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Cited by 165 publications
(69 citation statements)
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References 39 publications
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“…Gong et al exploited ITS and neural networks to predict the trend of future vehicle speeds and then used dynamic programming algorithms for energy optimization and power allocation. Moreover, learning‐based energy management strategies from recorded historical or driving data have also been developed . The internet of vehicles can transmit the vehicle and traffic information back to the cloud platform and then use the technologies such as cloud computing to learn and optimize EMS.…”
Section: Randd Of Bms Functionality and Integration In Vehiclesmentioning
confidence: 99%
“…Gong et al exploited ITS and neural networks to predict the trend of future vehicle speeds and then used dynamic programming algorithms for energy optimization and power allocation. Moreover, learning‐based energy management strategies from recorded historical or driving data have also been developed . The internet of vehicles can transmit the vehicle and traffic information back to the cloud platform and then use the technologies such as cloud computing to learn and optimize EMS.…”
Section: Randd Of Bms Functionality and Integration In Vehiclesmentioning
confidence: 99%
“…Marzougui et al 50 described an energy management algorithm for FCHEV using MATLAB/Simulink and validated experimentally with real-time controller with digital signal processing and control engineering. Zhou et al 51 proposed an online energy management based on optimized offline fuzzy logic controllers with data fusion approach for three different types of road conditions. A probabilistic support vector machine online controller results are compared with HiL tests.…”
Section: Experimental Evolutionmentioning
confidence: 99%
“…Inspired by the idea of Zhou and colleagues, 24,25 the contributions in this article are summarized as follows:…”
Section: Introductionmentioning
confidence: 99%