Multi-disciplinary decision making and optimization for hybrid electric propulsion systems

Shaltout, Mohamed L.; Chen, Dongmei; Malikopoulos, Andreas A.; Pannala, Sreekanth

doi:10.1109/ievc.2014.7056212

Cited by 9 publications

(6 citation statements)

References 19 publications

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“…This work has been extended [43], [44], and the proposed multiobjective optimization framework has considered the battery in the problem formulation in addition to the engine's BSFC and motor's efficiency. The extended optimization framework aims to enhance our understanding of the associated tradeoffs among the HEV subsystems, e.g., the engine, the motor, and the battery, and investigates the related implications for fuel consumption and battery capacity and lifetime.…”

Section: B Simulation Resultsmentioning

confidence: 99%

A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles

Malikopoulos

2016

IEEE Trans. Contr. Syst. Technol.

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The increasing urgency to extract additional efficiency from hybrid propulsion systems has led to the development of advanced power management control algorithms. In this paper, we address the problem of online optimization of the supervisory power management control in parallel hybrid electric vehicles (HEVs). We model HEV operation as a controlled Markov chain and show that the control policy yielding the Pareto optimal solution minimizes online the long-run expected average cost per unit time criterion. The effectiveness of the proposed solution is validated through simulation and compared with the solution derived with dynamic programming using the average cost criterion. Both solutions achieved the same cumulative fuel consumption demonstrating that the online Pareto control policy is an optimal control policy.

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Section: B Simulation Resultsmentioning

confidence: 99%

A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles

Malikopoulos

2016

IEEE Trans. Contr. Syst. Technol.

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“…There are many reasons for doing this. First, in some engines, electrical power and ICE are combined to reduce overall consumption, or for other objectives of interest (e.g., extend the lifetime of the battery, as in [18]). Second, access to LEZs may be restricted to zero emission vehicles.…”

Section: Commentmentioning

confidence: 99%

Cooperative control and smart procurement of naturally generated energy (SPONGE) for PHEVs

Häusler

Griggs

et al. 2015

2015 International Conference on Connected Vehicles and Expo (ICCVE)

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Electric vehicles can potentially be the best means of transportation for improving air quality, provided that they are powered by electricity from natural gas or wind, water or solar power. In this paper we describe a simple cooperative algorithm that exploits the energy management units of Plug-in Hybrid Electric Vehicles (PHEVs) to absorb the expected forthcoming energy available from renewable sources. The proposed approach bridges the gap between mobility patterns and power grid constraints, and allows to prevent green energy from being wasted while at the same time reducing the complexity burden of the power grid to charge unexpected loads of electric vehicles. Simulation results are given to show the efficacy of the proposed method

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“…There are many reasons for doing this. First, in some engines, electrical power and ICE are combined to reduce overall consumption, or for other objectives of interest (e.g., extend the lifetime of the battery) [16]. Thus, it is advantageous to keep a store of naturally generated electrical energy for this purpose.…”

Section: Commentmentioning

confidence: 99%

Smart Procurement Of Naturally Generated Energy (SPONGE) for PHEV's

Häusler¹,

Crisostomi²,

Radusch³

et al. 2015

Preprint

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In this paper we propose a new engine management system for hybrid vehicles to enable energy providers and car manufacturers to provide new services. Energy forecasts are used to collaboratively orchestrate the behaviour of engine management systems of a fleet of PHEV's to absorb oncoming energy in an smart manner. Cooperative algorithms are suggested to manage the energy absorption in an optimal manner for a fleet of vehicles, and the mobility simulator SUMO is used to show simple simulations to support the efficacy of the proposed idea.

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Multi-disciplinary decision making and optimization for hybrid electric propulsion systems

Cited by 9 publications

References 19 publications

A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles

A Multiobjective Optimization Framework for Online Stochastic Optimal Control in Hybrid Electric Vehicles

Cooperative control and smart procurement of naturally generated energy (SPONGE) for PHEVs

Smart Procurement Of Naturally Generated Energy (SPONGE) for PHEV's

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