Modeling and Design of Heavy Duty Hybrid Electric Vehicles

Pisu, Pierluigi; Hubert, Christopher J.; Dembski, Nicholas; Rizzoni, Giorgio; Josephson, John R.; Russell, J. D.; Carroll, Mark

doi:10.1115/imece2005-81514

Cited by 9 publications

(3 citation statements)

References 5 publications

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“…The proposed battery model was integrated with various subcomponents to complete the forward vehicle simulator (Pisu et al, 2005;Pisu et al, 2006) and a few selected driving cycles were tested upon for the component sizing optimization (Figure 11).…”

Section: Vehicle Simulation and Prototype Implementationmentioning

confidence: 99%

Optimized dynamic battery model suited for power based vehicle energy simulation

Guezennec

Choi

2011

Int.J Automot. Technol.

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Ever increasing demand for the petroleum is causing faster than expected oil shortages in the supply and demand balance around the world and furthermore, many specialists in the field of oil production such as Association for the Study of Peak Oil and World Energy Outlook are claiming that the petroleum is around the peak of its production ( Figure 1). Such shortage made the greatest impact on the gasoline price hikes at the gas pump and thus, this impact was felt by the consumers severely and became the greatest motivation for automotive industries to strive to pioneer the researches for the next generation vehicle configurations ranging from HEV, PHEV, Pure EV to FCHEV (collectively noted as xEV). While the great deal of researches has been carried over the last few decades, it is still far from mass productions for consumer use except for the HEV mainly due to the high cost involved with other types of xEV configurations. Therefore, it is critical to design the vehicle to maximize the use of each component at its highest point regardless of any cost scenarios and it is clear that this optimization can only be achieved through the accurate energy balance simulation for a specific target vehicle prior to the actual hardware implementation. In this paper, it is our intention to introduce modified dynamic battery modeling scheme that would provide a more accurate way of simulating the battery behavior when used in the vehicle energy simulation system. Starting from a typical battery dynamic model to predict the voltage given an imposed current request, we have introduced a new scheme to establish the relationship between the voltage and the power (rather than the current) requested by the vehicle simulation system. The proposed scheme handles the power request from the vehicle simulator considering the dynamic battery characteristics and in turn, contributes to the better estimation of the current integrated energy usage and battery SOC level in the given battery dynamic system used in the vehicle energy simulation system.

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Section: Vehicle Simulation and Prototype Implementationmentioning

confidence: 99%

Optimized dynamic battery model suited for power based vehicle energy simulation

Guezennec

Choi

2011

Int.J Automot. Technol.

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“…Two different approaches to the HEV modelling can be adopted: backwards or forward (with respect to the physical causality principles) (Pisu et al, 2005). In the forward approach the vehicle speed is a consequence of a torque delivered by the powertrain, in response to the demand generated by the driver model (usually a PID controller that compares the actual velocity with the desired value).…”

Section: Model Of the Hybrid Electric Vehiclementioning

confidence: 99%

“…In the backward approach, instead, no driver is necessary, since the vehicle speed is supposed known and the torque necessary to obtain it is computed by the model, as shown in Figure 2. Source: Pisu et al (2005) In this paper, a backward, quasi static simulator is used to implement the DP algorithm, because it allows to treat the vehicle speed as an external input rather than a dynamic state. As Figure 2 shows, the vehicle speed, defined by the driving cycle, is used to calculate the tractive force; through the driveline model, the torque request upstream of the gearbox is computed.…”

Section: Model Of the Hybrid Electric Vehiclementioning

confidence: 99%

Layered control strategies for hybrid electric vehicles based on optimal control

Bianchi¹,

Rolando²,

Serrao

et al. 2011

IJEHV

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Dynamic programming is known to provide the optimal solution to the energy management problem. However, it is not implementable online because it requires complete a-priori knowledge of the driving cycle and high computational requirements. This article presents a methodology to extract an implementable rule-based strategy from the dynamic programming results and thus build a near-optimal controller. The case study discussed in this paper focused on mode switching in a series/parallel hybrid vehicle, in which a clutch may be used to change the powertrain topology. Because of the complexity of the system, the controller is divided in two layers: the supervisory controller, which decides the powertrain configuration, and the energy management, which decides the power split. The process of deriving the rules from the optimal solution is described in detail. Then, the performance of the resulting rule-based strategy is studied and compared with the solution given by dynamic programming, which functions as a benchmark. Then another comparison is performed with respect to the equivalent consumption minimisation strategy (ECMS) which, if optimally tuned, can achieve optimal performance as close to DP as possible with the advantage of being implementable.

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Optimal control of power split for a hybrid electric refuse vehicle

Serrao

Rizzoni

2008

2008 American Control Conference

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Modeling and Design of Heavy Duty Hybrid Electric Vehicles

Cited by 9 publications

References 5 publications

Optimized dynamic battery model suited for power based vehicle energy simulation

Optimized dynamic battery model suited for power based vehicle energy simulation

Layered control strategies for hybrid electric vehicles based on optimal control

Optimal control of power split for a hybrid electric refuse vehicle

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