Battery health monitoring and prediction are critically important in the era of electric mobility with a huge impact on safety, sustainability, and economic aspects. Existing research often focuses on prediction accuracy but tends to neglect practical factors that may hinder the technology’s deployment in real-world applications. In this paper, we address these practical considerations and develop models based on the Bayesian neural network for predicting battery end-of-life. Our models use sensor data related to battery health and apply distributions, rather than single-point, for each parameter of the models. This allows the models to capture the inherent randomness and uncertainty of battery health, which leads to not only accurate predictions but also quantifiable uncertainty. We conducted an experimental study and demonstrated the effectiveness of our proposed models, with a prediction error rate averaging 13.9%, and as low as 2.9% for certain tested batteries. Additionally, all predictions include quantifiable certainty, which improved by 66% from the initial to the mid-life stage of the battery. This research has practical values for battery technologies and contributes to accelerating the technology adoption in the industry.


    Access

    Check access

    Check availability in my library

    Order at Subito €


    Export, share and cite



    Title :

    Practical Battery Health Monitoring using Uncertainty-Aware Bayesian Neural Network


    Contributors:
    Zhao, Yunyi (author) / Zhang, Wei (author) / Yan, Qingyu (author) / Ng, Man-Fai (author) / Sivaneasan, B. (author) / Xiang, Cheng (author)


    Publication date :

    2024-10-07


    Size :

    1429326 byte





    Type of media :

    Conference paper


    Type of material :

    Electronic Resource


    Language :

    English



    Uncertainty-based Sensor Fusion Architecture using Bayesian-LSTM Neural Network

    Geragersian, Patrick / Petrunin, Ivan / Guo, Weisi et al. | AIAA | 2023



    Multidisciplinary Optimization under Uncertainty Using Bayesian Network

    Liang, Chen / Mahadevan, Sankaran | British Library Conference Proceedings | 2016


    Multidisciplinary Optimization under Uncertainty Using Bayesian Network

    Liang, Chen / Mahadevan, Sankaran | SAE Technical Papers | 2016