Browsing by Author "Zhang, Hanwen"

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    Battery temperature Prediction in electric vehicles using bayesian regularization
    (IEEE, 2024-07-02) Akpinar, R. Alp; Achanta, Sreekar; Fotouhi, Abbas; Zhang, Hanwen; Auger, Daniel J.
    This study is focused on developing a new temperature prediction model to be used in battery thermal management systems. Electric vehicle (EV) application is considered as a case study however, the proposed model is applicable in other applications too. The final goal is to improve batteries’ performance, durability, and safety. By specifically examining two types of batteries, which are Lithium Iron Phosphate (LFP) and Nickel Cobalt Aluminum (NCA), the proposed model utilizes Bayesian Regularization to precisely predict variations in the battery’s surface temperature in an EV application. The present study experimentally evaluates the accuracy of the proposed model for prediction of the batteries’ surface temperature under various conditions. According to the results, average errors of less than 0.1°C and 0.3°C are achieved when predicting the batteries’ surface temperature in 30 and 90 seconds ahead. This study is expected to have an impact on the advancement of EVs’ battery technologies by improving the battery’s performance and safety.
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    Battery temperature prediction using an adaptive neuro-fuzzy inference system
    (MDPI, 2024-03-01) Zhang, Hanwen; Fotouhi, Abbas; Auger, Daniel J.; Lowe, Matt
    Maintaining batteries within a specific temperature range is vital for safety and efficiency, as extreme temperatures can degrade a battery’s performance and lifespan. In addition, battery temperature is the key parameter in battery safety regulations. Battery thermal management systems (BTMSs) are pivotal in regulating battery temperature. While current BTMSs offer real-time temperature monitoring, their lack of predictive capability poses a limitation. This study introduces a novel hybrid system that combines a machine learning-based battery temperature prediction model with an online battery parameter identification unit. The identification unit continuously updates the battery’s electrical parameters in real time, enhancing the prediction model’s accuracy. The prediction model employs an Adaptive Neuro-Fuzzy Inference System (ANFIS) and considers various input parameters, such as ambient temperature, the battery’s current temperature, internal resistance, and open-circuit voltage. The model accurately predicts the battery’s future temperature in a finite time horizon by dynamically adjusting thermal and electrical parameters based on real-time data. Experimental tests are conducted on Li-ion (NCA and LFP) cylindrical cells across a range of ambient temperatures to validate the system’s accuracy under varying conditions, including state of charge and a dynamic load current. The proposed models prioritise simplicity to ensure real-time industrial applicability.