考虑不同时域的商用车预见性巡航控制

doi:10.19562/j.chinasae.qcgc.2024.11.011

Abstract

Abstract:

Predictive cruise control （PCC） performs long-term speed planning at the planning layer with the objective of predicting energy savings and short-term tracking control for the vehicle speed at the execution layer. Integrating these layers into a single optimal control problem poses significant challenges in system design due to the different time scale step requirements between the planning layer and the execution layer. To address this challenge， a hierarchical control approach is adopted in this paper. At the planning layer， an improved twin delayed deep deterministic policy gradient （TD3） algorithm is utilized to determine the long-term planning speed over the prediction horizon. Meanwhile， at the execution layer， based on model predictive control （MPC）， taking the planned vehicle speed as the reference speed and considering engine fuel consumption characteristics and transmission shift laws， further economic optimization and tracking control of the planned speed are carried out in the short term. The hardware-in-the-loop （HIL） validation results show that combining the improved TD3 algorithm with MPC effectively resolves the time scale inconsistency between planning and execution in PCC， which can significantly reduce both fuel consumption and shift frequency during the cruising of heavy-duty commercial vehicles.

Key words: predictive cruising, speed planning and control, deep reinforcement learning, model predictive control

Xiaohu Geng,Yao Fu,Jie Wang,Yulong Lei,Weidong Liu,Yuhai Wang,Ke Liu. Predictive Cruise Control for Commercial Vehicles Considering Different Time Domains[J].Automotive Engineering, 2024, 46(11): 2046-2058.

Figures/Tables 29

参考速度序列生成
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$f o r ? i = 0 ? t o ? N p ? d o :$
$???? u k + i = T D 3 P C C (s h k + i, v h k + i, α (s h k + i))$
$???? [s h k + i + 1, v h k + i + 1] T = f ([s h k + i, v h k + i] T, u (k + i))$
$???? v r e f · a p p e n d (v h (k + i + 1))$
End for

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参数	数值或设置
参数	策略网络	价值网络
输入层维度	3	4
隐藏层1和2	128/无	128/128
输出层	1	1
隐藏层激活函数	ReLU	ReLU
输出层激活函数	tanh	tanh
优化器	Adam	Adam

参数	数值或设置
策略网络学习率	5e-4
价值网络学习率	5e-3
回报衰减率	0.98
软更新参数	0.000 5
经验回放数组维度	10 000
训练最小经验回放维度	1 000
单次更新采样经验回放数组	64
预测数组	200
单次更新采样预测数组	20
OU噪声标准差/均值	0.15/0
OU噪声回归系数	0.15

项目	CC	RL规划	RL-MPC
燃油消耗/kg	1.368	1.283	1.268
换挡次数	6	4	4
行驶距离/m	3 999.99	4 004.09	4 004.09

算法	CC	RL规划	RL-MPC
燃油消耗/kg	9.525	8.573	8.232
换挡次数	118	62	51
行驶距离/m	26 500	26 500	26 500

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Predictive Cruise Control for Commercial Vehicles Considering Different Time Domains

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