Decentralized Optimal Merging Control With Optimization of Energy Consumption for Connected Hybrid Electric Vehicles

2021 ◽  
pp. 1-13
Author(s):  
Fuguo Xu ◽  
Tielong Shen
Keyword(s):  
Energy Consumption ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Hybrid Electric

scholarly journals Optimal Energy Management of Plug-In Hybrid Electric Vehicles Concerning the Entire Lifespan of Lithium-Ion Batteries

Energies ◽  
2020 ◽  
Vol 13 (10) ◽  
pp. 2543 ◽  
Author(s):  
Zeyu Chen ◽  
Jiahuan Lu ◽  
Bo Liu ◽  
Nan Zhou ◽  
Shijie Li
Keyword(s):  
Energy Efficiency ◽  
Energy Consumption ◽  
Lithium Ion Batteries ◽  
Energy Management ◽  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Lithium Ion ◽  
Energy Management Strategy ◽  
Optimal Energy Management ◽  
Hybrid Electric

The performance of lithium-ion batteries will inevitably degrade during the high frequently charging/discharging load applied in electric vehicles. For hybrid electric vehicles, battery aging not only declines the performance and reliability of the battery itself, but it also affects the whole energy efficiency of the vehicle since the engine has to participate more. Therefore, the energy management strategy is required to be adjusted during the entire lifespan of lithium-ion batteries to maintain the optimality of energy economy. In this study, tests of the battery performances under thirteen different aging stages are involved and a parameters-varying battery model that represents the battery degradation is established. The influences of battery aging on energy consumption of a given plug-in hybrid electric vehicle (PHEV) are analyzed quantitatively. The results indicate that the variations of capacity and internal resistance are the main factors while the polarization and open circuit voltage (OCV) have a minor effect on the energy consumption. Based on the above efforts, the optimal energy management strategy is proposed for optimizing the energy efficiency concerning both the fresh and aging batteries in PHEV. The presented strategy is evaluated by a simulation study with different driving cycles, illustrating that it can balance out some of the harmful effects that battery aging can have on energy efficiency. The energy consumption is reduced by up to 2.24% compared with that under the optimal strategy without considering the battery aging.

scholarly journals A Novel Control Algorithm Design for Hybrid Electric Vehicles Considering Energy Consumption and Emission Performance

Energies ◽  
2019 ◽  
Vol 12 (14) ◽  
pp. 2698 ◽  
Author(s):  
Yuan Qiao ◽  
Yizhou Song ◽  
Kaisheng Huang
Keyword(s):  
Energy Consumption ◽  
Objective Function ◽  
Electric Vehicles ◽  
Control Algorithm ◽  
Hybrid Electric Vehicles ◽  
Algorithm Design ◽  
Classical Dynamic ◽  
Emission Performance ◽  
Simulation Results ◽  
Hybrid Electric

Under the severe challenge of increasingly stringent emission regulations and constantly improving fuel economy requirements, hybrid electric vehicles (HEVs) have attracted widespread attention in the auto industry as a practicable technical route of green vehicles. To address the considerations on energy consumption and emission performance simultaneously, a novel control algorithm design is proposed for the energy management system (EMS) of HEVs. First, energy consumption of the investigated P3 HEV powertrain is determined based on bench test data. Second, crucial performance indicators of NOx and particle emissions, prior to a catalytic converter, are also measured and processed as a prerequisite. A comprehensive objective function is established on the grounds of the Equivalent Consumption Minimization Strategy (ECMS) and corresponding simulation models are constructed in MATLAB/SIMULINK. Subsequently, the control algorithm is validated against the simulation results predicated on the Worldwide-Harmonized Light-Vehicle Test Procedure (WLTP).Integrated research contents include: (1) The searching process aimed at the optimal solution of the pre-established multi-parameter objective function is thoroughly investigated; (2) the impacts of weighting coefficients pertaining to two exhaust pollutants upon the specific configurations of the proposed control algorithm are discussed in detail; and (3) the comparison analysis of the simulation results obtained from ECMS and classical Dynamic Programming (DP), respectively, is performed.

Battery-Health Conscious Power Management for Plug-In Hybrid Electric Vehicles via Stochastic Control

2010 ◽  
Author(s):  
Scott J. Moura ◽  
Jeffrey L. Stein ◽  
Hosam K. Fathy
Keyword(s):  
Energy Consumption ◽  
Power Management ◽  
Electric Vehicles ◽  
Energy Cost ◽  
Hybrid Electric Vehicles ◽  
Stochastic Dynamic ◽  
Management Problem ◽  
Battery Charge ◽  
Consumption Cost ◽  
Hybrid Electric

This paper investigates power management algorithms that optimally manage lithium-ion battery pack health, in terms of anode-side film growth, for plug-in hybrid electric vehicles (PHEVs). Specifically, we integrate a reduced electrochemical model of solid electrolyte interface (SEI) film formation into a stochastic dynamic programming formulation of the PHEV power management problem. This makes it possible to optimally trade off energy consumption cost versus battery health. A careful analysis of the resulting Pareto-optimal set of power management solutions provides two important insights into the tradeoffs between battery health and energy consumption cost in PHEVs. First, optimal power management solutions that minimize energy consumption cost tend to ration battery charge, while the solutions that minimize battery health degradation tend to deplete charge aggressively. Second, solutions that balance the needs for minimum energy cost and maximum battery health tend to aggressively deplete battery charge at high states of charge (SOCs), then blend engine and battery power at lower SOCs. These results provide insight into the fundamental tradeoffs between battery health and energy cost in PHEV power management.

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