Research on coordinated control of electro-hydraulic composite braking for an electric vehicle based on the Fuzzy-TD3 deep reinforcement learning algorithm

IF 5.4 2区 计算机科学 Q1 AUTOMATION & CONTROL SYSTEMS Control Engineering Practice Pub Date : 2025-01-21 DOI:10.1016/j.conengprac.2025.106248
Zhengrong Chen , Renkai Ding , Qin Zhou , Ruochen Wang , Binggen Zhao , Yinsheng Liao
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Abstract

In order to improve the energy utilization efficiency of electric vehicles, alleviate range anxiety, and ensure braking stability and comfort, a coordinated control strategy of electro-hydraulic composite braking (EHB) is proposed based on the fuzzy twin delayed deep deterministic policy gradient (Fuzzy-TD3) algorithm. A mathematical EHB system model is established. A particle swarm backpropagation (PSO-BP) neural network is used to determine the braking intensity of driver. Combined with the Fuzzy-TD3 algorithm to optimize the distribution of regenerative braking torque and hydraulic braking torque under normal braking, efficient recovery of braking energy is achieved to ensure braking stability and comfort. For emergency braking, the coordinated control of the anti-lock braking system (ABS) and the regenerative braking system (RBS) is realized by combining the sliding mode control (SMC) and the Fuzzy-TD3 algorithm. This effectively lowers the risk of wheel slip during emergency braking and enhances safety and ride comfort. The results show that compared to the conventional PID control method, the Fuzzy-TD3 control strategy lowers braking time by 11.5 % and 9.5 % under normal and emergency braking conditions, respectively. Additionally, the state of charge (SOC) of the battery increases by 0.487 % and 0.266 %, respectively. These findings are consistent with experimental data and validate the effectiveness of this strategy in improving braking performance and energy recovery efficiency.
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来源期刊
Control Engineering Practice
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.
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