CAJ | 학술논문

为了使车辆驾驶性能满足驾驶员需求,提出了基于数据融合决策的驾驶员类型识别方法并建立了基于驾驶员类型的换挡规律.首先基于驾驶员的驾驶行为和驾驶意图,对驾驶员类型进行分析,制定了基于驾驶风格的驾驶员类型识别方案.选定能表征驾驶员驾驶风格的有效工况及相应的表征信号后,先采用BP神经网络分类器对驾驶风格进行辨识,再采用贝叶斯融合决策方法先后对同类操纵的驾驶风格辨识结果和所有操纵类型驾驶风格辨识结果进行数据融合决策,最终辨识出驾驶员类型.根据驾驶员类型,引入动力性系数,通过不同类型驾驶员对应的动力性系数值的改变,实现换挡规律中动力性因素和经济性因素所占比例的调整,最终形成基于驾驶员类型的 DCT 换挡规律.最后,以搭载 6DCT的某试验车为对象,对不同驾驶员的换挡过程进行仿真实验,结果表明基于驾驶员类型的DCT换挡规律能够适应不同类型的驾驶员需求.该研究为驾驶员类型识别和智能型换挡规律的制定提供了参考.
위료사차량가사성능만족가사원수구,제출료기우수거융합결책적가사원류형식별방법병건립료기우가사원류형적환당규률.수선기우가사원적가사행위화가사의도,대가사원류형진행분석,제정료기우가사풍격적가사원류형식별방안.선정능표정가사원가사풍격적유효공황급상응적표정신호후,선채용BP신경망락분류기대가사풍격진행변식,재채용패협사융합결책방법선후대동류조종적가사풍격변식결과화소유조종류형가사풍격변식결과진행수거융합결책,최종변식출가사원류형.근거가사원류형,인입동력성계수,통과불동류형가사원대응적동력성계수치적개변,실현환당규률중동력성인소화경제성인소소점비례적조정,최종형성기우가사원류형적 DCT 환당규률.최후,이탑재 6DCT적모시험차위대상,대불동가사원적환당과정진행방진실험,결과표명기우가사원류형적DCT환당규률능구괄응불동류형적가사원수구.해연구위가사원류형식별화지능형환당규률적제정제공료삼고.
Shift schedule is one of the major factors for drivability. When using traditional method to establish shift schedule, it considers power performance and fuel economy, but neglects driver characteristics. Speed and throttle in traditional two-parameter shift schedule may reflect vehicle performance for driver to some extent, but driving characteristics of different drivers can't be considered. In this paper, a shift schedule method based on driver type was proposed for making vehicle maneuverability meet drivers' characteristics. In order to obtain the drive type, driving behavior and intention were analyzed according to drivers' operations in driving process, different driver characteristics were obtained, and then drivers could be classified into conservative and sport type. So identification scheme of driver type was proposed. Driver's operations, road condition and vehicle state were transformed into electrical signals by vehicle sensors. These electrical signals could be identified by electronic control unit and used to classify driving style, and then driver type could be obtained by fusion decision of driving style. Firstly, BP (back propagation) neural network classifier was employed for driving style identification from the obtained signals. The classifier designed had 3 layers, and any 2 layers were linked by nonlinear S-functions. The data of effective driving cycles and corresponding characteristic signals, which could remarkably characterize the driving style, were placed in the input layer, the different driving styles were obtained from the output layer, and the node number of the middle layer was optimized by the empirical formula. Moreover, the classifier was trained off-line on the basis of the driving data under various working conditions. Secondly, the driving styles were fused by Bayesian to obtain driver type. Because there were many different effective driving cycles while driving, the fusion decision process was performed in 2 stages. The fusion decision of driving style date of the same effective driving cycle was accomplished at the first stage, and the driver type was obtained at a later stage which was fusion decision of different effective driving cycles. Finally, the conception of power performance coefficient was proposed in this paper. It could be calculated with practical throttle opening, small opening threshold and large opening threshold. The values of small and large opening threshold were determined by driver type. By a change of power performance coefficient corresponding to driver type, the proportion of power performance and fuel economy in shift schedule could be adjusted. After the strategy of the power performance coefficient was determined, a comprehensive shift schedule based on driver type, power performance and fuel economy was presented. The more conservative the driver type was identified, the more attention the fuel economy was paid in the comprehensive shift schedule based on driver type. Conversely, the more sport driver type corresponded to the more power factor. In order to validate the reliability of comprehensive shift schedule based on driver type, vehicle shifting processes corresponding to different driver types were simulated by the test vehicle equipped with six-speed dual clutch transmission. This simulation was performed under the condition of starting with 50% throttle opening, and vehicle speed and gear position were observed. The simulation results showed that the more sport driver type corresponded to a higher speed of shift points under the condition of same throttle opening, which led to later upshift and higher vehicle speed at the same time, and then sport driver could obtain better power performance. So the shift schedule based on driver type proposed in the paper is feasible and efficient, and can meet the requirements of different drivers. The research provides a reference for driver style identification and intelligent shift schedule establishment.