An efficient auxiliary system controller for Fuel Cell Electric Vehicle (FCEV)

Lawrence, Christopher; El-Shatshat, Ramadan; Salama, M.M.A.; Fraser, Roydon

doi:10.1016/j.energy.2016.09.131

Cited by 11 publications

(14 citation statements)

References 10 publications

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“…The results showed that the solution might be applied for the economy performance, efficiency, and strategies optimization. Lawrence et al, in 37 want to increase the fuel efficiency with a control algorithm, with the ability to adjust the output power value. They use flexibility calculations to evaluate the optimal power‐split factor and the auxiliary power that results for the demand selection.…”

Section: Methodsmentioning

confidence: 99%

“…Several types of classifications of energy management strategies have been suggested in the revised literature considering the criteria of taxonomy, advantages and disadvantages, the natural‐inspired algorithm used, performance obtained, etc. In the following sections, we built two classifications: the first is based on the type of algorithm, and the second is based on the goal they seek to optimize. Rule‐based strategies Fuzzy control strategy 42,76,80,82 State machine control strategy 84 Classical PI control strategy 37,43,47,48,52‐55,58,60,65,67,69,79 Power prediction 24,30,47,87,89,91 Unscented Kalman filter 61 Optimisation‐based strategies Pontryagin's minimum principle (PMP) 39,64 Quadratic programming (QPo) 38,56,86 Stochastic dynamic programming (SDP) 71,92 Multi‐mode predictive 68,90 Dynamic particle swarm optimization 28,83 Equivalent consumption minimization strategy (ECMS) 31,38,41,98 Dynamic programming (DP) 25,59,64 Genetic algorithm (GA) 40,43,76 Efficiency optimization strategy 27,29,36,57,85,93,96 Learning‐based strategies Reinforcement learning 33,62,74 Hybrid 32,46,49,63,66,70,72,73,75,77,78,81,88,95,97 …”

Section: Classification Of Strategiesmentioning

confidence: 99%

“…Compared to the problems and strategies raised in the previews 77 references [24–97, 100–102], critically commented by the authors in their work using tools as tables, diagrams, and figures, the actual problems and requests, identified from the reviewed literature from 2019 and 2020, evolved with the trends, and they are more specific and harder to archive.…”

Section: Novelty Of the Subjectmentioning

confidence: 99%

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Optimal energy management strategies for the electric vehicles compiling bibliometric maps

2021

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Summary The object of this research came from the continuing need to analyze and identify effective methods of reducing pollution and optimizing the functions of electric vehicles. So, a classic analysis of the management strategies proposed in the literature was performed to identify the main research directions that can be used to design an optimal strategy. The performed analysis was validated using different counting and representation methods in VOSviewer and Excel. VOSviewer is a program that visually presents the best research approaches for understanding and developing applications. Thus, analyzing the results obtained, it is possible to identify where the research of energy management strategies for electric vehicles is focused. For example, the most important factors for increasing the performance of a strategy are identified as related to optimization, efficiency, degradation, fuel economy, and systemic development. We identified 127 energy management strategies in the studied articles, which were analyzed and classified by multiple criteria. For example, 21.59% of the strategies have as main goal to increase the vehicles efficiency, followed by the 10% on fuel economy, 7.8% hydrogen consumption strategy, 6.29% work for performance optimization and emission reduction, and so on. So, using this tool, researchers can explore their field of research to identify challenging topics to address in the coming years.

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Section: Methodsmentioning

confidence: 99%

Section: Classification Of Strategiesmentioning

confidence: 99%

Section: Novelty Of the Subjectmentioning

confidence: 99%

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Optimal energy management strategies for the electric vehicles compiling bibliometric maps

2021

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“…To achieve greater efficiency, the modified drive train control algorithm is implemented, and this approach improve efficiency by 3.4% as compared to conventional control methods. 94…”

Section: Control Techniquesmentioning

confidence: 99%

A review on braking control and optimization techniques for electric vehicle

Jamadar

Jadhav²

2021

Proceedings of the Institution of Mechanical Engineers, Part D:

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In this modern world, due to limited fossil fuels, increasing cost, and environmental concerns a clean and sustainable transportation system in the form of electric vehicles (EV) is now becoming popular. On the other hand, electric vehicles have an advantage such as zero-emission, less maintenance, comfortable ride, less noise, etc. An electric vehicle is driven by an electric motor so considering driving patterns and road traffic conditions the vehicle often requires braking operation. The most common method is to employ mechanical brakes to reduce the speed of EV. However, mechanical braking mechanism results in friction which in turn, into heating losses and reduces the efficiency of EV. Therefore, regenerative braking system (RBS) and energy management system are employed in addition to mechanical braking for increasing the braking efficiency of the EV system. It is reported in many literatures about different regenerative braking control techniques for EV systems. This paper presents a review of regenerative braking and braking energy management techniques by considering different driving situations and road conditions.

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“…In the last decades, the vehicle transportation system is occupying a huge proportion of human life, while the traditional vehicle industry can conduct to severe environmental pollution and energy consumption, which leads to the increasing demand for energy-saving and environment -friendly automobile such as electric vehicle (EV) [1]. Due to the breakthrough of the key technology of the electric motor and motor controller, the EV industry is becoming a research hotspot recently, which possesses the merits of high driving safety and transmission efficiency [2], [3].…”

Section: Introductionmentioning

confidence: 99%

An Electronic Line-Shafting Control Strategy Based on Sliding Mode Observer for Distributed Driving Electric Vehicles

Huang

Jiang

et al. 2021

IEEE Access

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This paper proposed control schemes to realize the collaborative control as well as enhance the control precision for the distributed driving electric vehicle (DDEV). Firstly, we suggested an electronic lineshafting (ELS) to realize the cooperative control for the Multi-Motor system which is installed in the DDEV. Secondly, we adopted a nonsingular terminal sliding mode control (NTSMC) method combined with a sliding mode observer (SMO) to quicken the response velocity and strengthen the robustness of the single motor controller. Finally, for the chattering value caused by the internal parameter variation and external disturbance, a fuzzy algorithm is proposed to obtain the controller parameters of NTSMC in real-time to eliminate the chattering value of DDEV. Through several simulations and experiments, the results demonstrate that the proposed strategies can realize ideal collaborative control for the Multi-Motor system, as well as improve the dynamic performance and anti-jamming ability for the whole control system of DDEV. INDEX TERMS distributed driving electric vehicle, Multi-Motor system, electronic line-shafting, nonsingular terminal sliding mode control, sliding mode observer

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An efficient auxiliary system controller for Fuel Cell Electric Vehicle (FCEV)

Cited by 11 publications

References 10 publications

Optimal energy management strategies for the electric vehicles compiling bibliometric maps

Optimal energy management strategies for the electric vehicles compiling bibliometric maps

A review on braking control and optimization techniques for electric vehicle

An Electronic Line-Shafting Control Strategy Based on Sliding Mode Observer for Distributed Driving Electric Vehicles

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