A-ECMS: An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management

Musardo, Cristian; Rizzoni, Giorgio; Staccia, Benedetto

doi:10.1109/cdc.2005.1582424

Cited by 568 publications

(175 citation statements)

References 3 publications

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“…Some researchers have conducted research on the ECMS for hybrid electric vehicles and have compared *Corresponding author. e-mail: gq.xu@siat.ac.cn the simulation results of the ECMS to those of the DP, which is a global optimal control law but which takes a long calculation time (Sciarretta et al, 2004;Musardo and Rizzoni, 2005). The ECMS simulation results were very close to those for the DP.…”

Section: Introductionsupporting

confidence: 54%

“…The ECMS is based on the concept that the usage of the electric energy can be exchanged to the equivalent fuel consumption. The equivalence between the electric energy and the fuel energy is basically evaluated by considering the average energy paths leading from the fuel to the storage of electric energy (Musardo and Rizzoni, 2005). At the beginning of the ECMS research (Paganelli et al, 2001;Paganelli et al, 2002), mean values of the primary power source (engine or fuel cell system), including mean power and mean fuel consumption rate, and mean efficiencies of powertrain components were used to calculate the equivalent fuel consumption of the battery.…”

Section: Introductionmentioning

confidence: 99%

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Numerical comparison of ECMS and PMP-based optimal control strategy in hybrid vehicles

Zheng

et al. 2014

Int.J Automot. Technol.

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During the last decade, the equivalent consumption minimization strategy (ECMS) and the Pontryagin's Minimum Principle (PMP)-based optimal control strategy have been developed for power management of hybrid vehicles, and it has been noticed that there are some similarities between the two strategies. Establishing their exact relationship and distinguishing their fundamental differences have become necessary for further development in the field of power management strategy. The two strategies are numerically compared and their relationship is established in this research. The two strategies are applied to a fuel cell hybrid vehicle (FCHV) in a computer simulation environment and the simulation results of the two strategies are also compared. It is concluded that the numerical comparison result depends on the opencircuit-voltage (OCV) of the battery model. As a result, the ECMS and the PMP-based optimal control strategy can numerically have the same solutions for non-plug-in hybrid vehicles by adjusting two parameters. Differences between the two strategies are also discussed and the superiority of the PMP-based optimal control strategy is emphasized.

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Section: Introductionsupporting

confidence: 54%

Section: Introductionmentioning

confidence: 99%

Numerical comparison of ECMS and PMP-based optimal control strategy in hybrid vehicles

Zheng

et al. 2014

Int.J Automot. Technol.

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“…Reviewing the existing literature, many different approaches are identified in energy management systems (EMS) for HEV power-split transmissions (Hofman et al, 2007;Musardo et al, 2005;Sciarretta et al, 2004;Pisu and Rizzoni, 2007), that can be classified in terms of the cycle knowledge: -No cycle knowledge: where HEV controllers include rules, based on engineering intuition and component efficiency maps that are intended to maximize vehicle efficiency -Full cycle knowledge: where HEV controllers achieve maximum fuel economy over a known driving cycle, by using an overall optimization techniques such as Dynamic Programming -Flexible forecasted knowledge about any cycle: where HEV controllers rely on route prediction from GPS and traffic information systems, to modify the control strategy All actual commercialized HEV controllers are able to manage the vehicle in real time, and with no prior cycle knowledge. They basically fall into the rule-based category.…”

Section: Ths-ii Energy Management Strategymentioning

confidence: 99%

Dynamic modeling of the electro-mechanical configuration of the Toyota Hybrid System series/parallel power train

Mansour

Clodic

2011

Int.J Automot. Technol.

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International audienceThe hybridization of the conventional thermal vehicles nowadays constitutes a paramount importance for car manufacturers, facing the challenge of minimizing the consumption of the road transport. Although hybrid power train technologies did not converge towards a single solution, series/parallel power trains with power-split electromechanical transmissions prove to be the most promising hybrid technology. In fact, these power trains show maximum power train overall efficiency and maximum fuel reduction in almost all driving conditions compared to the conventional and other hybrid power trains. This paper addresses the model and design of the electro-mechanical configuration of one of the most effective HEV power trains: case study of the 2nd generation Prius. It presents the simulation work of the overall operation of the Toyota Hybrid System (THS-II) of the Prius, and explores not only its power-split eCVT innovative transmission system but also its overall supervision controller for energy management. The kinematic and dynamic behaviors of the THS-II power train are explained based on the power-split aspect of its transmission through a planetary gear train. Then, the possible regular driving functionalities that result from its eCVT operation and the energy flow within its power train are outlined. A feed-forward dynamic model of the studied power train is next proposed, supervised by a rule-based engineering intuition controller. The energy consumption of the THS-II proposed model has been validated by comparing simulation results to published results on European, American and Japanese regulatory driving cycles

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“…At a given vehicle speed v(t) and road slope α(t), the required force to drive the wheels is (10) where the four terms are vehicle acceleration force, aerodynamic drag force, resistive gravity force, and rolling resistance force, respectively; m, g, ρ, C d , A d , and C r are the vehicle mass, gravity constant, air density, air drag coefficient, frontal area, and rolling resistance, respectively. The load torque and angular speed of the vehicle are (11) (12) respectively, where r w is the radius of the wheels. The demand power to drive the vehicle is (13) and therefore, the mechanical energy delivered to the wheels is (14) According to the power balance principle, the driving demand power P m (t) is always assumed to be fulfilled by the power delivered by the fuel path and electrical path:…”

Section: Mechanical Pathmentioning

confidence: 99%

“…The energy management strategies, also called supervisory control strategies, can be grouped into three categories: rulebased control strategies [5,6,7], optimization-based control strategies [8,9,10], and real-time control strategies [11,12,13]. In the rule-based control strategies, the rules are designed using heuristics, human expertise, or mathematical models.…”

Section: Introductionmentioning

confidence: 99%

Real-Time Optimal Energy Management of Heavy Duty Hybrid Electric Vehicles

Zhao¹,

Stobart²

2013

SAE Int. J. Alt. Power.

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INTRODUCTIONThe White House announced new fuel efficiency standards for trucks, buses, and other heavy duty vehicles in August 2011. Within the standards, vehicles manufactured between 2014 and 2018 are required to reduce their fuel consumption and greenhouse gas emissions by 10% to 20%. HEVs are considered as the most promising short-term solutions in reducing the fuel consumption [1,2]. By exploiting the capacity of a storage system installed aboard, HEVs can achieve better fuel economy and lower exhaust emissions than traditional powertrain vehicles. In a HEV, an electrical path is added to the powertrain such that part of the vehicle kinetic energy and exhaust gas energy can be captured by the electrical machines, and be used to recharge the battery. The energy assisted by the electrical machines also helps downsizing the internal combustion engine (ICE), resulting in better fuel efficiency and lower heat loss [3]. Since electrical machines provide faster boosting torque than mechanical systems, HEVs offer the improved launching performance and the reduced overall rated power comparing with traditional ICE vehicles.A key issue in developing HEVs is the coordination of multiple energy flow of both fuel path and electrical path, that is the management of the power distribution in parallel paths to minimize the fuel consumption, while satisfying the constraints of power demand and battery SOC [4]. The energy management strategies, also called supervisory control strategies, can be grouped into three categories: rulebased control strategies [5,6,7], optimization-based control strategies [8,9,10], and real-time control strategies [11,12,13]. In the rule-based control strategies, the rules are designed using heuristics, human expertise, or mathematical models. Although the control approaches can offer improvement on energy efficiency, it is clear that they do not guarantee an optimal result in all conditions. Using the optimization-based control strategies, the global optimum solution can be found by performing the optimization over a fixed drive cycle. Unfortunately, the global optimization approach is noncasual in nature, because the prior knowledge of the future driving information is required. The real-time optimization strategies manage the energy flow online, where the most well-known approach is the ECMS. The ECMS realizes the real-time optimization by minimizing an instantaneous cost function, so behaves as a closed-loop controller. The equivalent fuel consumption is defined as the extra energy which is offered by the battery and is effected by both the engine operating condition and the supervisory control action. It is assumed that the variation of battery SOC will be compensated in the Real-Time Optimal Energy Management of Heavy Duty Hybrid Electric VehiclesDezong Zhao and Richard StobartLoughborough University ABSTRACTThe performance of energy flow management strategies is essential for the success of hybrid electric vehicles (HEVs), which are considered amongst the most promising solutions for impro...

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A-ECMS: An Adaptive Algorithm for Hybrid Electric Vehicle Energy Management

Cited by 568 publications

References 3 publications

Numerical comparison of ECMS and PMP-based optimal control strategy in hybrid vehicles

Numerical comparison of ECMS and PMP-based optimal control strategy in hybrid vehicles

Dynamic modeling of the electro-mechanical configuration of the Toyota Hybrid System series/parallel power train

Real-Time Optimal Energy Management of Heavy Duty Hybrid Electric Vehicles

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