Co-optimization of speed planning and energy management for intelligent fuel cell hybrid vehicle considering complex traffic conditions

Nie, Zhigen; Jia, Yuping; Wang, Wanqiong; Chen, Zheng; Outbib, Rachid

doi:10.1016/j.energy.2022.123476

Cited by 36 publications

(13 citation statements)

References 43 publications

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“…Genetic optimization algorithm, as a global optimization method with powerful search capability, is widely used in parameter matching and control strategy optimization [19]. The parameters of the fuzzy controller are globally optimized by genetic algorithm, and fuel economy and fuel cell durability are taken as the optimization goals, and multi-objective genetic algorithm is used to solve the multi-objective optimization problem to achieve the optimization of the final fuel cell vehicle energy strategy [20].…”

Section: Control Optimization Based On Multi-objective Cenetic Algorithmmentioning

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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Section: Control Optimization Based On Multi-objective Cenetic Algorithmmentioning

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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“…Notably, many researchers focused on energy management systems. Nie et al proposed a cooperative control strategy for fuel cell hybrid vehicles (FCHVs) to achieve simultaneous speed planning and energy management [9]. Han et al proposed an energy management strategy for plug-in hybrid electric vehicles (PHEVs) that considers the thermal control of the onboard electric motor [10].…”

Section: Introductionmentioning

confidence: 99%

New vehicle control strategy for fuel consumption and battery state of health improvement in a series hybrid vehicle utilizing fuzzy logic

Moghimi,

Teymoori,

Chitsaz

2024

J Braz. Soc. Mech. Sci. Eng.

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Series hybrid vehicles have become increasingly popular due to their high efficiency and low fuel consumption. However, the efficiency of a series hybrid electric vehicle largely depends on its control strategy. This paper presents a practical control strategy that optimizes the performance of series hybrid vehicle in terms of fuel consumption. To achieve this objective, the power-follower strategy which uses a fuzzy controller was employed. The combination of the fuzzy controller and the optimal working method for the combustion engine resulted in improvements in fuel consumption and battery state of health. To validate the GT-Power model, experiments were designed and conducted. Three control strategies (thermostat, power-follower, and Max-SOC) were simulated and compared to evaluate the fuel efficiency of the vehicle model. The power-follower strategy led to a 3.7% reduction in fuel consumption compared to the thermostat strategy (currently implemented as the RUNNA strategy), and a 60.1% reduction compared to the Max-SOC strategy. Moreover, using the power-follower method instead of a thermostat prevented frequent charging and discharging of the battery, which is beneficial for improving the battery's state of charge.

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“…Wang et al [20] proposed an energy management strategy based on the Pontryagin's minimum principle, which can ensure both overall economy and stable SOC, but it cannot adapt to the dynamic changes in complex traffic environments. To solve this problem, Nie et al [21] used the model predictive control method to carry out the energy scheduling of hybrid systems, which can effectively optimize driving performance and improve economy. In the field of non-road vehicles, there are few known studies on hydrogen FC power distribution.…”

Section: Introductionmentioning

confidence: 99%

Research on Power Optimization for Energy System of Hydrogen Fuel Cell Wheel-Driven Electric Tractor

Zhang,

Wang,

Zhang

et al. 2024

WEVJ

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Hydrogen fuel cell tractors are emerging as a new power source for tractors. Currently, there is no mature energy management control method available. Existing methods mostly rely on engineers’ experience to determine the output power of the fuel cell and the power battery, resulting in relatively low energy utilization efficiency of the energy system. To address the aforementioned problems, a power optimization method for the energy system of hydrogen fuel cell wheel-driven electric tractor was proposed. A dynamic model of tractor ploughing conditions was established based on the system dynamics theory. From this, based on the equivalent hydrogen consumption theory, the charging and discharging of the power battery were equivalent to the fuel consumption of the hydrogen fuel cell, forming an equivalent hydrogen consumption model for the tractor. Using the state of charge (SOC) of the power battery as a constraint, and with the minimum equivalent hydrogen consumption as the objective function, an instantaneously optimized power allocation method based on load demand in the energy system is proposed by using a traversal algorithm. The optimization method was simulated and tested based on the MATLAB simulation platform, and the results showed under ploughing conditions, compared with the rule-based control strategy, the proposed energy system power optimization method optimized the power output of hydrogen fuel cells and power batteries, allowing the energy system to work in a high-efficiency range, reducing the equivalent hydrogen consumption of the tractor by 7.79%, and solving the energy system power distribution problem.

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Co-optimization of speed planning and energy management for intelligent fuel cell hybrid vehicle considering complex traffic conditions

Cited by 36 publications

References 43 publications

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

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

New vehicle control strategy for fuel consumption and battery state of health improvement in a series hybrid vehicle utilizing fuzzy logic

Research on Power Optimization for Energy System of Hydrogen Fuel Cell Wheel-Driven Electric Tractor

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