Optimization-oriented adaptive equivalent consumption minimization strategy based on short-term demand power prediction for fuel cell hybrid vehicle

• Published in: Energy (Oxford) Vol. 227; p. 120305
• Main Authors: Zeng, Tao, Zhang, Caizhi, Zhang, Yanyi, Deng, Chenghao, Hao, Dong, Zhu, Zhongwen, Ran, Hongxu, Cao, Dongpu
• Format: Journal Article
• Language: English
• Published: Oxford Elsevier Ltd 15.07.2021; Elsevier BV
• Subjects: Battery charge sustaining capability; Consumption; Energy efficiency; Energy management; Equivalence; Equivalent consumption minimization; Extreme values; Fossil fuels; Fuel cell hybrid vehicle; Fuel cells; Fuel consumption; Fuel economy; Fuel technology; Hybrid vehicles; Local optimization; Optimization; Power consumption; Power prediction; Predictions; State of charge; Equivalent consumption minimization; Battery charge sustaining capability; Fuel cell hybrid vehicle; Power prediction; Energy management
• ISSN: 0360-5442; 1873-6785
• DOI: 10.1016/j.energy.2021.120305

The fuel economy and battery charge sustaining capability are two key criteria for the energy management of a full-power fuel cell hybrid vehicle equipped with small-capacity battery pack. In order to achieve stable battery charge sustenance and near-optimal fuel consumption, this study proposes an optimization-oriented adaptive equivalent consumption minimization strategy (A-ECMS) based on demand power prediction achieved via an iterative predictor. The proposed strategy updates the optimal equivalent factor periodically via local optimization process according to the predicted power to converge the state of charge (SOC) and guarantee fuel economy. The simulation results show that the iterative predictor has considerable accuracy, and the correlation between the predicted data and the real data reaches up to 0.987. The proposed strategy can quickly recover the battery SOC within 40 s in a 500-s driving cycle, which is shorter than existing feedback-oriented A-ECMS. At charge sustaining stage, the proposed strategy maintains the battery SOC around the reference value with an extremely low fluctuation degree of 0.36. The equivalent fossil-fuel consumption of the proposed strategy is 8.003 L/100 km, which is lower than that of existing A-ECMS. Besides, further investigation reveals that the proposed strategy has robust performance against the disturbance of power prediction errors. [Display omitted] •An optimization-oriented predictive adaptive equivalent consumption minimization strategy is developed.•A short-term demand power predictor is designed based on iterative learning framework.•A local optimization method is proposed to update near-future optimal equivalent factor periodically.•Better battery charge sustaining capability is achieved while guaranteeing fuel economy.