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<p>The growing adoption of electric vehicles (EVs) highlights the need for efficient energy management strategies (EMS) to optimize hybrid energy storage systems (ESSs) and reduce operating costs; yet, traditional controllers struggle with nonlinear powertrain dynamics and fluctuating demand and supply conditions. To address this challenge, we propose a hybrid energy management framework that integrates a Takagi–Sugeno fuzzy controller with Portia spider optimization (PSO) and a mix style neural network (MSNN), termed PSO-MSNN. In this approach, PSO optimizes fuzzy controller parameters, while MSNN predicts optimal vehicle and system states for improved decision making. The main goal of the proposed PSO-MSNN technique is to minimize the overall operational cost of EV energy management systems, reduce energy consumption, and improve system efficiency. In this approach, PSO optimizes fuzzy controller parameters, while MSNN predicts optimal vehicle and system states for improved decision making. The method is validated in MATLAB and compared with the deep deterministic policy gradient algorithm (DDPGA), gray wolf optimization (GWO), and adaptive salp swarm algorithm (ASSA). The results show that PSO-MSNN achieves the lowest cost of 1$ and energy consumption of 710.20&#xa0;kJ. These outcomes demonstrate that PSO-MSNN significantly enhances cost efficiency, energy utilization, and adaptability, underscoring its potential for advancing robust and scalable EV energy management systems.</p>

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Optimizing electric vehicle energy management using PSO-MSNN and Takagi–Sugeno fuzzy control

  • 期刊论文

  • 发表日期：

    2026年2月27日

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  • M. Karthick,
  • S. Venkatesh Kumar,
  • C. Kathirvel,
  • S. Saravanan,
  • V. Ranjith

摘要

The growing adoption of electric vehicles (EVs) highlights the need for efficient energy management strategies (EMS) to optimize hybrid energy storage systems (ESSs) and reduce operating costs; yet, traditional controllers struggle with nonlinear powertrain dynamics and fluctuating demand and supply conditions. To address this challenge, we propose a hybrid energy management framework that integrates a Takagi–Sugeno fuzzy controller with Portia spider optimization (PSO) and a mix style neural network (MSNN), termed PSO-MSNN. In this approach, PSO optimizes fuzzy controller parameters, while MSNN predicts optimal vehicle and system states for improved decision making. The main goal of the proposed PSO-MSNN technique is to minimize the overall operational cost of EV energy management systems, reduce energy consumption, and improve system efficiency. In this approach, PSO optimizes fuzzy controller parameters, while MSNN predicts optimal vehicle and system states for improved decision making. The method is validated in MATLAB and compared with the deep deterministic policy gradient algorithm (DDPGA), gray wolf optimization (GWO), and adaptive salp swarm algorithm (ASSA). The results show that PSO-MSNN achieves the lowest cost of 1$ and energy consumption of 710.20 kJ. These outcomes demonstrate that PSO-MSNN significantly enhances cost efficiency, energy utilization, and adaptability, underscoring its potential for advancing robust and scalable EV energy management systems.