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Hybrid optimized deep learning approach for prediction of battery state of charge, state of health and state of temperature

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Abstract

Lithium-ion batteries are becoming more popular due to their superior performance like high power density, long lifespan, broad operating range of temperatures, quick charging capabilities, and low self-discharge. The implementation of a Battery Management System (BMS) is crucial in order to guarantee the secure and optimal functioning of electric vehicle batteries. BMS monitors, controls, and maintains the health and performance of rechargeable batteries by monitoring the State of Charge (SoC), State of Health (SoH), and State of Temperature (SoT). SoC quantifies the amount of energy stored in a battery at a certain moment, and it is employed to approximate the remaining distance that may be covered. SoH describe the overall health or state of a rechargeable battery. It reveals how well a battery performs in comparison to its original state. SoT estimation keeps the battery temperature within a safe range, allowing it to reach a higher age and safety, which is critical for battery reliability. In this paper SoC, SoH, SoT estimation models for a lithium-ion battery have been developed using an improved EP-based R110-BLSTM approach. The Emperor Penguin based Residual Network-110 incorporated Bidirectional Long-Short Term Memory (EP-based R110-BLSTM) is ideal for estimating SoC, SoH, and SoT and features great accuracy, a quick estimation speed, and strong generalization capabilities. However, Extreme Learning Machine performance is heavily reliant on proper feature extraction. In order to enhance estimate performance by extracting the best features, Simulated-Annealing-based Golden Eagle optimization is utilized. Electric vehicle drive cycles are used to test the model’s resilience to temperature changes. MATLAB 2018b software is used in the execution of this research. The results demonstrate that the proposed model performs better in terms of accuracy and has lower SoC, SoH, and SoT error rates than existing models. A thorough comparison between the recommended model and existing methods is also made, further demonstrating the proposed model’s superiority.

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Authors and Affiliations

  1. Department of Electrical Engineering, National Institute of Technology Jamshedpur, Jamshedpur, Jharkhand, India

    Pooja Kumari & Niranjan Kumar

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  1. Pooja Kumari
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  2. Niranjan Kumar
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PK written the manuscript and NK provided the guidance.

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Correspondence to Pooja Kumari.

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Kumari, P., Kumar, N. Hybrid optimized deep learning approach for prediction of battery state of charge, state of health and state of temperature. Electr Eng 106, 1283–1290 (2024). https://doi.org/10.1007/s00202-023-02105-w

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