模型失配条件下混合动力两栖车功率协调预测控制

doi:10.12382/bgxb.2023.0919

摘要/Abstract

摘要：

为实现混合动力两栖车综合效率最优,提出一种功率协调预测控制策略。该策略旨在协同优化能量管理策略与车速控制策略之间的耦合关系。针对车速预测模型失配的问题,提出利用极限学习机进行实时误差预测,并通过预测值进行预测模型校正。设计模型预测控制器实现能量管理与车速控制的实时优化控制,并通过仿真进行验证。研究结果表明:提出的策略相较于传统的基于模型预测控制的能量管理策略能够降低等效燃油消耗、荷电状态(State of Charge, SOC)标准差、母线电压标准差和电池容量衰退,降低幅度分别为9.35%、59.63%、15.79%和45.33%;通过有无模型校正的功率协调预测控制对比,表明通过模型校正可实现等效燃油消耗、SOC标准差、母线电压标准差和电池容量衰退分别降低6.95%、25.91%、13.46%和24.07%,体现了所提出的基于极限学习机模型校正的功率协调预测控制在提升燃油经济性、维持电气系统稳定性和降低电池损耗方面的优越性。

关键词: 混合动力两栖车, 功率协调预测控制, 模型失配, 模型预测控制, 极限学习机

Abstract:

To achieve the optimal comprehensive efficiency of hybrid amphibious vehicle, a power coordinated predictive control strategy is proposed. This strategy deals with the coupling relationship between energy management strategy and longitudinal control strategy through collaborative optimization. Aiming at the mismatch of prediction model, an extreme learning machine (ELM) is used for real-time error prediction, and the prediction model is corrected through the predicted value. A model predictive controller (MPC) is designed for the real-time optimal control of energy management and longitudinal control, and it is verified by simulation. The results show that, compared with the traditional energy management strategy based on MPC, the proposed strategy can be used to reduce the equivalent fuel consumption, state-of-charge (SOC) standard deviation, bus voltage standard deviation and battery capacity fading by 9.35%, 59.63%, 15.79% and 45.33%, respectively. By comparing the power coordinated predictive control with and without model correction, it shows that the equivalent fuel consumption, SOC standard deviation, bus voltage standard deviation and battery capacity fading can be reduced by 6.95%, 25.91%, 13.46% and 24.07%, respectively, through model correction, which reflects the superiority of the power coordinated predictive control based on ELM model correction in improving the fuel economy, maintaining the stability of electrical system and reducing the battery consumption.

Key words: hybrid amphibious vehicle, power coordinated predictive control, model mismatch, model predictive control, extreme learning machine

王绪, 高晓宇, 黄英, 崔涛, 骆承良. 模型失配条件下混合动力两栖车功率协调预测控制[J]. 兵工学报, 2024, 45(12): 4578-4588.

WANG Xu, GAO Xiaoyu, HUANG Ying, CUI Tao, LUO Chengliang. Power Coordinated Predictive Control of Hybrid Amphibious Vehicle with Model Mismatch[J]. Acta Armamentarii, 2024, 45(12): 4578-4588.

图/表 17

图1 混合动力水陆两栖车构型

Fig.1 Hybrid amphibious vehicle configuration

表1 车辆和环境参数

Table 1 Parameters of vehicle and environment

参数	数值
整备质量/kg	25000
长×宽×高/m	8×3×2.1
主动轮半径/m	0.62
滚动阻力系数	0.1
旋转质量换算系数	1.2
空气阻力系数	1
方形系数	0.8
变速器1挡速比	28.8
湿表面积/m²	46
吃水深度/m	1.5
排水体积/m³	31.2
发动机额定功率/kW	656
发动机最高转速/(r·min^-1)	2500
电池额定电压/V	900
电池额定容量/(A·h)	100
变速器2挡速比	12.0

图2 发动机万有特性曲线

Fig.2 Engine universal characteristic curve

图3 基于ELM误差校正的MPC策略

Fig.3 MPCstrategy with ELM-based error correction

图4 ELM结构

Fig.4 Structure of ELM

图5 控制算法流程

Fig.5 Control algorithm flowchart

图6 循环工况

Fig.6 Driving cycle

图7 车速

Fig.7 Vehicle speed

图8 发动机功率

Fig.8 Engine power

图9 发动机工作点

Fig.9 Engine operating points

图10 电池电流

Fig.10 Battery current

图11 母线电压

Fig.11 Bus voltage

图12 SOC变化情况

Fig.12 Change of SOC

表2 指标对比

Table 2 Comparison of indexes

策略	等效燃油消耗/L	SOC 标准差	母线电压标准差	ΔSOH/ %	仿真时长/min
ELM-MPC-PCPC	10.475	0.306	18.863	0.041	35
MPC-PCPC	11.258	0.413	21.797	0.054	34
MPC-EMS	11.555	0.758	22.401	0.075	29

图13 测试车速

Fig.13 Test vehicle speed

表3 工况1指标对比

Table 3 Comparison of indexes under Condition 1

策略	等效燃油消耗/L	SOC 标准差	母线电压标准差	ΔSOH/ %	仿真时长/min
ELM-MPC-PCPC	11.250	0.388	22.383	0.043	36
MPC-PCPC	12.078	0.503	24.483	0.057	34
MPC-EMS	12.455	0.881	24.941	0.074	29

表4 工况2指标对比

Table 4 Comparison of indexes under Condition 2

策略	等效燃油消耗/L	SOC 标准差	母线电压标准差	ΔSOH/ %	仿真时长/min
ELM-MPC-PCPC	19.850	0.379	20.363	0.077	63
MPC-PCPC	21.379	0.533	21.797	0.087	59
MPC-EMS	21.640	0.777	22.401	0.095	48

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