Journal
JOURNAL OF POWER SOURCES
Volume 523, Issue -, Pages -Publisher
ELSEVIER
DOI: 10.1016/j.jpowsour.2021.230910
Keywords
Model predictive control; Optimal control; Eco-driving; Energy management; Energy efficiency
Funding
- National Natural Science Foundation of China [U1964206]
- Key-Area Research and Development Program of Guangdong Province [2019B090909001]
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In this study, a simultaneous method and a hierarchical method are developed for active speed planning and powertrain energy management strategy. A modified hierarchical method is proposed by modifying the decoupling strategy of the hierarchical method, achieving similar energy saving effectiveness and high computational efficiency compared to the simultaneous method.
Adapting to the instantaneous disturbance in the driving environment and balancing the optimality and computational efficiency of control algorithms are two major challenges for the integrated optimization of active speed planning and powertrain energy management strategy. In this study, utilizing the framework of model predictive control, a simultaneous method (SM) and a hierarchical method (HM) are developed to serve as benchmarks for control optimality and computational efficiency, respectively. Then, by modifying the decou-pling strategy of the HM, this study ultimately proposes a modified HM which achieves similar control effec-tiveness in energy saving as that of the SM and preserves high computational efficiency. The comparative validation demonstrates that, due to fierce acceleration/deceleration operation caused by the heuristic decou-pling strategy adopted in the HM, the energy consumption provided by the HM is 221.5% in traffic flow scenarios (and 633.5% in the manually designed scenario) of that generated by the SM. However, by adopting the soft constraint on acceleration magnitudes, the modified HM narrows the sub-optimality in energy consumption to 3.95% compared with the SM, and it also realizes a 55.81% improvement in computation efficiency compared with the original HM.
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