Co-Optimization Strategies for Connected and Automated Fuel Cell Hybrid Vehicles in Dynamic Curving Scenarios
SSRN, ISSN: 1556-5068
2022
- 190Usage
- 1Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Article Description
The performance of speed planning and energy management for connected and automated fuel cell hybrid vehicles (CAFCHVs) in the curve directly affects curve passage, operating safety and energy economy. However, the uncertain factors of complex traffic conditions (such as the dynamic state of the preceding and ego vehicle, road coefficient, and curve radius) and the lateral stability of CAFCHV lead to the difficulty of online speed planning and energy management. Therefore, a co-optimization strategy is implemented in this study. First, the gradient-based model prediction control (GRAMPC) leveraging the fast projection gradient method is adopted to calculate the safe and optimal speed sequence. Meanwhile, the energy management strategy (EMS) based on the Fuzzy adaptive equivalent consumption minimization strategy (Fuzzy-AECMS) is utilized for power distribution, and a multi-objective performance function is introduced to evaluate the total cost of hydrogen consumption and battery life extension. The simulation results reveal that the proposed strategy can obtain a safe and optimal speed sequence when CAFCHV operates on the curve road. And compared with the mode of tracking the speed of preceding vehicle, the hydrogen consumption, SOC, battery degradation, and total cost are reduced by 1.4%, 1.9%, 9.9%, and 1.8% regulated by the planning mode, respectively.
Bibliographic Details
Elsevier BV
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