Fuzzy Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicle Based on Power Ratio

doi:10.7652/xjtuxb202201002

您当前的位置：

首页 >

文章列表页 >

Fuzzy Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicle Based on Power Ratio

更新时间：2025-05-08

- Fuzzy Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicle Based on Power Ratio
- Vol. 56, Issue 1, Pages: 12-21(2022)
- 作者机构：
  
  1. 江苏大学汽车工程研究院,江苏,镇江,212013
  2. 重庆理工大学汽车零部件先进制造技术教育部重点实验室,重庆,400054
- 作者简介：
- 基金信息：
- DOI：10.7652/xjtuxb202201002
  CLC： U463.2
- Online First：10 January 2022，
  
  Published：2022
- 稿件说明：
移动端阅览
Fuzzy Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicle Based on Power Ratio[J]. 2022, 56(1): 12-21.
DOI：

Fuzzy Adaptive Equivalent Consumption Minimization Strategy for Hybrid Electric Vehicle Based on Power Ratio[J]. 2022, 56(1): 12-21. DOI： 10.7652/xjtuxb202201002.

摘要

针对基于优选固定等效因子的等效燃油消耗最小策略(ECMS)工况适应性差的问题

提出一种基于功率比的自适应ECMS(PR-AECMS)。以功率分流式混合动力汽车为对象

建立采用电池荷电状态(SOC)修正ECMS等效因子的显示自适应求解模型

针对仅基于SOC反馈修正等效因子的不足

引入相邻时段平均功率作为ECMS等效因子前馈调节变量

通过研究相邻时段平均功率、前一时段电池充放电行为

以及不同电池SOC实时值和参考值等因素对等效因子修正机制的影响

提出基于多模糊控制器切换的ECMS等效因子自适应求解方法

根据电池SOC和前一时段车辆平均功率制定各模糊控制器的切换逻辑

以当前时段与前一时段的平均功率比、前一时段电池SOC变化量为各模糊控制器输入

基于标准循环工况的全局优化结果确定模糊控制参数。基于不同标准循环工况的仿真结果表明

相较于无功率比修正的AECMS

本文提出的PR-AECMS使整车在大范围工况下具有更优越的等效燃油经济性和电池充放电平衡特性

有效提高了ECMS策略的工况适应性。

Abstract

Aiming at the poor cycle adaptability of the equivalent consumption minimization strategy(ECMS)with calibrated constant equivalence factor

this paper proposes a power ratio based adaptive ECMS(PR-AECMS)for hybrid electric vehicles(HEV). For the power split HEVs

an explicit adaptive model that corrects the equivalence factor of the ECMS with the battery state of charge(SOC)is established. Aiming at the deficiency of the adaptive method in correcting the equivalent factor only based on the feed-back SOC

the average power of the adjacent time periods is introduced as the feed-forward correction variable of the equivalent factor. The impacts of different factors

such as the average power of the adjacent time periods

the battery charging and discharging behavior in the previous period

and different real-time and reference values of battery SOC

on the correction mechanism of the equivalence factor are studied. Then

the adaptive solving method of the equivalence factor based on multi-fuzzy controller switching is proposed. The switching logic of different fuzzy controllers is determined according to the previous average power and the current battery SOC. The SOC variation value of the previous time period and the ratio of current average power to previous one are set as the inputs of the different fuzzy controllers. Based on the global optimization results under standard cycles

the fuzzy control parameters are designed. Simulation results under different standard test cycles demonstrates that

compared with the AECMS without power ratio correction

the proposed PR-AECMS possesses better equivalent fuel economy and battery charging balance in a wide range of driving cycles. The cycle adaptability of the ECMS is effectively improved.

关键词

Keywords

references

刘永刚, 李杰, 秦大同, 等. 基于多工况优化算法的混合电动汽车参数优化 [J]. 机械工程学报, 2017, 53(16): 61-69.

LIU Yonggang, LI Jie, QIN Datong, et al. Parameter optimization of hybrid electric vehicle based on multi-cycle optimization algorithm [J]. Journal of Mechanical Engineering, 2017, 53(16): 61-69.

SHI Dehua, PISU P, CHEN Long, et al. Control design and fuel economy investigation of power split HEV with energy regeneration of suspension [J]. Applied Energy, 2016, 182: 576-589.

黄康, 王强, 邱明明, 等. 考虑模式切换频率的多模式混合动力汽车参数优化 [J]. 西安交通大学学报, 2019, 53(7): 99-107.

HUANG Kang, WANG Qiang, QIU Mingming, et al. Parameter optimization of multi-mode hybrid vehicle considering mode-switching frequency [J]. Journal of Xi'an Jiaotong University, 2019, 53(7): 99-107.

牛礼民, 杨洪源, 周亚洲, 等. 混合动力汽车动力总成多智能体集成控制策略 [J]. 机械工程学报, 2019, 55(12): 168-177, 188.

NIU Limin, YANG Hongyuan, ZHOU Yazhou, et al. Hybrid electric vehicle integrated control strategy based on multi-agent [J]. Journal of Mechanical Engineering, 2019, 55(12): 168-177, 188.

ANSELMA P G, HUO Yi, ROELEVELD J, et al. Integration of on-line control in optimal design of multimode power-split hybrid electric vehicle powertrains [J]. IEEE Transactions on Vehicular Technology, 2019, 68(4): 3436-3445.

徐兴, 徐新铮, 王峰, 等. 新型双电机行星耦合PHEV多目标补偿能量优化策略研究 [J]. 西安交通大学学报, 2019, 53(3): 125-134.

XU Xing, XU Xinzheng, WANG Feng, et al. Optimal energy management for dual motor planetary coupled PHEV based on multi-objective compensation factor [J]. Journal of Xi'an Jiaotong University, 2019, 53(3): 125-134.

ENANG W, BANNISTER C. Robust proportional ECMS control of a parallel hybrid electric vehicle [J]. Proceedings of the Institution of Mechanical Engineers: Part D Journal of Automobile Engineering, 2017, 231(1): 99-119.

ONORI S, SERRAO L, RIZZONI G. Adaptive equivalent consumption minimization strategy for hybrid electric vehicles [C]∥ASME 2010 Dynamic Systems and Control Conference. New York, USA: ASME, 2010: 499-505.

PISU P, RIZZONI G. A comparative study of supervisory control strategies for hybrid electric vehicles [J]. IEEE Transactions on Control Systems Technology, 2007, 15(3): 506-518.

SUN Chao, HU Xiaosong, MOURA S J, et al. Velocity predictors for predictive energy management in hybrid electric vehicles [J]. IEEE Transactions on Control Systems Technology, 2015, 23(3): 1197-1204.

KAZEMI H, FALLAH Y P, NIX A, et al. Predictive AECMS by utilization of intelligent transportation systems for hybrid electric vehicle powertrain control [J]. IEEE Transactions on Intelligent Vehicles, 2017, 2(2): 75-84.

ZHANG Fengqi, XI Junqiang, LANGARI R. Real-time energy management strategy based on velocity forecasts using V2V and V2I communications [J]. IEEE Transactions on Intelligent Transportation Systems, 2017, 18(2): 416-430.

ZHANG Yuanjian, CHU Liang, FU Zicheng, et al. Optimal energy management strategy for parallel plug-in hybrid electric vehicle based on driving behavior analysis and real time traffic information prediction [J]. Mechatronics, 2017, 46: 177-192.

XUE Hongtao, LI Zhongxing, LI Yong, et al. A fuzzy diagnosis of multi-fault state based on information fusion from multiple sensors [J]. Journal of Vibroengineering, 2016, 18(4): 2135-2148.

张雷, 刘青松, 王震坡. 四轮轮毂电机驱动电动汽车电液复合制动平顺性控制策略 [J]. 机械工程学报, 2020, 56(24): 125-134.

ZHANG Lei, LIU Qingsong, WANG Zhenpo. Electro-hydraulic brake control for improved ride comfort in four-wheel-independently-actuated electric vehicles [J]. Journal of Mechanical Engineering, 2020, 56(24): 125-134.

ZHAO Dezong, STOBART R, DONG Guangyu, et al. Real-time energy management for diesel heavy duty hybrid electric vehicles [J]. IEEE Transactions on Control Systems Technology, 2015, 23(3): 829-841.

付主木, 刘晓真, 周祥. 基于模糊控制的ISG-FHEV等效燃油消耗最小策略 [J]. 火力与指挥控制, 2017, 42(10): 103-108.

FU Zhumu, LIU Xiaozhen, ZHOU Xiang. Equivalent consumption minimization strategy based on fuzzy control for full hybrid electric vehicle assisted by an integrated starter generator [J]. Fire Control Command Control, 2017, 42(10): 103-108.

LI Liang, YOU Sixiong, YANG Chao, et al. Driving-behavior-aware stochastic model predictive control for plug-in hybrid electric buses [J]. Applied Energy, 2016, 162: 868-879.

施德华, 蔡英凤, 汪少华, 等. 系统效率最优的功率分流式混合动力汽车非线性预测控制 [J]. 浙江大学学报(工学版), 2019, 53(12): 2271-2279.

SHI Dehua, CAI Yingfeng, WANG Shaohua, et al. Nonlinear predictive control of power split hybrid electric vehicle with optimal system efficiency [J]. Journal of Zhejiang University(Engineering Science), 2019, 53(12): 2271-2279.

SHI Shuming, ZHANG Man, LIN Nan, et al. Low-cost reconstruction of typical driving cycles based on empirical information and low-frequency speed data [J]. IEEE Transactions on Vehicular Technology, 2020, 69(8): 8221-8231.

SHI Dehua, WANG Shaohua, CAI Yingfeng, et al. Model predictive control for nonlinear energy management of a power split hybrid electric vehicle [J]. Intelligent Automation and Soft Computing, 2020, 26(1): 27-39.

-------

Views

下载量

CSCD

Alert me when the article has been cited

提交

Tools

Publicity Resources

Reinforcement Learning-Based Energy Management Strategy Considering Driving Style for Hybrid Electric Vehicle

Finite-Time Prescribed Performance Coordinated Control for Power-Split Hybrid Electric Vehicle Systems

Predictive Optimization of Energy Management Strategy for New Type Power-Split Hybrid Driveline System

Optimal Energy Management for Dual Motor Planetary Coupled PHEV Based on Multi-Objective Compensation Factor

Adaptive Energy Management Strategy for Hybrid Tractors Based on Condition Prediction

Related Author

CHEN Long

ZHOU Weiqi

WANG Shaohua

YUAN Chao

SHI Dehua

AN Xingke

RONG Xiangwei

WANG Shaohua

Related Institution

Jiangsu Province Engineering Research Center of Electric Drive System and Intelligent Control for Alternative Vehicles

Electrical Information Engineering, Jiangsu University

Institute of Automotive Engineering, Jiangsu University

Institute of Engineering and Technology Research, Jiangsu University

Clean Energy Automotive Engineering Center, Tongji University

⁰