A Sequential Network-model Alliance Module for Lithium-ion Battery Temperature Prediction

Li, Marui; Dong, Chaoyu; Li, Xiangke; Dong, Xisong; Mu, Yunfei; Jia, Hongjie

doi:10.1109/ecce50734.2022.9947649

Cited by 2 publications

(1 citation statement)

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“…An unscented Kalman filter (UKF) is integrated with the ETNN to achieve reliable coestimation of state of charge (SOC) and state of temperature (SOT), and experimental results demonstrate satisfactory co-estimation performance. Marui Li et al [19] applied a sophisticated algorithm involving multi-sequential neural networks and deep learning for battery state estimation. Andreas et al [20] adopted a Convolutional Neural Network (CNN) model for battery temperature prediction, which is trained with cross-domain data from the simulation, vehicle fleet, and weather stations.…”

Section: Introductionmentioning

confidence: 99%

Battery Temperature Prediction Using an Adaptive Neuro-Fuzzy Inference System

Zhang,

Fotouhi,

Auger

et al. 2024

Batteries

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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.

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Section: Introductionmentioning

confidence: 99%