A GA-based online real-time optimized energy management strategy for plug-in hybrid electric vehicles

Fan, Likang; Wang, Yufei; Wei, Hongqian; Zhang, Youtong; Peng-yu, Zheng; Huang, Tianyi; Li, Wei

doi:10.1016/j.energy.2021.122811

Cited by 56 publications

(11 citation statements)

References 43 publications

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“…The IGPSO is applied when splitting power between the BE and motor depending on the required torque and SOC values Eq. (18).…”

Section: Offline Strategy (Long-period Optimization)mentioning

confidence: 99%

“…In addition, the DP necessitates the data of the whole system dynamics to obtain the optimal global solution. In [17,18], a genetic algorithm (GA) was applied to obtain the optimal EMS solution and overcome the issue of heavy computation time in the DP algorithm by adjusting the power split control parameters for certain driving conditions. Although the GA has a low computing burden and nearoptimality for a wide range of driving cycles, the prior driving cycle knowledge is important to be valid.…”

Section: Introductionmentioning

confidence: 99%

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RETRACTED ARTICLE: Intelligent power management based on multi-objective cost function for plug-in biogas hybrid vehicles under uncertain driving conditions

Abd‐Elhaleem

Shoeib

Sobaih

2022

Complex Intell. Syst.

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This paper presents a new intelligent power management strategy based on multi-objective cost function for plug-in biogas hybrid vehicles (PBHVs). This strategy consists of long-term power management and a short-term controller. The long-term power management depends on an improved generalized particle swarm optimization algorithm (IGPSO) to obtain the globally optimal values of motor and biogas engine torques. To reduce the computation time, five-mode rule-based control is used, where the IGPSO estimates the optimal values for the motor and engine torques in a hybrid mode depending on a multi-objective cost function. This cost function aims to reduce fuel consumption and the drawn current from the battery and takes into consideration the battery ageing. The short-term controller is designed using an interval type-2 Takagi–Sugeno-Kang (IT2TSK) fuzzy algorithm, which depends on human experts to overcome the uncertainties of the driving conditions. Lyapunov stability theory for the online controller is proved. The proposed technique improves the energy consumption compared to other techniques. The simulation results using real data for the engine, motor and battery illustrate the feasibility and effectiveness of the proposed approach with comparative results.

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“…The IGPSO is applied when splitting power between the BE and motor depending on the required torque and SOC values Eq. (18).…”

Section: Offline Strategy (Long-period Optimization)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

RETRACTED ARTICLE: Intelligent power management based on multi-objective cost function for plug-in biogas hybrid vehicles under uncertain driving conditions

Abd‐Elhaleem

Shoeib

Sobaih

2022

Complex Intell. Syst.

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“…Choosing an appropriate intelligent optimization algorithm for parameter identification is essential to building a highprecision model. Genetic algorithm, with strong random search ability and good robustness, [34][35][36] is chosen to identify the parameters of the battery and ultracapacitor models. The detailed modeling steps are as follows.…”

Section: Parameter Identification Of the Battery And Ultracapacitor A...mentioning

confidence: 99%

A reinforcement learning‐based energy management strategy for a battery–ultracapacitor electric vehicle considering temperature effects

Wang

Tang

et al. 2023

Circuit Theory & Apps

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SummaryThe design of energy management strategy (EMS) plays a vital role in the power performance and economy of battery–ultracapacitor for electric vehicles. A reinforcement learning (RL)‐based EMS is proposed to obtain an optimal power allocation strategy for battery–ultracapacitor electric vehicle, and its robustness is verified at different temperatures. First of all, the dynamic characteristic experiments of the battery and ultracapacitor were performed at 10°C, 25°C, and 40°C to obtain mechanism characteristics at different temperatures. Secondly, a genetic algorithm is selected to identify the parameters of the battery and ultracapacitor model. Next, the RL‐based strategy takes the minimum energy loss of the hybrid energy storage system as the reward function and solves the optimal policy based on Markov theory. The simulation results show that the economy of the RL‐based strategy correspondingly improved by 3.05%, 3.20%, and 3.15% at different temperatures in comparison with the fuzzy‐based strategy, and the economic gap between the RL‐based strategy and the DP‐based strategy is further narrowed down to 7.30%, 3.88%, and 8.40% at different temperatures, respectively. Finally, the proposed strategy is validated under different driving conditions, which indicate that the RL‐based strategy can effectively reduce energy consumption and has good robustness at different temperatures.

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“…The objectives of the fuel cell hybrid vehicle energy management strategy include:① maintaining the SOC of the power cell within a reasonable range, charging the fuel cell when the SOC is low and maintaining a balanced discharge when the SOC is high; ② keeping the vehicle in a low energy consumption state；③ ensuring that the output power of the fuel cell is stable and preventing large variations [17,18], As shown in Table Ⅰ below.…”

Section: Energy Management Strategymentioning

confidence: 99%

Optimization of energy management strategy for fuel cell vehicles based on hybrid genetic algorithm

Zhang

Li²

2022

6th International Workshop on Advanced Algorithms and Control Engineering (IWAACE 2022)

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Genetic algorithm is an algorithm that searches for the optimal solution by simulating the natural evolutionary process. The development of fuel cell vehicles is of great significance to the traditional automobile industry as well as the energy industry, and is an important initiative to protect the environment and conserve resources. However, fuel cell vehicles still face the problem of insufficient range. One solution is to form a multi-energy source system by combining fuel cells with secondary charging auxiliary power or other power sources. Therefore, to give full play to the advantages of hybrid power, it is necessary to solve the energy management problems brought about by hybrid power. In this study, the applicability of energy management strategy optimization for fuel cell vehicles is analyzed with the help of hybrid genetic algorithms.

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A GA-based online real-time optimized energy management strategy for plug-in hybrid electric vehicles

Cited by 56 publications

References 43 publications

RETRACTED ARTICLE: Intelligent power management based on multi-objective cost function for plug-in biogas hybrid vehicles under uncertain driving conditions

RETRACTED ARTICLE: Intelligent power management based on multi-objective cost function for plug-in biogas hybrid vehicles under uncertain driving conditions

A reinforcement learning‐based energy management strategy for a battery–ultracapacitor electric vehicle considering temperature effects

Optimization of energy management strategy for fuel cell vehicles based on hybrid genetic algorithm

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