Control Strategy Dynamic Optimization of the Hybrid Electric Bus Based on Driving Cycle Self-learning

2010 ◽  
Vol 46 (06) ◽  
pp. 33 ◽  
Author(s):  
Daowei ZHU
Keyword(s):  
Dynamic Optimization ◽  
Control Strategy ◽  
Driving Cycle ◽  
Hybrid Electric Bus ◽  
Electric Bus ◽  
Hybrid Electric ◽  
Self Learning

Adaptive Control of Hybrid Electric Bus

2012 ◽  
Vol 246-247 ◽  
pp. 912-917
Author(s):  
Bin Yan ◽  
Yan Qing Hu ◽  
Shu Mei Zhang ◽  
Ting Yan ◽  
Lin Yang
Keyword(s):  
Adaptive Control ◽  
Control Strategy ◽  
Hybrid Electric Vehicle ◽  
Driving Cycle ◽  
Energy Management Strategy ◽  
Hybrid Electric Bus ◽  
Electric Bus ◽  
Good Energy ◽  
Hybrid Electric ◽  
Adaptive Control Strategy

Hybrid electric vehicle was powered by motor and engine. So making a good energy management strategy to torque split between motor and engine is very important. A static control strategy is not suitable for all drive cycles and all driving habits. This paper designs an adaptive control strategy for parallel HEV. In order to regulate the SOC balance of batter, the adaptive control strategy can update itself through analyzing the data obtained from previous driving cycle. Adaptive control strategy was tested in driving cycle of BC_CTC, the result prove that this method is effective.

scholarly journals Development of Driving Cycle Construction for Hybrid Electric Bus: A Case Study in Zhengzhou, China

2020 ◽  
Vol 12 (17) ◽  
pp. 7188
Author(s):  
Jiankun Peng ◽  
Jiwan Jiang ◽  
Fan Ding ◽  
Huachun Tan
Keyword(s):  
Transfer Matrix ◽  
Electricity Consumption ◽  
Original Data ◽  
Performance Measure ◽  
Driving Cycle ◽  
Statistical Characteristics ◽  
Hybrid Electric Bus ◽  
Navigation Function ◽  
Electric Bus ◽  
Hybrid Electric

A driving cycle is important to accomplish an accurate depiction of a vehicle’s driving characteristics as the traction motor’s flexible response to stop and start commands. In this paper, the driving cycle construction of an urban hybrid electric bus (HEB) in Zhengzhou, China is developed in which a measurement system integrating global positioning and inertial navigation function is used to acquire driving data. The collected data are then divided into acceleration, deceleration, uniform, and stop fragments. Meanwhile, the velocity fragments are classified into seven state clusters according to their average velocities. A transfer matrix applied to reveal the transfer relationship of velocity clusters can be obtained with statistical analysis. In the third stage, a three-part construction method of driving cycle is designed. Firstly, according to the theory of Markov chain, all the alternative parts that satisfy the construction’s precondition are selected based on the transfer matrix and Monte Carlo method. The Zhengzhou urban driving cycle (ZZUDC) could be determined by comparing the performance measure (PM) values subsequently. Eventually, the method and the cycle are validated by the high correlation coefficient (0.9972) with original data of ZZUDC than that of the other driving cycle (0.9746) constructed with traditional micro-trip and as well by comparing several statistical characteristics of ZZUDC and seven international cycles. Particularly, with around 20.5 L/100 km fuel and approximately 12.8 kwh/100 km electricity consumption, there is a narrow gap between the energy consumption of ZZUDC and WVUCITY, and their characteristics are similar.

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