Optimal Control of Hybrid Vehicles

Jager, A.G. de; Keulen, T.A.C. van; Kessels, J.T.B.A.

doi:10.1007/978-1-4471-5076-3

Cited by 66 publications

(34 citation statements)

References 0 publications

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“…There are different algorithms to govern the power split between the alternative power sources [19,20], such as Lagrange Multipliers, Pontryagin's Minimum Principle, or Dynamic Programming, but they rely on exact description of energy losses in the all components including the energy sources and seeking the optimal solutions requires high computing resources and time.…”

Section: Introductionmentioning

confidence: 99%

The Kinetic Energy Storage as an Energy Buffer for Electric Vehicles

Jivkov¹,

Draganov²

2017

Adv Automob Eng

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It is considered a hybrid driveline intended for electric vehicle in which Kinetic Energy Storage (KES) is used as an energy buffer for the load levelling over the main energy source -Li-Ion battery. Relations for KES local efficiency are worked out. Overall efficiencies of the parallel power branches are defined, and a control strategy for power split is proposed based on the alternative storage devices State of Charge (SoC). Quantity estimations of KES influence on the battery loading are obtained by evaluation of covered mileage, achievable with a single battery recharge over standard driving cycles, and by expected battery cycle-life prediction.

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Section: Introductionmentioning

confidence: 99%

The Kinetic Energy Storage as an Energy Buffer for Electric Vehicles

Jivkov¹,

Draganov²

2017

Adv Automob Eng

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“…The models of the high-and low-voltage battery are derived from a battery equivalent circuit model, i.e., an open circuit voltage U m,oc for m ∈ {hvb, lvb} in series with a resistance R m,i for m ∈ {hvb, lvb} (see, e.g., [2]), which lead to an input-output behavior given by (5a) with q m = R m,i U 2 m,oc , f m = −1 and e m = 0 for m ∈ {hvb, lvb}, which is bounded by (5b) for m ∈ {hvb, lvb}. The amount of energy in the high-and low-voltage battery, i.e., x m (t) for m ∈ {hvb, lvb} satisfies (5d) for m ∈ {hvb, lvb} withÃ m = 0,B m,w = 0,B m,u = −1 and w m (t) = 0 for m ∈ {hvb, lvb} and is constrained to (5e) for m ∈ {hvb, lvb}.…”

Section: High-and Low-voltage Batterymentioning

confidence: 99%

“…The proposed solution strategies can be divided into so-called offline and online solution strategies [2,3]. Offline solution strategies have been developed based on, e.g., dynamic programming (DP; see, e.g., [4][5][6]), Pontryagin's minimum principle (PMP; see, e.g., [7][8][9][10]) or convex optimization (see, e.g., [11,12]).…”

Section: Introductionmentioning

confidence: 99%

Real-Time Distributed Economic Model Predictive Control for Complete Vehicle Energy Management

et al. 2017

Self Cite

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Abstract:In this paper, a real-time distributed economic model predictive control approach for complete vehicle energy management (CVEM) is presented using a receding control horizon in combination with a dual decomposition. The dual decomposition allows the CVEM optimization problem to be solved by solving several smaller optimization problems. The receding horizon control problem is formulated with variable sample intervals, allowing for large prediction horizons with only a limited number of decision variables and constraints in the optimization problem. Furthermore, a novel on/off control concept for the control of the refrigerated semi-trailer, the air supply system and the climate control system is introduced. Simulation results on a low-fidelity vehicle model show that close to optimal fuel reduction performance can be achieved. The fuel reduction for the on/off controlled subsystems strongly depends on the number of switches allowed. By allowing up to 15-times more switches, a fuel reduction of 1.3% can be achieved. The approach is also validated on a high-fidelity vehicle model, for which the road slope is predicted by an e-horizon sensor, leading to a prediction of the propulsion power and engine speed. The prediction algorithm is demonstrated with measured ADASIS information on a public road around Eindhoven, which shows that accurate prediction of the propulsion power and engine speed is feasible when the vehicle follows the most probable path. A fuel reduction of up to 0.63% is achieved for the high-fidelity vehicle model.

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“…For this reason, PHEV puts more weigh on Series and combine parallel or power split modes by adding clutch or gear for Charging sustaining (CS) cycle. In this paper any Plug-in hybrid systems with series and parallel or power-split, are included in a combined topologies [12].…”

Section: Phev Powertrain Topologiesmentioning

confidence: 99%

Configuration Analysis of Plug-in Hybrid Systems using Global Optimization

Kim

2013

WEVJ

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The purpose of the study is to analyze the configurations of Plug-in Hybrid Electric Vehicles (PHEV) with respect to fuel economy. Existing studies mostly focus on hybrid systems or few PHEV systems by only considering power split ratio and component efficiency. This paper adds original contribution to these literatures. First of all, this study compares and analyzes "series + α" PHEV -Input split, Series-output split and Series-parallel, which is consisted of a single Planetary gear or spur gear and clutches. Those are currently applied to mass-production vehicles such as Toyota Prius PHEV, Chevrolet Volt and Honda Accord PHEV. On top of that, it examines the impact of the transmission mechanical losses on Dynamic programming (DP) results and especially the planetary gear loss is modelled using power split ratio analysis. Lastly, the effect of Series mode for each PHEV system is examined by analysis of the theoretical system efficiency and DP in a certain driving profile. From this study the strength and weakness of PHEV systems are revealed depending on a driving condition and battery status, e.g. charging depleting (CD) or charging sustaining (CS). The PHEV system analysis in this study can help select proper system for a certain purpose.

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Optimal Control of Hybrid Vehicles

Cited by 66 publications

References 0 publications

The Kinetic Energy Storage as an Energy Buffer for Electric Vehicles

The Kinetic Energy Storage as an Energy Buffer for Electric Vehicles

Real-Time Distributed Economic Model Predictive Control for Complete Vehicle Energy Management

Configuration Analysis of Plug-in Hybrid Systems using Global Optimization

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