Control strategy optimization of plug-in hybrid electric vehicle based on driving data mining

Compared to traditional fuel vehicles, the structure of pure electric vehicles (BEVs) and the actual driving behaviour of users have changed. Therefore, the original durability evaluation conditions of traditional fuel vehicles cannot fully cover the use of new energy vehicles. In the past, the determination of durability targets was mainly based on user data collection, but this work required a lot of manpower and material resources to meet the engineering requirements. In this paper, the fuzzy clustering method is used to mine the user trajectory to obtain the user-based endurance target of pure electric vehicle, and then according to the durability target, the particle swarm method is used to correlate the user behaviour and the proving ground, and the proving ground test method of electric drive system is developed. Studies have shown that user data mining methods can obtain more user information, so as to better formulate durable target close to users. The particle swarm algorithm can improve the simulation correlation accuracy and reduce the iteration time, which shortens the simulation iteration time by more than 80% compared with polynomials. The test acceleration ratio of 7:1 in relation to user behaviour with the proving ground. During the durability test of the electric drive system of pure electric vehicles, it is found that the test specification can well reflect the user’s motor operation during actual driving.

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Research on the durability test method of electric driving systems based on fuzzy clustering and particle swarm algorithm

Wang

Cheng

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part D:

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Driving cycle prediction based on Markov chain combined with driving information mining

Sun

Tao

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part D:

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Driving cycle prediction (DCP) is of great importance for vehicle’s awareness of surrounding environment and optimization of control strategy. It has won much attention around the world. However, the used driving database could not reflecting the great diversity in multi driving cycles in real world. The characteristic parameters could not represent the complex and diverse driving conditions. Therefore, DCP models’ adaptability and generalization ability are limited. Moreover, prediction methods in existing researches always take large computation burden, difficult to apply in practice. To tackle these issues, the present research focus on DCP based on Markov chain combined with driving information mining (DIM). Firstly, many standard test cycles and real cycles are collected to construct an database of great diversity. Secondly, DIM technologies are studied to extract the optimal parameter set reflecting driving characteristics and determine the driving cycle categories. Thirdly, prediction method based on recursive self-learning Markov chain is proposed. Recursive equation of Markov state transition probability matrix (TPM) is deduced to save computation time. The key parameter of prediction method is adjusted to achieve optimal prediction performance. Synthesized standard test driving cycles (SSTDC) and real driving cycles (RDC) in database, as well as a new real driving cycle (NRDC) are tested. Driving pattern prediction accuracy of the proposed method is 99.25% for SSTDC, 100.00% for RDC, and 99.84% for NRDC, respectively. Compared with the benchmark method, the prediction accuracy is improved by 18.87% for SSTDC, 10.07% for RDC and 23.51% for NRDC, respectively. In addition, processing time of the prediction program is significantly reduced by 2.23 s (36.14%) for SSTDC, 12.30 s (51.25%) for RDC and 3.99 s (52.16%) for NRDC, respectively. It can be verified that the proposed method has high precision, short response time, as well as good adaptability and charming generalization ability for new cycles.

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Applications of Clustering Methods for Different Aspects of Electric Vehicles

2023

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The growing penetration of electric vehicles can pose several challenges for power systems, especially distribution systems, due to the introduction of significant uncertain load. Analysis of these challenges becomes computationally expensive with higher penetration of electric vehicles due to various preferences, travel behavior, and the battery size of electric vehicles. This problem can be addressed using clustering methods which have been successfully used in many other sectors. Recently, there have been several studies published on applying clustering methods for various aspects of electric vehicles. To summarize the existing efforts and provide future research directions, this contribution presents a three-step analysis. First, the existing clustering methods, including hard and soft clustering, are discussed. Then, the recent literature on the application of clustering methods for different aspects of electric vehicles is reviewed. The review concentrates on four major aspects of electric vehicles: the behavior of the user, driving cycle, used batteries, and charging stations. Then, several representative studies are selected from each category and their merits and demerits are summarized. Finally, gaps in the existing literature are identified and directions for future research are presented. They indicate the need for further research on the impact on distribution circuits, charging infrastructure during emergencies, equity and disparity in rebate allocations, and the use of big data with cluster analysis to assist transportation network management.

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Control strategy optimization of plug-in hybrid electric vehicle based on driving data mining

Cited by 3 publications

References 21 publications

Research on the durability test method of electric driving systems based on fuzzy clustering and particle swarm algorithm

Research on the durability test method of electric driving systems based on fuzzy clustering and particle swarm algorithm

Driving cycle prediction based on Markov chain combined with driving information mining

Applications of Clustering Methods for Different Aspects of Electric Vehicles

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