Internal temperature prediction of ternary polymer lithium‐ion battery pack based on
            <scp>CNN</scp>
            and virtual thermal sensor technology

Wang, Mengyi; Hu, Weifeng; Jiang, Yanfang; Su, Fang; Zheng, Fang

doi:10.1002/er.6699

Cited by 18 publications

(4 citation statements)

References 35 publications

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“…108 Researchers have still devoted themselves to using ML to forecast the battery's internal thermal behavior. Up till now, multi-features such as cell external temperature, depth of discharge, nominal capacity, ambient temperature, and discharge rate have been utilized to train the ML, realizing a prediction of thermal effects (generally expressed as heat generation rate 109,110 /internal temperature 111 /external temperature 112,113 ).…”

Section: Thermal-based Tasksmentioning

confidence: 99%

Feature–target pairing in machine learning for battery health diagnosis and prognosis: A critical review

Huang

Sugiarto

2023

EcoMat

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Lithium‐ion batteries (LIBs) have been dominating the markets of electric vehicles and grid energy storage. Accurate monitoring of battery health status has been one of the most critical challenges of the battery industry. Machine learning (ML) has been widely applied to battery health estimation as well as prediction. Here, by investigating the specific features and targets, we comprehensively discuss task‐oriented ML implementation in various application scenarios in the field of battery health. This review explores the tasks assisted by ML based on multi‐level cell degradation. We highlight opportunities and significance of considering the potential feature–target pair during the ML model training to identify more health information about LIBs as well as shed light into designing tasks for new application scenarios.

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Section: Thermal-based Tasksmentioning

confidence: 99%

Feature–target pairing in machine learning for battery health diagnosis and prognosis: A critical review

Huang

Sugiarto

2023

EcoMat

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“…Seho Park et al [21] developed a technique to predict the battery temperature change trend based on the driving pattern and adopted the UDDC driving cycle and the cooling effects with empirical function. Mengyi Wang et al [22] applied a CNN model for a battery pack's internal temperature prediction with good visual analysis and soft sensor technology with big data size that counts the thermal convection rate and thermal dissipation rate. Kaizheng Fang et al [23] developed an ANN model to predict the surface temperature of a Ni-MH battery during charging under different ambient temperatures.…”

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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“…The carbon neutrality proposal has promoted clean energy development in recent years. 1 , 2 Electric vehicles (EVs) are investigated as the appropriate replacement for the conventional internal combustion engine-based vehicle to reduce greenhouse gas emissions and pollution, such as carbon dioxide (CO 2 ). 3 As a renewable power source, batteries make the EV efficient and environmentally friendly.…”

Section: Introductionmentioning

confidence: 99%