Recent Advances and Applications of AI-Based Mathematical Modeling in Predictive Control of Hybrid Electric Vehicle Energy Management in China

Zhang, Qian; Tian, Shichao; Lin, Xinyan

doi:10.3390/electronics12020445

Cited by 14 publications

(6 citation statements)

References 36 publications

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“…The use of artificial intelligence for modelling is gaining popularity. In the current literature, similar modelling techniques can also be found in the context of modelling energy consumption by electric vehicles (EVs) [46][47][48][49].…”

Section: Discussionmentioning

confidence: 99%

Modelling CO2 Emissions from Vehicles Fuelled with CNG Based on On-Road and Chassis Dynamometer Tests

Mądziel

2024

Preprint

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Contemporary global policy, driven by concern for the environment, imposes increasingly stringent goals for reducing CO2 emissions. Therefore, there is an urgent need to develop effective models that allow an accurate prediction of CO2 emissions from vehicles powered by alternative fuels such as CNG. This article presents the process of creating one of the first models of CO2 emission for a vehicle powered by CNG. Emission modelling is based on data obtained from chassis dynamometer tests and road tests using the portable emission measurement system (PEMS). CO2 emission modelling was conducted in Python programming language using the Optuna framework for the XGBoost technique. The models obtained were validated, with indicators R2 0.9 and RMSE 0.49 for data from chassis dynamometer tests, and R2 0.7, RMSE 0.71 for road test data. This work has the potential to be used by transportation decision makers involved in environmental analyses and policymaking for urban areas.

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Section: Discussionmentioning

confidence: 99%

Modelling CO2 Emissions from Vehicles Fuelled with CNG Based on On-Road and Chassis Dynamometer Tests

Mądziel

2024

Preprint

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“…In their groundbreaking study, Wang et al [56] examined the impact of digital technologies on resource efficiency, highlighting how data-driven approaches can optimize production processes and minimize waste, thereby promoting sustainability. Building on this foundation, Zhang et al [57] explored the role of artificial intelligence in energy management systems, illustrating how intelligent algorithms could optimize energy consumption and reduce carbon emissions in China's industrial sectors. Furthermore, Hou et al [58] delved into the potential of block-chain technology to enhance transparency and traceability in supply chains, promoting responsible production and consumption patterns.…”

Section: Modelmentioning

confidence: 99%

Exploring the Synergistic Effects of Digitalization and Economic Uncertainty on Environmental Sustainability: An Investigation from China

Teng

Qiao

2023

Sustainability

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This study delves into the intricate interplay between digitization, economic uncertainty, and environmental sustainability, aiming to shed light on their multifaceted roles. Using an approach, the analysis spans 28 Chinese provinces over the period from 2000 to 2020, employing linear and nonlinear autoregressive distributed lag techniques to unveil symmetric and asymmetric impacts. The findings reveal the urgent need to address the challenges posed by economic uncertainty for effective environmental sustainability. Notably, a negative association between digitization and carbon dioxide emissions is identified, emphasizing its transformative potential in driving energy efficiency and sustainable practices. Furthermore, the study elucidates the detrimental influence of industrial structure on carbon dioxide emissions, highlighting the importance of balancing economic growth and environmental sustainability. The positive influence of urbanization on carbon dioxide emissions underscores the complexities of achieving sustainable development amidst rapid urbanization. By providing a comprehensive understanding of these dimensions, this study contributes to the scholarly discourse and aids in formulating evidence-based strategies for a greener and more sustainable future. The insights gained from this research can guide policymakers and stakeholders in navigating the complex dynamics between digitization, economic uncertainty, and environmental sustainability, fostering a more sustainable and resilient world.

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“…The feasibility of the strategy was verified by experiments. In addition, in recent years, the energy management strategy based on model predictive control has become the research focus of the automotive R&D industry [20]. The algorithm predicts the future driving state information of the vehicle through the prediction model, solves the local optimal problem in the finite time domain online and achieves optimal control under the whole working condition by real-time rolling optimization to achieve the global approximate optimal effect.…”

Section: Introductionmentioning

confidence: 99%

Research on Energy Management Strategy of Fuel Cell Tractor Hybrid Power System

Zhao,

Gao,

et al. 2024

WEVJ

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In recent years, more and more attention has been paid to fuel cell-based hybrid tractors. In order to optimize the global power distribution of tractors and further improve the fuel economy and fuel cell durability of the system, this paper designs an energy management strategy to maximize external energy efficiency based on fuel cell/lithium battery/supercapacitor hybrid tractors. This strategy aims to reduce the real-time hydrogen consumption of the system while maximizing the external energy output so as to reduce the impact of load randomness on the output power of the fuel cell. Under the typical ploughing conditions of the tractor, the simulation is compared with the state machine strategy and the equivalent hydrogen consumption minimization strategy. The results show that the proposed strategy meets the power requirements of a given ploughing condition, and compared with the two traditional strategy systems, the performance characteristics of auxiliary energy are more fully exerted. It reduces the burden on fuel cells and improves the durability of fuel cells. The hydrogen consumption of the system was reduced by 11.03 g and 16.54 g, respectively, improving the overall economy of the hybrid system.

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Recent Advances and Applications of AI-Based Mathematical Modeling in Predictive Control of Hybrid Electric Vehicle Energy Management in China

Cited by 14 publications

References 36 publications

Modelling CO2 Emissions from Vehicles Fuelled with CNG Based on On-Road and Chassis Dynamometer Tests

Modelling CO2 Emissions from Vehicles Fuelled with CNG Based on On-Road and Chassis Dynamometer Tests

Exploring the Synergistic Effects of Digitalization and Economic Uncertainty on Environmental Sustainability: An Investigation from China

Research on Energy Management Strategy of Fuel Cell Tractor Hybrid Power System

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