Machine learning and whale optimization algorithm based design of energy management strategy for plug‐in hybrid electric vehicle

Hou, Zhuoran; Guo, Jianhua; Xing, Jiaming; Guo, Chong; Zhang, Hongjie

doi:10.1049/itr2.12084

Cited by 20 publications

(9 citation statements)

References 35 publications

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Section: Whale Optimization Algorithmmentioning

confidence: 99%

“…Mirjalili proposed the whale optimization algorithm (WOA) in 2016 [35], which has been widely used in power market bidding, battery energy management and other fields due to its strong search capabilities and fast convergence speed [36, 37]. The WOA algorithm gradually approaches the optimal solution through imitating the bubble net attack mechanism of whales, which includes three parts: encircling prey, bubble‐net feeding manoeuvre and searching for prey.…”

Section: Theoretical Foundationmentioning

confidence: 99%

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Control scheme to extend lifetime of BESS for assisting wind farm to track power generation plan based on wind power feature extraction

Chen

Wang

et al. 2022

IET Power Electronics

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In allusion to the issues of high loss of lifetime and neglecting scheduling potential, a control scheme to extend BESS lifetime for assisting wind farm to track power generation plan based on wind power feature extraction through improved whale optimization algorithm (IWOA) optimized swing door trending (IWOA‐SDT) is proposed. Firstly, an IWOA is designed to optimize the compression offset of the swing door trending algorithm. The preliminary wind power feature is extracted by IWOA‐SDT and is further corrected with actual output of wind power to acquire the corrected wind power feature. Secondly, BESS is divided into two groups and the second battery group is subdivided into three battery clusters, and their capacities are configured by the deviation between preliminary feature and forecasted power. The grouping state of battery units is dynamically adjusted based on devised dynamic grouping method at the corresponding time of corrected wind power feature. The power coordinative allocation approach of the two battery groups is designed. A power coordinative allocation strategy for battery groups is devised to obtain the power regulation signals of two battery groups. The power allocation in battery units of the first group is calculated by the number of the units, and the double‐layer power allocation in battery units of the second group coordinated with multiple rules is devised. Finally, the proposed control scheme is implemented in a wind farm to track the power generation plan, and the results show that the wind farm with BESS can accurately track the power generation plan, the service life of BESS is significantly extended and the scheduling potential is sufficient for the next period.

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Section: Whale Optimization Algorithmmentioning

confidence: 99%

Section: Theoretical Foundationmentioning

confidence: 99%

Control scheme to extend lifetime of BESS for assisting wind farm to track power generation plan based on wind power feature extraction

Chen

Wang

et al. 2022

IET Power Electronics

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“…Regarding the optimization of management efficiency in construction projects, foreign scholars have continuously increased their attention in recent years. Through the application of different research methods and theories, as well as attempts at various research objectives, a large amount of research has been conducted on the overall optimization of construction industry and construction project management efficiency, which has been continuously promoted and developed on the basis of previous research [7,6].…”

Section: Introductionmentioning

confidence: 99%

Application of Deep Learning Algorithm in Optimization of Engineering Intelligent Management Control

Zhou,

Guo

2024

SCPE

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Currently, there are a series of problems in the management of the construction industry, such as resource waste, substandard quality, and low construction efficiency. In response to this phenomenon, the author proposes a multi-objective optimization control method for construction engineering management projects using deep learning algorithms. This method analyzes the relationship between cost, duration, and quality, and constructs an optimization management model for these three factors. At the same time, the improved SULSTM neural network algorithm is used to optimize the model parameters. The experimental results indicate that, when the value coefficient is 0.2211, the total investment cost and quality coefficient are 412700 yuan and 0.99496 yuan, respectively. When the value coefficient is 0.1976, the total cost and quality coefficient are 456300 yuan and 0.98798 yuan, respectively. When the value coefficient is 0.1990, the total cost and quality coefficient are 456300 yuan and 0.99496 yuan, respectively. Proved that the SUSTM neural network algorithm has faster convergence speed and lower loss values compared to the improved LSTM neural network algorithm. The cost of improving quality has a greater impact on the quality coefficient than the duration, and the total investment cost has a greater impact on the value coefficient than the quality coefficient.

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“…Existing EMSs for PFCEVs can be classified into three categories: rule-based EMSs (RB-EMSs), optimization-based EMSs (OB-EMSs), and learning-based EMSs (LB-EMSs) [ 7 , 8 , 9 ]. Rule-based EMS generally establishes rules through human inspiration and expert experience, such as state flow diagrams [ 10 , 11 ] or fuzzy rules [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

A Novel A-ECMS Energy Management Strategy Based on Dragonfly Algorithm for Plug-in FCEVs

Chu

et al. 2023

Sensors

Self Cite

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The mechanical coupling of multiple powertrain components makes the energy management of 4-wheel-drive (4WD) plug-in fuel cell electric vehicles (PFCEVs) relatively complex. Optimizing energy management strategies (EMSs) for this complex system is essential, aiming at improving the vehicle economy and the adaptability of operating conditions. Accordingly, a novel adaptive equivalent consumption minimization strategy (A-ECMS) based on the dragonfly algorithm (DA) is proposed to achieve coordinated control of the powertrain components, front and rear motors, as well as the fuel cell system and the battery. To begin with, the equivalent consumption minimization strategy (ECMS) with extraordinary instantaneous optimization ability is used to distribute the vehicle demand power into the front and rear motor power, considering the different motor characteristics. Subsequently, under the proposed novel hierarchical energy management framework, the well-designed A-ECMS based on DA empowers PFCEVs with significant energy-saving advantages and adaptability to operating conditions, which are achieved by precise power distribution considering the operating characteristics of the fuel cell system and battery. These provide state-of-the-art energy-saving abilities for the multi-degree-of-freedom systems of PFCEVs. Lastly, a series of detailed evaluations are performed through simulations to validate the improved performance of A-ECMS. The corresponding results highlight the optimal control performance in the energy-saving performance of A-ECMS.

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Machine learning and whale optimization algorithm based design of energy management strategy for plug‐in hybrid electric vehicle

Cited by 20 publications

References 35 publications

Control scheme to extend lifetime of BESS for assisting wind farm to track power generation plan based on wind power feature extraction

Control scheme to extend lifetime of BESS for assisting wind farm to track power generation plan based on wind power feature extraction

Application of Deep Learning Algorithm in Optimization of Engineering Intelligent Management Control

A Novel A-ECMS Energy Management Strategy Based on Dragonfly Algorithm for Plug-in FCEVs

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