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<p>Given the current level of global pollution and the severe lack of fossil fuels, establishing electric vehicles (EVs) powered by clean energy sources is essential to resolving these issues. The most efficient approach is to develop a hybrid EV maintained on fossil fuels. Numerous academics have discovered that the output performance of hybrid designs is a crucial factor influencing the fuel life span. Improving the system's dynamic stability and maximizing energy efficiency are crucial to minimizing expenses associated with EVs. Therefore, the energy management system's (EMS) most suitable control strategy significantly impacts power system reliability and decreases expenses. This paper proposes energy management of EVs using Improved Genetic Algorithm (IGA) optimization and Differential Flatness (DF) control technique. Using DC-DC bidirectional converters, each source is linked in parallel to the DC-bus and provides a synchronous reluctance motor (SRM) based drive, and a supercapacitor-battery power system is applied. DF and an IGA combine complementary approaches to create fundamental forces for the proposed energy management methodology (EMM). The IGA is a quick optimization mechanism that enables it to adjust the DF gains in real time to maximize system performance. An entirely new control procedure is examined that is predicated on the non-linear differential flatness method.DF uses predefined trajectories to ensure the desired robust control proprieties that respect the system's physical properties. The differential flatness theory has created simple approaches for the unpredictable balance of systems issues and energy management. This is a powerful tool that guarantees the source's dynamic constraints. The principal objective of the suggested EMM is to ensure DC-bus stabilization, minimize DC-bus voltage ripples and voltage overshoots of 15&#xa0;V (3.1%), adhere to source dynamics, and meet the power prerequisite of the synchronous motor. Additionally, the algorithm reduces the drive-induced harmonics by 10.38%, which lowers the battery current ripple by 16.23A and lengthens the battery life. The simulation results for the proposed method are compared with various existing approaches such as PSO, SSA, and Classic model in terms of Total Harmonic Distortion (THD)% and the voltage overshoots. By analyzing the determined results, the proposed approach outperforms the existing methods.</p>

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A Novel Differential Flatness Control Approach of an Electric Vehicle Using Energy Management Methodology

  • 期刊论文

  • 发表日期：

    2025年5月5日

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  • S. Poorani,
  • P. Kathirvel,
  • T. Maris Murugan,
  • P. Josephin Shermila

摘要

Given the current level of global pollution and the severe lack of fossil fuels, establishing electric vehicles (EVs) powered by clean energy sources is essential to resolving these issues. The most efficient approach is to develop a hybrid EV maintained on fossil fuels. Numerous academics have discovered that the output performance of hybrid designs is a crucial factor influencing the fuel life span. Improving the system's dynamic stability and maximizing energy efficiency are crucial to minimizing expenses associated with EVs. Therefore, the energy management system's (EMS) most suitable control strategy significantly impacts power system reliability and decreases expenses. This paper proposes energy management of EVs using Improved Genetic Algorithm (IGA) optimization and Differential Flatness (DF) control technique. Using DC-DC bidirectional converters, each source is linked in parallel to the DC-bus and provides a synchronous reluctance motor (SRM) based drive, and a supercapacitor-battery power system is applied. DF and an IGA combine complementary approaches to create fundamental forces for the proposed energy management methodology (EMM). The IGA is a quick optimization mechanism that enables it to adjust the DF gains in real time to maximize system performance. An entirely new control procedure is examined that is predicated on the non-linear differential flatness method.DF uses predefined trajectories to ensure the desired robust control proprieties that respect the system's physical properties. The differential flatness theory has created simple approaches for the unpredictable balance of systems issues and energy management. This is a powerful tool that guarantees the source's dynamic constraints. The principal objective of the suggested EMM is to ensure DC-bus stabilization, minimize DC-bus voltage ripples and voltage overshoots of 15 V (3.1%), adhere to source dynamics, and meet the power prerequisite of the synchronous motor. Additionally, the algorithm reduces the drive-induced harmonics by 10.38%, which lowers the battery current ripple by 16.23A and lengthens the battery life. The simulation results for the proposed method are compared with various existing approaches such as PSO, SSA, and Classic model in terms of Total Harmonic Distortion (THD)% and the voltage overshoots. By analyzing the determined results, the proposed approach outperforms the existing methods.