基于能耗预测的网联电动公交车生态驾驶优化策略

doi:10.19562/j.chinasae.qcgc.2025.05.005

Abstract

Abstract:

The power system and energy consumption characteristics of electric buses significantly differ from those of traditional buses with internal combustion engines， and conventional eco-driving strategies cannot fully adapt to electric buses. An energy consumption prediction-based deep reinforcement learning model is proposed for eco-driving of connected electric buses， taking into account of signal timing， information from preceding vehicles， energy consumption characteristics and comfort of passengers. Firstly， natural driving data from battery electric buses is collected， and a basic energy consumption model is established using vehicle dynamics， considering the regenerative braking characteristics of electric buses. A system identification model is then constructed to identify and estimate the unknown parameters in the basic energy consumption model. Next， the impact of different signal phases on speed patterns when entering and exiting signalized intersections is analyzed， and state variables that accurately describe traffic environment information are determined. Based on the constructed energy consumption model， a reward function is developed， considering safety， efficiency， energy conservation， and comfort. An optimization model for eco-driving strategies at signalized intersections for electric buses is established using the SAC（soft actor critic） algorithm. Finally， the proposed strategy is compared with the classic intersection passage strategy GLOSA. The results show that the proposed eco-driving strategy ensures vehicle safety across the four defined traffic scenarios. Despite an average increase in travel time of only 7.29%， the strategy enhances comfort by an average of 21.96% and reduces energy consumption by an average of 24.47%.

Key words: eco-driving strategy, connected electric bus, deep reinforcement learning, passenger comfort, energy consumption prediction, signalized intersection

Yingjiu Pan,Yi Xi,Yansen Liu,Wenpeng Fang,Wenshan Zhang. An Energy Consumption Prediction-Based Optimization Strategy for Eco-driving of Connected Electric Buses[J].Automotive Engineering, 2025, 47(5): 839-850.

Figures/Tables 15

参数	数据描述	实例
记录日期	记录日期	2022-09-07 07：39：25：472
经度	公交车所处位置的经度	113.366 3
纬度	公交车所处位置的纬度	22.920 88
车速/（ $k m ? h - 1$ ）	公交车实时车速	20.428 57
电压/ $V$	电池电压	596
电流/ $A$	电池电流	18.5
CAN里程/ $k m$	公交车累计里程	254 779.3

参数	含义	取值
$E m a x$ /（W·h）	最大电池容量	48 300
$m / k g$	车辆质量	12 400
$v m a x / (k m ? h - 1)$	最高车速	40
$a m a x / (m ? s - 2)$	最大加速度	2.5
$d m a x / (m ? s - 2)$	最大减速度	2.5
$l / m$	交叉口信号范围	300
$t o p e n$	通行信号相位持续时间比	0.466 7
$t c y c l e$	交通信号灯周期时长	90
$D / s$	仿真步长	1

主要参数	数值
学习率	0.000 3
折现系数	0.99
重放缓冲区容量	100 000
样本批次大小	256
Actor网络层数	4
Critic网络层数	4
隐藏层神经元数量	256
$f 1$ ， $f 2$ ， $f 3$ ， $f v$ ， $β 1$ ， $β 2$ ， $β 3$ ， $α 1$ ， $α 2$ ， $α 3$ ， $α 4$	-1，1，-1，0.8，-100，-100， -100，1，0.5，3，1

交通情境	信号控制状态	剩余时间/s	初始车速/（ $k m ? h - 1$ ）
交通情境1	绿灯	15	34.54
交通情境2	绿灯	35	30.87
交通情境3	红灯	23	33.06
交通情境4	红灯	33	30.32

参数	控制策略	交通情境
参数	控制策略	情境1	情境2	情境3	情境4
前车最小距离/ $m$	本文方法	5.33	23.63	7.31	8.73
前车最小距离/ $m$	GLOSA	2.50	27.49	2.50	2.50
加速度绝对值平均值/ $(m · s - 2)$	本文方法	0.24	0.08	0.31	0.29
加速度绝对值平均值/ $(m · s - 2)$	GLOSA	0.31	0.07	0.54	0.46
能源消耗率/ （ $k W · h · k m - 1$ ）	本文方法	0.95	0.50	0.81	0.89
能源消耗率/ （ $k W · h · k m - 1$ ）	GLOSA	1.35	0.61	1.24	1.05
通行时间/ $s$	本文方法	81	42	48	55
通行时间/ $s$	GLOSA	77	37	45	53
节能率/%	本文方法对比GLOSA	29.63	18.33	34.68	15.24

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An Energy Consumption Prediction-Based Optimization Strategy for Eco-driving of Connected Electric Buses

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