Shifting strategy for electric heavy trucks with automated manual transmission based on extended Kalman filtering and reinforcement learning

IF 5.4 2区计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS

Control Engineering Practice Pub Date : 2025-03-26 DOI:10.1016/j.conengprac.2025.106324

Mingwei Zhou, Dongye Sun, Can Wang, Jiezhong Wang

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引用次数: 0

Abstract

To enhance the energy efficiency of electric vehicles and improve their adaptability to dynamic driving environments, this study utilized multigear automated manual transmission (AMT) electric heavy trucks as the research object and proposed an optimal system efficiency shifting strategy based on extended Kalman filtering and deep deterministic policy gradient (EKF-DDPG) algorithm correction. First, based on the integrated bond graph model, the loss mechanism and dynamic efficiency characteristics of the electric drive system were analyzed, and a shifting strategy based on the optimal system efficiency was developed. Second, considering the influence of vehicle mass and slope, the EKF-DDPG algorithm was used to correct the shifting strategy based on optimal system efficiency offline. Finally, the effectiveness and superiority of the proposed strategy were verified through a combination of simulations and real vehicle experiments. The research results indicate that the proposed strategy achieves real-time control of the optimal output efficiency of the electric drive system, correction of the shift curve to decrease cyclic shifting in dynamic driving environments, and a 4.45% reduction in vehicle energy consumption compared to traditional economical shifting strategy.

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来源期刊

Control Engineering Practice 工程技术-工程：电子与电气

CiteScore

9.20

自引率

12.20%

发文量

183

审稿时长

44 days

期刊介绍： Control Engineering Practice strives to meet the needs of industrial practitioners and industrially related academics and researchers. It publishes papers which illustrate the direct application of control theory and its supporting tools in all possible areas of automation. As a result, the journal only contains papers which can be considered to have made significant contributions to the application of advanced control techniques. It is normally expected that practical results should be included, but where simulation only studies are available, it is necessary to demonstrate that the simulation model is representative of a genuine application. Strictly theoretical papers will find a more appropriate home in Control Engineering Practice''s sister publication, Automatica. It is also expected that papers are innovative with respect to the state of the art and are sufficiently detailed for a reader to be able to duplicate the main results of the paper (supplementary material, including datasets, tables, code and any relevant interactive material can be made available and downloaded from the website). The benefits of the presented methods must be made very clear and the new techniques must be compared and contrasted with results obtained using existing methods. Moreover, a thorough analysis of failures that may happen in the design process and implementation can also be part of the paper. The scope of Control Engineering Practice matches the activities of IFAC. Papers demonstrating the contribution of automation and control in improving the performance, quality, productivity, sustainability, resource and energy efficiency, and the manageability of systems and processes for the benefit of mankind and are relevant to industrial practitioners are most welcome.