Optimized predictive energy management of plug-in hybrid electric vehicle based on traffic condition

Montazeri-Gh, Morteza; Mahmoodi-k, Mehdi

doi:10.1016/j.jclepro.2016.07.203

Cited by 124 publications

(68 citation statements)

References 23 publications

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“…Considering the charging behavior of electric car users will be affected by the price points, so the paper assumes that the charge behavior will be appropriately reduced in peak price period or increased in low price period and in other periods of charging behavior follow the rules of the traditional fuel vehicles travel. The EV charging situations of different driving conditions are shown in the (2) We can get the SOC probability distribution according to the charging period produced in the first step and then using the corresponding function in MATLAB to get the initial charge status of the electric car. Assuming that the electric vehicle charging power is and the total capacity of the electric car is S, the situation of the charge of the battery before and after the electric car is charged is shown in the form (1):…”

Section: Calculation Methods Of Charging Demandmentioning

confidence: 99%

“…(3) Simulating the situation of N electric vehicles by MATLAB and finally load the charging load of N electric vehicle to get the total charging load at each moment, such as formula (2):…”

Section: Calculation Methods Of Charging Demandmentioning

confidence: 99%

“…There are many researches on the performance optimization of EV and further reduction of air pollution [1] and some researches on how to make a progress in fuel economy for various driving patterns [2,3]. It is foreseeable that in the near future, there will be large-scale electric vehicles connected to the grid for charging.…”

Section: Introductionmentioning

confidence: 99%

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Influence and Strategy of Electric Vehicle Charging on Distribution Network Voltage Based on Uncertainty Charging Habit

Mengtian¹

2017

AUTOMATION

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With the increasing scale of the use of electric vehicles, impact of electric vehicle charging on the distribution system cannot be ignored. Considering uncertainty in the charging behavior, large-scale disorderly charging will cause the voltage deviation of distribution network exceeds the specified value. In the paper, based on the IEEE33 node as an example, the goal is to minimum each node voltage deviation by to controlling charge power, considering the constraints of the charging energy and the charging time, and optimized by the genetic algorithm. By using Newton Raphson power flow method, the voltage offset of each node before and after optimization is obtained, and the effectiveness of the charging load optimization model is verified by comparing and analyzing.

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Section: Calculation Methods Of Charging Demandmentioning

confidence: 99%

“…(3) Simulating the situation of N electric vehicles by MATLAB and finally load the charging load of N electric vehicle to get the total charging load at each moment, such as formula (2):…”

Section: Calculation Methods Of Charging Demandmentioning

confidence: 99%

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Influence and Strategy of Electric Vehicle Charging on Distribution Network Voltage Based on Uncertainty Charging Habit

Mengtian¹

2017

AUTOMATION

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“…A versatile driving conditions preceptive model is developed based on four traffic congestion levels [27]. Fuzzy logic parameters are optimized off-line for every condition using genetic algorithms GA. Simulation results revealed that the method is more adequate for real-life application rather than optimizing these parameters for the whole drive cycle.…”

Section: Predictive Fuzzymentioning

confidence: 99%

Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

2018

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Abstract:In light of increasing alerts about limited energy sources and environment degradation, it has become essential to search for alternatives to thermal engine-based vehicles which are a major source of air pollution and fossil fuel depletion. Hybrid electric vehicles (HEVs), encompassing multiple energy sources, are a short-term solution that meets the performance requirements and contributes to fuel saving and emission reduction aims. Power management methods such as regulating efficient energy flow to the vehicle propulsion, are core technologies of HEVs. Intelligent power management methods, capable of acquiring optimal power handling, accommodating system inaccuracies, and suiting real-time applications can significantly improve the powertrain efficiency at different operating conditions. Rule-based methods are simply structured and easily implementable in real-time; however, a limited optimality in power handling decisions can be achieved. Optimization-based methods are more capable of achieving this optimality at the price of augmented computational load. In the last few years, these optimization-based methods have been under development to suit real-time application using more predictive, recognitive, and artificial intelligence tools. This paper presents a review-based discussion about these new trends in real-time optimal power management methods. More focus is given to the adaptation tools used to boost methods optimality in real-time. The contribution of this work can be identified in two points: First, to provide researchers and scholars with an overview of different power management methods. Second, to point out the state-of-the-art trends in real-time optimal methods and to highlight promising approaches for future development.

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“…In presented NSGA-II, we implemented continues uniform crossover and swap strategy for crossover and mutation operators, respectively (Hajipour et al, 2014, Montazer-Gh andMahmoodi-k, 2016). It should be mentioned that, after the crossover and mutation operations, the fitness function values are calculated for offspring.…”

Section: Crossover and Mutation Operatorsmentioning

confidence: 99%

Multi-Objective Soft Computing-Based Approaches to Optimize Inventory-Queuing-Pricing Problem under Fuzzy Considerations

Alinezhad¹,

Mahmoudi²,

Hajipour³

2016

Industrial Engineering and Management Systems

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Due to uncertain environment, various parameters such as price, queuing length, warranty, and so on influence on inventory models. In this paper, an inventory-queuing-pricing problem with continuous review inventory control policy and batch arrival queuing approach, is presented. To best of our knowledge, (I) demand function is stochastic and price dependent; (II) due to the uncertainty in real-world situations, a fuzzy programming approach is applied. Therefore, the presented model with goal of maximizing total profit of system analyzes the price and order quantity decision variables. Since the proposed model belongs to NP-hard problems, Pareto-based approaches based on non-dominated ranking and sorting genetic algorithm are proposed and justified to solve the model. Several numerical illustrations are generated to demonstrate the model validity and algorithms performance. The results showed the applicability and robustness of the proposed soft-computing-based approaches to analyze the problem.

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Optimized predictive energy management of plug-in hybrid electric vehicle based on traffic condition

Cited by 124 publications

References 23 publications

Influence and Strategy of Electric Vehicle Charging on Distribution Network Voltage Based on Uncertainty Charging Habit

Influence and Strategy of Electric Vehicle Charging on Distribution Network Voltage Based on Uncertainty Charging Habit

Towards Optimal Power Management of Hybrid Electric Vehicles in Real-Time: A Review on Methods, Challenges, and State-Of-The-Art Solutions

Multi-Objective Soft Computing-Based Approaches to Optimize Inventory-Queuing-Pricing Problem under Fuzzy Considerations

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