Time-convolution based energy management strategy for fuel cell vehicles
To suit the rapid development of intelligent transportation and internet connected vehicles,this paper builds an intelligent transportation simulation dataset based on some partial maps of Beijing. A new time-convolution-based data-driven speed prediction model is designed and validated by combining the network structure of causal inflationary convolution and residual connection. To improve the energy efficiency of fuel cell vehicles, the multi-objective optimization function of equivalent hydrogen consumption and the constraints of power source life are established. To boost the real-time performance of the strategy, a convex optimization mathematical model of the energy management strategy is built, and the OSQP ( operator splitting quadratic program) solution algorithm is employed, reducing the lifetime decay of the power source by properly allocating the output power of the power source under the premise of satisfying the demanded power and the computational real-time performance. Our results show the proposed convex-optimized fuel cell vehicle energy management strategy based on intelligent transportation reduces the computation time by over 90％ compared with the dynamic planning and keeps the equivalent hydrogen consumption basically at the same level.
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