Designing of hybrid architecture has greater importance in the development of electric vehicles to enhance the life cycle of the battery, to protect from nonlinearities and uncertainties of electrical energy storage systems. The objective of this paper is to design and apply the Adaptive Neuro-Fuzzy Inference rule-based controller with the semi-empirical strategy to protect from nonlinearities, uncertainties, and to improve efficiency in electric vehicles. In this paper, a fully active Li-Ion battery/Electric Double-Layer supercapacitor hybrid energy storage system used to decouple Li-Ion battery/Electric Double-Layer Supercapacitor from Direct Current bus and to generate Li-Ion battery current reference online semi-empirical rule-based energy management strategy used. The Control system is designed with the Adaptive Neuro-Fuzzy Interface rule-based controller to reduce non-linearity and different uncertainties of the energy storage system with two outputs battery current and DC bus voltage are chosen to measure control system design, which is tested under heavy and light load conditions. Results are validated using MATLAB/Simulink and the performance of the Adaptive Neuro-Fuzzy Interface rule-based controller is 16.96% and 9.81% greater than Robust Fractional Order Sliding Mode Controller under heavy and light load conditions.

中文翻译：

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基于自适应神经模糊推理规则的控制器在混合动力汽车中的应用

混合架构的设计在电动汽车的发展中具有更大的重要性，以提高电池的生命周期，以防止电能存储系统的非线性和不确定性。本文的目的是设计和应用基于自适应神经模糊推理规则的控制器和半经验策略，以防止非线性、不确定性和提高电动汽车的效率。在本文中，一种完全有源的锂离子电池/电动双层超级电容器混合储能系统用于将锂离子电池/电动双层超级电容器与直流母线解耦并生成锂离子电池电流参考在线半使用基于经验的基于规则的能源管理策略。控制系统采用基于自适应神经模糊接口规则的控制器设计，以减少非线性，并选择具有两个输出电池电流和直流母线电压的储能系统的不同不确定性来测量控制系统设计，并在以下条件下进行测试重载和轻载条件。结果使用 MATLAB/Simulink 进行验证，在重载和轻载条件下，基于自适应神经模糊接口规则的控制器的性能比稳健分数阶滑模控制器高 16.96% 和 9.81%。