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<p>The target plug-in hybrid electric vehicle (PHEV) uses a lithium-ion phosphate battery to power its energy storage system (ESS). Lithium-ion batteries often charge and discharge due to the need for quick output and input power, which accelerates battery ageing and causes PHEVsƒ_T capacity to significantly deteriorate. This research proposes a hybrid technique for the battery anti-aging control of a PHEV. The proposed strategy is the combination of both the waterwheel plant algorithm (WWPA) and Finite Basis Physics-Informed Neural Networks (FBPINNs), and itƒ_Ts known as WWPA- FBPINNs method. The main objective of the proposed method is to increase battery life and lower the systemƒ_Ts entire cost. The WWPA is used to reduce lifecycle costs and minimize battery aging. The FBPINN approach is used to forecast the degradation of the battery over time. The proposed strategy is executed in the MATLAB platform, and contrasted with existing methods. The proposed strategy outperforms the current techniques, such as particle swarm optimization (PSO), graph neural network (GNN), and salp swarm optimization (SSO). The outcome demonstrates that the proposed strategy is less costly than the ones that are already in use. The proposed method outperforms existing methods, achieving 97% accuracy, 155.9% battery service life improvement, and a computation time of 50 s. This result demonstrates the proposed methodƒ_Ts superior performance in accurately modeling battery aging, effectively minimizing charge and discharge cycles, reducing degradation, and enhancing battery longevity.</p>

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Revolutionizing plug-in hybrid vehicles by energy storage system optimization: prolonging battery lifespan and minimizing degradation

  • 期刊论文

  • 发表日期：

    2026年1月9日

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  • P. Rajesh,
  • Francis H. Shajin,
  • Logeswaran Thangamuthu,
  • H. Umesh Prabhu

摘要

The target plug-in hybrid electric vehicle (PHEV) uses a lithium-ion phosphate battery to power its energy storage system (ESS). Lithium-ion batteries often charge and discharge due to the need for quick output and input power, which accelerates battery ageing and causes PHEVsƒ_T capacity to significantly deteriorate. This research proposes a hybrid technique for the battery anti-aging control of a PHEV. The proposed strategy is the combination of both the waterwheel plant algorithm (WWPA) and Finite Basis Physics-Informed Neural Networks (FBPINNs), and itƒ_Ts known as WWPA- FBPINNs method. The main objective of the proposed method is to increase battery life and lower the systemƒ_Ts entire cost. The WWPA is used to reduce lifecycle costs and minimize battery aging. The FBPINN approach is used to forecast the degradation of the battery over time. The proposed strategy is executed in the MATLAB platform, and contrasted with existing methods. The proposed strategy outperforms the current techniques, such as particle swarm optimization (PSO), graph neural network (GNN), and salp swarm optimization (SSO). The outcome demonstrates that the proposed strategy is less costly than the ones that are already in use. The proposed method outperforms existing methods, achieving 97% accuracy, 155.9% battery service life improvement, and a computation time of 50 s. This result demonstrates the proposed methodƒ_Ts superior performance in accurately modeling battery aging, effectively minimizing charge and discharge cycles, reducing degradation, and enhancing battery longevity.