{'fraction_non_alphanumeric': 0.016037735849056604, 'fraction_numerical': 0.0, 'mean_word_length': 5.672955974842767, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Battery Thermal Model Identification And Surface Temperature Prediction

Performance of a Li-ion battery is affected by temperature; low temperature causes reduced power output and high temperature affects state of health and compromises safety. To overcome these challenges and for reliable performance of batteries, thermal management is needed in electric vehicles. This paper presents a thermal-electrical equivalent circuit model to predict the surface temperature of a battery. Three algorithms are presented for the estimation of thermal-electrical equivalent model parameters. These algorithms are based on least square, constrained least squares, and weighted least squares respectively. It is shown that the performance of a battery thermal model parameter estimation approach can suffer from measurement noise. The performance of the algorithms are compared using computer simulations at various signal-to-noise levels. It is found that the weighted least squares based approach outperforms the other two approaches in parameter estimation accuracy.