Optimization of energy management strategy for extended range electric vehicles using multi-island genetic algorithm

Xu, Yonghong; Zhang, Honggang; Yang, Yifan; Zhang, Jian; Yang, Fubin; Yan, Dong; Yang, Hailong; Wang, Yan

doi:10.1016/j.est.2023.106802

Cited by 33 publications

(19 citation statements)

References 51 publications

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“…Finally, by iteration to the maximum generation, the optimal solution is achieved. MIGA improves efficiency and reliability by preventing the algorithm from falling into the local optimal solution in advance [ 72 ], quantified by the following objective function:

where

are the experimental and predicted loads exerted on the indenter, respectively, and D is the total number of indentation depth levels; in this study, 20 indentation depth levels were adopted.…”

Section: Methodsmentioning

confidence: 99%

An Inverse Method to Determine Mechanical Parameters of Porcine Vitreous Bodies Based on the Indentation Test

Zhang

et al. 2023

Bioengineering

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The vitreous body keeps the lens and retina in place and protects these tissues from physical insults. Existing studies have reported that the mechanical properties of vitreous body varied after liquefaction, suggesting mechanical properties could be effective parameters to identify vitreous liquefaction process. Thus, in this work, we aimed to propose a method to determine the mechanical properties of vitreous bodies. Fresh porcine eyes were divided into three groups, including the untreated group, the 24 h liquefaction group and the 48 h liquefaction group, which was injected collagenase and then kept for 24 h or 48 h. The indentation tests were carried out on the vitreous body in its natural location while the posterior segment of the eye was fixed in the container. A finite element model of a specimen undertaking indentation was constructed to simulate the indentation test with surface tension of vitreous body considered. Using the inverse method, the mechanical parameters of the vitreous body and the surface tension coefficient were determined. For the same parameter, values were highest in the untreated group, followed by the 24 h liquefaction group and the lowest in the 48 h liquefaction group. For C10 in the neo-Hookean model, the significant differences were found between the untreated group and liquefaction groups. This work quantified vitreous body mechanical properties successfully using inverse method, which provides a new method for identifying vitreous liquefactions related studies.

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where

are the experimental and predicted loads exerted on the indenter, respectively, and D is the total number of indentation depth levels; in this study, 20 indentation depth levels were adopted.…”

Section: Methodsmentioning

confidence: 99%

An Inverse Method to Determine Mechanical Parameters of Porcine Vitreous Bodies Based on the Indentation Test

Zhang

et al. 2023

Bioengineering

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“…The multi-objective optimization problem optimizes and analyzes multiple sub-objectives to make each sub-objective coordinated and balanced to obtain optimal solutions. 54 When the collaborative optimization framework operates, the system level optimizer first transmits initial values X 0 to the discipline level of flow resistance and winding temperature. After receiving the initial values from the system level, the discipline level optimizer quickly conducts single objective intelligent optimization based on the established Kriging approximation model to obtain the optimization value that has a minor difference from the expected value at the system level on the condition of meeting the discipline constraints and real-time feedback of the target optimization value and various design variable values to the system level.…”

Section: Cooling Performance Improvement Studymentioning

confidence: 99%

Collaborative optimization on cooling channel flow resistance and winding temperature for integrated cooling of motor and controller

Tan,

Yuan,

Tang

2024

Proceedings of the Institution of Mechanical Engineers, Part C:

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The efficiency and working performance of the motor and controller system in micro-electric vehicles depends mainly on its thermal performance, which is contingent upon effective and active cooling. The working temperature of the motor and controller may be too high during operation, which properly causes motor power reduction or motor breakdowns. Therefore, it is crucial to design a good cooling channel for better cooling the integrated motor and controller system. This work proposes an integrated cooling channel of motor and controller, and the fluid-solid coupling analysis is applied for analyzing the velocity, pressure distributions of the cooling channel, and temperature distributions of motor solid components. Meanwhile, the collaborative optimization method based on the multi-island genetic algorithm (MIGA) is used to attain optimal design parameters of the integrated cooling channel of the motor and controller for obtaining a better cooling channel design with lower flow resistance and lower winding temperatures. Results show that the original cooling channel’s flow resistance and maximum winding temperature are 31.87 kPa and 120.04°C, respectively. The flow resistance and winding temperature are pretty significant. After optimization, the local vortex flow and zero velocity area in optimal cooling channel design are improved, the coolant flow velocity becomes more uniform, and the flow resistance is significantly reduced. The flow resistance and maximum winding temperature of the optimal cooling channel design are 12.5 kPa and 116.76°C, respectively, which are decreased compared with the original cooling channel structure, with a decrease of 39.4% and 2.7%, respectively. The research findings in this work can provide theoretical reference for solid components temperature evaluation of motor and give valuable data to cooling performance evaluation and optimization of integrated cooling channel for motor and controller system.

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“…This integration is poised to contribute to more reliable and nuanced SoC estimations across diverse operational conditions, offering a comprehensive solution to the challenges posed by the complex nature of electric vehicle dynamics. d) Graph Neural Networks (GNNs) Reference [166] leveraged GNNs to capture spatial-temporal dependencies, offering valuable insights into SoC dynamics.…”

Section: ) Machine Learning For Soc Estimationmentioning

confidence: 99%

Advancing State of Charge Management in Electric Vehicles With Machine Learning: A Technological Review

Mousaei,

Naderi,

Bayram

2024

IEEE Access

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As the share of electric vehicles increases, electric vehicles are exposed to broader of driving conditions (e.g., extreme weather), which reduce the performance and driving ranges of electric vehicles below their nameplate rating. To ensure customer confidence and support steady growth in electric vehicle adoption rates, accurate estimation of battery state of charge and maintaining battery state of health through optimal charge/discharge decisions are critical. Recently, vehicle manufacturers have begun to employ machine learning techniques to improve state-of-charge management to better inform drivers about both short-term (state of charge) and long-term (state of health) performance of their vehicles. This comprehensive review article explores the intersection of machine learning and state of charge management in electric vehicles. Recognizing the critical importance of state of charge in optimizing electric vehicle performance, the article starts by evaluating traditional state of charge estimation methods. Subsequently, it delves into the transformative impact of machine learning techniques and associated algorithms on state of charge management. Through the lens of various case studies, this article demonstrates how machine learning-based state of charge estimation empowers electric vehicles to make informed and dynamic energy usage decisions, enhancing efficiency and extending battery life. The challenges of data availability, model interpretability, and real-time processing constraints are acknowledged as impediments to the widespread adoption of machine learning techniques. Despite these challenges, the future outlook for machine learning in state of charge management appears promising, with emerging trends such as deep learning and reinforcement learning poised to refine state of charge estimation accuracy. Moreover, this study sheds light on the transformative potential of machine learning in enhancing state of charge management efficiency and effectiveness for electric vehicles, offering critical insights.. Machine learning emerges as a game-changing force in state of charge management for electric vehicles, paving the way for intelligent and adaptive vehicles that are both environmentally friendly and efficient. This evolving field invites further research and development, making it a vital and exciting area within the automotive industry.

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Optimization of energy management strategy for extended range electric vehicles using multi-island genetic algorithm

Cited by 33 publications

References 51 publications

An Inverse Method to Determine Mechanical Parameters of Porcine Vitreous Bodies Based on the Indentation Test

An Inverse Method to Determine Mechanical Parameters of Porcine Vitreous Bodies Based on the Indentation Test

Collaborative optimization on cooling channel flow resistance and winding temperature for integrated cooling of motor and controller

Advancing State of Charge Management in Electric Vehicles With Machine Learning: A Technological Review

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