Long Short-Term Memory Approach to Predict Battery SOC
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Abstract
Estimating the ‘State of Charge’ (SOC) is a complex endeavour. Data-driven techniques for SOC estimation tend to offer higher prediction accuracy compared to traditional methods. With the progression of Artificial Intelligence (AI), machine learning has found extensive applications across various fields such as infotainment, driver assistance systems, and autonomous vehicles. This paper categorizes the machine learning techniques utilized in Battery Management System (BMS) applications and employs a modern supervised neural network approach to predict SOC. Accurate SOC estimation is crucial to prevent battery failures in critical situations, such as during heavy traffic or when traveling with limited access to charging stations. Long Short-Term Memory (LSTM) networks are particularly adept at classifying, processing, and predicting based on time series data. These models are capable of capturing and retaining features over time, making them suitable for this study. The model's predicted SOC closely matches the true SOC, and the SOC prediction error remains nearly zero even with a large sample of input data.
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