An ECMS for Multi-Objective Energy Management Strategy of Parallel Diesel Electric Hybrid Ship Based on Ant Colony Optimization Algorithm

Xiang, Yongbing; Yang, Xiaomin

doi:10.3390/en14040810

Cited by 20 publications

(6 citation statements)

References 30 publications

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“…However, this strategy doesn't ensure the long-term sustainability of the system's charge. The equivalent factor (EF) significantly impacts the control strategy's torque distribution and is crucial for achieving the optimization effect of ECMS [90].…”

Section: B Optimization-oriented Strategies For Energy Management 1) ...mentioning

confidence: 99%

Energy Management Systems for Electric Vehicles: A Comprehensive Review of Technologies and Trends

Munsi,

Chaoui

2024

IEEE Access

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As the demand for electric vehicles (EVs) continues to surge, improvements to energy management systems (EMS) prove essential for improving their efficiency, performance, and sustainability. This paper covers the distinctive challenges in designing EMS for a range of electric vehicles, such as electrically powered automobiles, split drive cars, and P-HEVs. It also covers significant achievements and proposed solutions to these issues. The powertrain concept for series, parallel, series-parallel, and complex hybrid electric cars was also disclosed in this study. Much of this analysis is dedicated to investigating the various control strategies used in EMS for various electric vehicle types, which include global-optimization approaches, fuzzy rule based, and real-time optimization-oriented strategies. The study thoroughly evaluates the strengths and shortcomings of various electric vehicle strategies, offering valuable insights into their practical implementation and effectiveness across different EV models, such as BEVs, HEVs, and PHEVs.

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Section: B Optimization-oriented Strategies For Energy Management 1) ...mentioning

confidence: 99%

Energy Management Systems for Electric Vehicles: A Comprehensive Review of Technologies and Trends

Munsi,

Chaoui

2024

IEEE Access

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“…The proposed direct search method provided improved solutions compared to the rulebased start point of the search, and it can be an effective method for multi-objective non-convex problems. Xiang and Yang [61] presented a two-layer multi-objective optimization for a diesel electric fishing boat, considering mode switching to optimize the working points of the diesel engine and electric motor. In the inner layer, the Equivalent Consumption Minimization Strategy (ECMS) was used to reduce the amount of consumed energy, and in the outer layer the ant colony algorithm was utilized to maintain the desired battery SoC.…”

Section: B Optimal Control Studiesmentioning

confidence: 99%

A Comprehensive Review of Modeling and Optimization Methods for Ship Energy Systems

2023

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This paper presents a comprehensive literature review of the state-of-the art modeling and optimization methods for the power and propulsion systems of ships. Modeling is a tool to investigate the performance of actual systems by running simulations in the virtual world. There are two main approaches in modeling: physics-based and data-driven, which are both covered in detail in this survey paper. The output from the simulations might not be optimal in terms of certain performance criteria such as energy consumption, fuel cost etc. Hence, it is vital to optimize the systems considering the efficient interaction between the components, to yield the optimal performance for the integrated vessel's powertrain. In this paper, the optimization case studies, for the ship energy systems, will be divided in terms of a) optimal design (topology and sizing), b) optimal control and energy management strategies, c) combined optimal design and control. Tables that summarize the literature review outcomes will also be presented at the end of each section. The main outcome is that limited literature is available for optimizations of ship powertrains using datadriven models, especially surrogate models. Surrogate-assisted optimizations for integrated ship energy systems can yield optimal solutions at fast computational speeds, with sufficient accuracy, even for complex, nested, multi-level, multi-objective optimizations.

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“…To overcome these problems, some scholars have studied the speed prediction method based on the intelligent transportation system and used MPC (Model Predictive Control) algorithm to obtain the optimal target speed, including the speed optimization of a single vehicle and the speed optimization of a fleet containing multiple vehicles. Yet, there is little literature on the optimization of ECMS equivalent factors of multiple HEVs based on the speed predicted by intelligent network connection MPC, and there is little literature on the global optimization of energy management of multiple HEVs based on the predicted working conditions [32].…”

Section: Soc + Soc(t) _ Figure1 Principle Of A-ecms Algorithmmentioning

confidence: 99%

Intelligent Networked Hybrid Electric Vehicle Optimization Using a Linearized Ecms Control Algorithm

2021

IJOMAM

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The purpose is to promote the development of the automotive industry, develop energysaving, safe, comfortable, and environment-friendly HEVs (Hybrid Electric Vehicle). Here, an HEV fleet is researched through the intelligent network connection to improve the fuel economy, traffic fluency, comfort, and safety of the vehicle. The HEV based on linearized ECMS (Equivalent Fuel Consumption Minimization Strategy) control algorithm is partially optimized. Based on the control algorithm of ECMS, cA-ECMS [Equivalent Factor Adaptive Control Strategy Based on Continuous SOC (System on Chip) Feedback] and dA-ECMS (Equivalent Factor Adaptive Control Strategy Based on Discrete SOC Feedback) are proposed. Based on SOC feedback, an online identification A-ECMS (Adaptive ECMS) algorithm is established. The results show that the control algorithm gives full play to the advantages of the adaptive control algorithm and improves the adaptability of the equivalent factor of the HEV. Besides, the fuel consumption, motor total energy consumption, and SOC end value deviation under different SOC initial values are compared. The results show that with the increase of SOC initial value, the end state SOC deviations of cA-ECMS and dA-ECMS control algorithm are about 0.6% and -1.6%, respectively, but the fuel consumption is increasing. Meanwhile, the cA-ECMS control algorithm tends to use fewer batteries. From the perspective of adaptability of equivalent factor, the equivalent factor of the dA-ECMS control algorithm changes less and has better adaptability. All of these lay a solid theoretical foundation for the establishment of an energy management control algorithm for real-time control of the real vehicle.

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An ECMS for Multi-Objective Energy Management Strategy of Parallel Diesel Electric Hybrid Ship Based on Ant Colony Optimization Algorithm

Cited by 20 publications

References 30 publications

Energy Management Systems for Electric Vehicles: A Comprehensive Review of Technologies and Trends

Energy Management Systems for Electric Vehicles: A Comprehensive Review of Technologies and Trends

A Comprehensive Review of Modeling and Optimization Methods for Ship Energy Systems

Intelligent Networked Hybrid Electric Vehicle Optimization Using a Linearized Ecms Control Algorithm

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