article · Applied Sciences
Precise state of charge estimation in lithium-ion batteries is vital for safe battery management systems, especially in electric vehicles. Three machine learning algorithms, namely a deep neural network, a gated recurrent unit, and a long short-term memory model, were evaluated for state of charge estimation. These models were trained and tested using laboratory data from an 18650 battery cell alongside simulation data for a lithium cobalt oxide cell. The framework accounts for fluctuating operating temperatures and capacity degradation caused by cycling. Notably, the deep neural network incorporates both temperature and ageing factors alongside a variable current profile across both charging and discharging stages. Benchmarking demonstrated that the deep neural network delivered superior accuracy compared to the recurrent models, maintaining a maximum estimation error below 2.5 percent across varied operating conditions.
Accurate tracking of battery charge is essential for the reliable and safe operation of electric vehicles. Standard estimation methods often overlook how battery ageing and temperature fluctuations affect performance over time. Demonstrating that deep neural networks can track charge levels across varied temperatures and cell degradation with less than 2.5 percent error helps improve real-time battery monitoring.
This technology is aimed at battery management systems for electric vehicle manufacturers and energy storage developers needing real-time state of charge tracking under variable temperatures and cell ageing. Because the system was developed and validated using cell-level laboratory measurements and Matlab/Simulink simulations, it sits at an applied, laboratory-tested stage that requires embedded hardware integration and full-pack validation before commercial deployment.
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Accurate estimation of the state of charge (SoC) of lithium-ion batteries is crucial for battery management systems, particularly in electric vehicle (EV) applications where real-time monitoring ensures safe and robust operation. This study introduces three advanced algorithms to estimate the SoC: deep neural network (DNN), gated recurrent unit (GRU), and long short-term memory (LSTM). The DNN, GRU, and LSTM models are trained and validated using laboratory data from a lithium-ion 18650 battery and simulation data from Matlab/Simulink for a LiCoO2 battery cell. These models are designed to account for varying temperatures during charge/discharge cycles and the effects of battery aging due to cycling. This paper is the first to estimate the SoC by a deep neural network using a variable current profile that provides the SoC curve during both the charge and discharge phases. The DNN model is implemented in Matlab/Simulink, featuring customizable activation functions, multiple hidden layers, and a variable number of neurons per layer, thus providing flexibility and robustness in the SoC estimation. This approach uniquely integrates temperature and aging effects into the input features, setting it apart from existing methodologies that typically focus only on voltage, current, and temperature. The performance of the DNN model is benchmarked against the GRU and LSTM models, demonstrating superior accuracy with a maximum error of less than 2.5%. This study highlights the effectiveness of the DNN algorithm in providing a reliable SoC estimation under diverse operating conditions, showcasing its potential for enhancing battery management in EV applications.
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DOI: 10.3390/app14156648
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