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Frontiers of Information Technology & Electronic Engineering

ISSN 2095-9184 (print), ISSN 2095-9230 (online)

Design of an eco-gearshift control strategy under a logic system framework

Abstract: Good access to traffic information provides enormous potential for automotive powertrain control. We propose a logical control approach for the gearshift strategy, aimed at improving the fuel efficiency of vehicles. The driver power demand in a specific position usually exhibits stochastic features and can be statistically analyzed in accordance with historical driving data and instant traffic conditions; therefore, it offers opportunities for the design of a gearshift control scheme. Due to the discrete characteristics of a gearshift, the control design of the gearshift strategy can be formulated under a logic system framework. To this end, vehicle dynamics are discretized with several logic states, and then modeled as a logic system with the Markov process model. The fuel optimization problem is constructed as a receding-horizon optimal control problem under the logic system framework,and a dynamic programming algorithm with algebraic operations is applied to determine the optimal strategy online. Simulation results demonstrate that the proposed control design has better potential for fuel efficiency improvement than the conventional method.

Key words: Stochastic logic system, Gearshift strategy, Receding-horizon optimization, Traffic information, Eco-driving

Chinese Summary  <28> 基于逻辑系统框架的节能换档控制策略设计

康铭鑫1,高金武2
1东北大学流程工业综合自动化国家重点实验室,中国沈阳市,110819
2吉林大学通信工程学院,中国长春市,130012

摘要:交通信息的有效获取为汽车动力系统节能减排控制带来巨大潜力。提出一种旨在提升汽车燃油经济性能的基于逻辑控制的换挡策略。通过分析处理历史驾驶数据和实时交通信息,驾驶员在特定路段的功率需求显示出随机特征且可被统计分析;该随机特征为换挡策略设计提供了新思路。考虑到档位控制的离散特性,换挡策略可在逻辑系统框架下设计。鉴于此,汽车车速动态被离散量化为若干逻辑状态,进而可用马尔可夫过程模型建模为一个逻辑系统。在逻辑系统框架下,将汽车燃油优化问题建模为滚动时域档位优化控制问题,并采用基于代数运算的动态优化算法在线求解最优档位控制策略。仿真结果表明,相较于传统换挡策略,所提出的控制器能有效提升燃油经济性能。

关键词组:随机逻辑系统;换挡策略;滚动时域优化;交通信息;节能驾驶


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DOI:

10.1631/FITEE.1900459

CLC number:

TP13

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On-line Access:

2020-03-04

Received:

2019-08-31

Revision Accepted:

2019-11-01

Crosschecked:

2019-11-15

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