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<p>Reliable prediction of the State-of-Health (SOH) of lithium-ion batteries is essential to guarantee the safety, robustness and lifetime of the electric vehicles and grid-scale energy storage devices. Although data-driven methods can provide viable alternatives to traditional model-based algorithms, despite these approaches, high computational complexity, overfitting, and poor feature extraction are common barriers to the use of these approaches in real-time battery management systems (BMS). To overcome these issues, this paper will suggest a light and precise SOH estimation model that incorporates an Improved MobileNet architecture and a Modified Poor and Rich Optimization (MPRO) algorithm. The Improved MobileNet has been designed with 1D temporal battery data particularly, which uses depthwise separable convolutions and Squeeze-and-Excitation attention units to help better represent features at the cost of minimal computational cost. The MPRO algorithm is improved by chaotic map based initializations and adaptive search strategies, which are used to automatically tune the important hyperparameters of the model to optimise its performance. Tested on the NASA, CALCE and Oxford battery data, the offered method has a state-of-the-art Root Mean Square Error (RMSE) equal to 0.48% compared to Transformer-based models with 29.41% and the default MobileNet with 41.46%. Having a small 1.1&#xa0;million parameter count and an inference time of 3.2 ms, the framework provides an effective and deployable SOH monitoring framework in resource-constrained BMS settings.</p>

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An improved MobileNet based on a modified poor and rich optimization algorithm for lithium-ion battery state-of-health estimation

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月7日

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  • Rejab Hajlaoui,
  • Mohamed Shalaby,
  • Raed H. C. Alfilh,
  • Narinderjit Singh Sawaran Singh

摘要

Reliable prediction of the State-of-Health (SOH) of lithium-ion batteries is essential to guarantee the safety, robustness and lifetime of the electric vehicles and grid-scale energy storage devices. Although data-driven methods can provide viable alternatives to traditional model-based algorithms, despite these approaches, high computational complexity, overfitting, and poor feature extraction are common barriers to the use of these approaches in real-time battery management systems (BMS). To overcome these issues, this paper will suggest a light and precise SOH estimation model that incorporates an Improved MobileNet architecture and a Modified Poor and Rich Optimization (MPRO) algorithm. The Improved MobileNet has been designed with 1D temporal battery data particularly, which uses depthwise separable convolutions and Squeeze-and-Excitation attention units to help better represent features at the cost of minimal computational cost. The MPRO algorithm is improved by chaotic map based initializations and adaptive search strategies, which are used to automatically tune the important hyperparameters of the model to optimise its performance. Tested on the NASA, CALCE and Oxford battery data, the offered method has a state-of-the-art Root Mean Square Error (RMSE) equal to 0.48% compared to Transformer-based models with 29.41% and the default MobileNet with 41.46%. Having a small 1.1 million parameter count and an inference time of 3.2 ms, the framework provides an effective and deployable SOH monitoring framework in resource-constrained BMS settings.