Optimal Power Management for a Hydraulic Hybrid Delivery Truck

Wu, Bin; Lin, C.C.; Filipi, Zoran; Peng, Huei; Assanis, Dennis N.

doi:10.1080/00423110412331291562

Cited by 190 publications

(140 citation statements)

References 10 publications

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“…Different alternatives are described, with electric hybridization , Bauman et al 2008, Khaligh et Zhihao 2010, Garcia et al 2004, Araujo et al 2014, Thoungtong et al 2009, Camara et al 2008, Li et al 2012, M. Esfahanian et al 2013, D.Chindamo et al 2013) and hydraulic hybridization (Baseley et al 2007, Filipi and Kim 2010, Kim and Filipi 2007, Wu et al 2002, Wohlgemuth 2013, Surampudi 2009) being the most typical ones.…”

Section: *Corresponding Author E-mail: Fsoriano@rosrocacommentioning

confidence: 99%

Topological analysis of powertrains for refuse-collecting vehicles based on real routes–Part II: Hybrid electric powertrain

Soriano¹,

Moreno-Eguilaz

Álvarez

et al. 2016

Int.J Automot. Technol.

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ABSTRACT-In this two-part paper, a topological analysis of powertrains for refuse-collecting vehicles (RCVs) based on simulation of different architectures (internal combustion engine, hybrid electric, and hybrid hydraulic) on real routes is proposed. In this second part, three different hybrid electric powertrain architectures are proposed and modeled. These architectures are based on the use of fuel cells, ultracapacitors, and batteries. A calculation engine, which is specifically designed to estimate energy consumption, respecting the original performance as the original internal combustion engine (ICE), is presented and used for simulations and component sizing. Finally, the overall performance of the different architectures (hybrid hydraulic, taken from the first paper part, and hybrid electric, estimated in this second part) and control strategies are summarized in a fuel and energy consumption table. Based on this table, an analysis of the different architecture performance results is carried out. From this analysis, a technological evolution of these vehicles in the medium-and long terms is proposed.

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Section: *Corresponding Author E-mail: Fsoriano@rosrocacommentioning

confidence: 99%

Topological analysis of powertrains for refuse-collecting vehicles based on real routes–Part II: Hybrid electric powertrain

Soriano¹,

Moreno-Eguilaz

Álvarez

et al. 2016

Int.J Automot. Technol.

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“…[8] showed that by considering the reference value for the accumulator's pressure as a function of vehicle velocity demand, the modulated-pressure control strategy produces an improvement in fuel economy. A schematic for this control strategy is shown in Figure 6.…”

Section: Thermostatic Control Strategymentioning

confidence: 99%

“…The thermostatic control approach is traditionally utilized for series hybrid electric vehicles (HEVs) and adopted for HHVs [6,8]. The concept of a thermostatic control algorithm is shown in Figure 5.…”

Section: Thermostatic Control Strategymentioning

confidence: 99%

The Development of an Optimal Control Strategy for a Series Hydraulic Hybrid Vehicle

Hung¹,

Vu²,

Chen³

2016

Applied Sciences

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Abstract:In this work, a Truck Class II series hydraulic hybrid model is established. Dynamic Programming (DP) methodology is applied to derive the optimal power-splitting factor for the hybrid system for preselected driving schedules. Implementable rules are derived by extracting the optimal trajectory features from a DP scheme. The system behaviors illustrate that the improved control strategy gives a highly effective operation region for the engine and high power density characteristics for the hydraulic components.

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“…The powertrain system simulation has been validated with vehicle test data from proving ground [25] and subsequently thoroughly updated to represent HMMWV [26]. The hydraulic components were added to the vehicle simulation in context of parallel [27] and series hybrid systems [24]. Table 1 gives the specification of the vehicle and powertrain.…”

Section: Hybrid Vehicle Supervisory Controller Problemmentioning

confidence: 99%

Self-Learning Neural Controller for Hybrid Power Management Using Neuro-Dynamic Programming

Johri

Filipi

2011

SAE Technical Paper Series

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A supervisory controller strategy for a hybrid vehicle coordinates the operation of the two power sources onboard of a vehicle to maximize objectives like fuel economy. In the past, various control strategies have been developed using heuristics as well as optimal control theory. The Stochastic Dynamic Programming (SDP) has been previously applied to determine implementable optimal control policies for discrete time dynamic systems whose states evolve according to given transition probabilities. However, the approach is constrained by the curse of dimensionality, i.e. an exponential increase in computational effort with increase in system state space, faced by dynamic programming based algorithms. This paper proposes a novel approach capable of overcoming the curse of dimensionality and solving policy optimization for a system with very large design state space. We propose developing a supervisory controller for hybrid vehicles based on the principles of reinforcement learning and neuro-dynamic programming, whereby the costto-go function is approximated using a neural network. The controller learns and improves its performance over time. The simulation results obtained for a series hydraulic hybrid vehicle over a driving schedule demonstrate the effectiveness of the proposed technique.

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Optimal Power Management for a Hydraulic Hybrid Delivery Truck

Cited by 190 publications

References 10 publications

Topological analysis of powertrains for refuse-collecting vehicles based on real routes–Part II: Hybrid electric powertrain

Topological analysis of powertrains for refuse-collecting vehicles based on real routes–Part II: Hybrid electric powertrain

The Development of an Optimal Control Strategy for a Series Hydraulic Hybrid Vehicle

Self-Learning Neural Controller for Hybrid Power Management Using Neuro-Dynamic Programming

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