How to optimize battery thermal management systems?

The balance between temperature and pressure drop of the BTMs is realized. Numerical computation and repeated experiments are the main optimization methods used in traditional battery thermal management systems (BTMs) to obtain a better structure by changing a single variable, which incurs higher time cost.

How does a battery management system work?

Internal operating constraints such as temperature, voltage, and current are monitored and controlled by the BMS when the battery is being charged and drained. To achieve a better performance, the BMS technically determines the SoC and SoH of the battery.

How can machine learning and IoT improve battery performance?

Additionally, the integration of machine learning- and IoT-based algorithms with data-driven methods enhances the performance matrix of the system and results in a precise estimation of the battery state.

What is the thermal management performance of a battery?

The thermal management performance of the above optimized design parameters is experimentally verified, and the battery is subjected to 10 cycles at 1C, 2C, 3C rate to obtain the temperature rise and temperature difference curve. It can be observed that the temperature is in a smooth state and is not affected by the number of cycles.

How to predict the thermal performance of a battery?

The response surfaces of input and output variables are established by surrogate model. A BP neural network prediction model is established to predict the thermal performance of the battery. The parameter is automatically adjusted using NSGA-II algorithm. The balance between temperature and pressure drop of the BTMs is realized.

How can machine learning improve battery management?

Obuli et al. used machine learning algorithms consisting of SVM, NN, and Gaussian process regression to enhance the SoC estimation of LIBs for real-time data. The implemented technique offers a trustworthy data-driven system that improves battery management through accurate real-time state-of-charge monitoring, enabled by advanced analytics.