of the publication may differ from the final published version. Permanent AbstractBattery electric vehicles (BEV) are crucial to the reduction of dependence on fossil fuels and for moving towards a zero emission transport system. Though BEV technology has been rapidly improving, the limited driving range and high cost are significant impediments to the popularity of electric vehicles. The battery is the main element which affects the range and cost of the vehicle. The batteries can provide either high power or high energy but not both. Hybridization of the energy source is one of the methods to improve the energy efficiency of the vehicle, which would involve combining a high energy battery with a high power source. High speed flywheels (FW) have attractive properties and low cost potential which makes them excellent secondary energy storage devices to be used in hybrid and electric vehicles. They are utilized to load level the battery so as to protect it from peak loads and enhance its capacity and life. The flywheel is coupled to the drive line with a continuous variable transmission (CVT). This paper presents the optimal energy management strategy (EMS) for a mechanically connected flywheel assisted BEV (FWBEV) powertrain. The optimization problem is complex due to factors such as the small storage capacity of flywheel, kinematic constraints and slipping of clutches. Dynamic programming is used to calculate the optimal control strategy for torque distribution during operation in real world driving cycles. The results show significant potential for reduction of energy consumption in extra-urban and highway cycles, while reducing battery peak loads during all cycles. The results give a benchmark of the energy saving potential for such a powertrain and insights into how a real sub-optimal controller can be designed.

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Section: Energy Management Strategymentioning

confidence: 99%

Section: Energy Management Strategymentioning

confidence: 99%

Section: Energy Management Strategymentioning

confidence: 99%

Section: Energy Management Strategymentioning

confidence: 99%

See 2 more Smart Citations

Optimal energy management for a flywheel-assisted battery electric vehicle

Dhand

Pullen

2015

Proceedings of the Institution of Mechanical Engineers, Part D:

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“…The improvements in fuel economy and the reductions in emissions of hybrid electric vehicles (HEV) mainly depend upon the energy management strategy (EMS); therefore, substantial research efforts have been carried out. The research methods can be classified into three categories: first, rulebased controls such as logic threshold control and finite state machine [1,2]; second, intelligent control algorithms such as model predictive control [3], fuzzy logic [4,5], and neural networks [6]; third, optimal theory methods such as minimum theory, deterministic dynamic programming (DP) [7,8], and stochastic DP (SDP) [9]. Rule-based control strategy is also named as the baseline control, which is a steady state optimization method through engineering experience and a simple analysis of the efficiencies of components such as engine, motor, and battery.…”

Section: Introductionmentioning

confidence: 99%

The Development and Verification of a Novel ECMS of Hybrid Electric Bus

Wang¹,

Wang

et al. 2014

Mathematical Problems in Engineering

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This paper presents the system modeling, control strategy design, and hardware-in-the-loop test for a series-parallel hybrid electric bus. First, the powertrain mathematical models and the system architecture were proposed. Then an adaptive ECMS is developed for the real-time control of a hybrid electric bus, which is investigated and verified in a hardware-in-the-loop simulation system. The ECMS through driving cycle recognition results in updating the equivalent charge and discharge coefficients and extracting optimized rules for real-time control. This method not only solves the problems of mode transition frequently and improves the fuel economy, but also simplifies the complexity of control strategy design and provides new design ideas for the energy management strategy and gear-shifting rules designed. Finally, the simulation results show that the proposed real-time A-ECMS can coordinate the overall hybrid electric powertrain to optimize fuel economy and sustain the battery SOC level.

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