Data-driven approach for short-term power demand prediction of fuel cell hybrid vehicles

Tao Zeng, Caizhi Zhang, Dong Hao, Dongpu Cao, Jiawei Chen, Jinrui Chen and Jin Li

Energy, 2020, vol. 208, issue C

Abstract: Due to slow internal mass transport, the fuel cell is a typical time-delay control object in vehicular hybrid powertrain. To yield better control effect, the predictive control is considered as an effective solution, in which the short-term power demand of vehicle is a key input variable and must be predicted accurately. However, a time-phase mismatch phenomenon usually occurs in prediction results when using non-iterative direct prediction method, resulting in poor prediction accuracy. This study systematically explains the mechanism of the studied time-phase mismatch and proposes a novel iterative learning framework (ILF) to reduce it. Several machine learning algorithms are compared to select a proper learning core for ILF. The results show that prediction RMSE reduces up to 76.8% and 65.0% for the power and power change rate predictions, respectively, comparing with non-iterative prediction manner. The least-squares support vector machine as the learning core of ILF achieves the best performance within the shortest runtime. Moreover, the proposed ILF predictor has a good adaptability to various driving conditions through more validations. The proposed ILF has better predictable ability for the future data comparing with classical recurrent time-series prediction method. The proposed ILF is expected to improve the accuracy of vehicle load-status perception.

Keywords: Fuel cell hybrid vehicles; Short-term power demand; Time-series prediction; Machine learning; Data-driven approach (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (18)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:208:y:2020:i:c:s0360544220314262

DOI: 10.1016/j.energy.2020.118319

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