On-Board State-of-Charge Estimation of Li-Ion Battery in Hybrid Electric Aircraft Vehicles Using State Estimators – Case Study

Tudoroiu, Roxana-Elena; Radu, Sorin Mihai

doi:10.19062/2247-3173.2018.20.35

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2022

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“…To supervise the operating SOH under the operation of battery usage habits, a feed forward neural network(FFNN) is adopted to build the forecasting model in this research. Historical operational data effectively extracted by the big data platform generates the performance of the framework [5]. The information related to battery capacity decay will comprehensively reflect the status of battery health.…”

Section: Introductionmentioning

confidence: 99%

Lithium battery SOH estimation through FFNN

Liu²,

Wang

et al. 2022

J. Phys.: Conf. Ser.

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Abstract:The battery health plays a key and decisive role in the use of lithium batteries. In this paper, the battery data detected offline is used to predict SOH through the big data platform algorithm model, in order to acknowledge the current health of the battery. The construction of the learning model is carried out through the historical data of 1000 batteries data set, so that the MAE(mean absolute error) is lower than 0.095%.

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Section: Introductionmentioning

confidence: 99%