基于人—车—环境识别的自适应档位决策方法研究
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摘要
汽车行驶在一个复杂的人-车-环境闭环系统中。传统换档规律仅以固定的模式换档，难以满足人们对当代车辆不断提高的性能需求。随着智能控制技术的发展，档位决策方法的研究已进入智能化、自适应阶段。自适应档位决策的首要问题是如何实现对人-车-环境的识别，然后是如何基于识别结果决策档位。本文围绕自适应档位决策领域的几个关键问题，开展了如下研究：
1）换档规律基础理论研究。构建了自动变速车辆动力传动系统仿真平台，对换档规律的基础理论、制定方法和适应性能进行分析，为智能化档位决策奠定理论基础。分析可知，两参数换档规律基于发动机和车辆的稳态模型设计，只能针对一种工况实现性能最优；而动态三参数动力性换档规律考虑了加速度参数，具有对环境的适应能力。
2）驾驶风格量化与定量分析。在分析驾驶风格与驾驶员动力需求之间本质联系的基础上，提出基于驾驶员动力需求的驾驶风格量化方法，开展了驾驶风格量化试验与分析工作。基于驾驶员动力需求的驾驶风格量化方法能够实现对驾驶风格的综合量化以及对驾驶风格主客观影响因素的单独量化，量化结果可用于识别驾驶风格。
3）驾驶风格识别及档位决策方法研究。提出长效和短效驾驶风格的概念，长效驾驶风格体现驾驶风格的总体和阶段性趋势，短效驾驶风格体现驾驶员瞬时驾驶意图。研究基于指数平滑法的长效驾驶风格预测方法，以及基于聚类分析的短效驾驶风格分类和模糊识别规则自动提取方法。用驾驶员动力需求因数综合反映驾驶员对整车动力的需求程度，进而提出基于驾驶员动力需求因数的档位决策方法，实现对驾驶员长期驾驶习惯和瞬时意图的自适应。
4）基于负荷度的行驶环境识别及档位决策方法研究。定义了负荷度的概念，其综合地反映了行驶环境中坡道、载重、天气和路面条件所产生的行驶阻力情况，也是行驶环境对整车动力需求程度的体现。研究基于负荷度的行驶环境识别方法以及基于正、负负荷度的档位决策方法，用于上坡、大载重等工况以及下坡工况。基于负荷度的档位决策方法和动态三参数动力型换档规律一样，具有对一般行驶环境的自适应能力，但其表达方式简洁、易于实现，可方便的用于实车控制。
5）特殊行驶环境识别及档位决策方法研究。利用轮速信号提取车辆侧向加速度及其变化率，结合当前车速信息，提出基于轮速的弯道工况缓急程度模糊识别方法，进而制定相应的弯道档位修正策略；通过对郊区、山区等颠簸路段上发动机转速变化率信号的频域和时域分析，研究基于发动机转速变化率的路面不平特征识别方法，相应的档位决策策略可抑制颠簸路段上的意外及频繁换档问题。
6）制动工况档位决策方法研究。在分析发动机辅助制动必要性的基础上，提出了综合考虑制动时间、制动减速度、行驶环境信息及车速情况的制动工况模糊推理档位决策方法。利用发动机辅助制动的制动工况模糊档位决策方法可增强制动效果、延长制动系统寿命、提高行驶安全性。
7）整车试验验证。基于整车试验平台，对人-车-环境识别方法以及相应的档位决策方法开展实车试验研究。试验结果可知，在不增加额外传感器的条件下，可实现对驾驶员和行驶环境的有效识别，进而用于智能档位决策与控制过程，具有工程实际应用价值。
Moving vehicle is a complex driver-vehicle-environment closed-loop system.However, the traditional shift law only works in fixed mode, which cannot satisfy thecustomers’ improving demands for vehicle performance. Actually, with the developmentof intelligent control technology, gearshift decision strategy has entered intelligent andadaptive stage. The first problem of adaptive gearshift decision method is how torecognize driver-vehicle-environment system, and the next problem is how to gearshiftdecision-making based on the recognition results. The paper focused on the several keyissues on the adaptive gearshift decision field and carried out the following researches:
1) Theoretical basis of shift law. To provide the theoretical foundation for adaptivegearshift decision strategy, the simulation platform of powertrain system of automaticvehicles was established. Then the basic theory, the formulating method as well as theadaptability of the shift law were analyzed. The analysis results showed that thetwo-parameter shift law is based on the steady-state model of the engine and vehicle,which can only achieve the optimal performance for a designed working condition;however, the dynamic three-parameter power shift law has the ability to adapt to theenvironment because the vehicle acceleration is considered.
2) Quantization method and quantitative analysis of driving style. By analyzing theessential relationship between driving style and driver power demand, the method ofquantifying driving style based on the driver power demand was proposed. Then thedriving style quantization experiment and the corresponding quantitative analysis workwere carried out. The quantization method is able to achieve the comprehensivequantification of the driving style and the separate quantification of the subjective and objective factors influencing driving style. The quantitative results can be used torecognize the driving style.
3) Driving style recognition and corresponding adaptive gearshift decision method.The concepts of long-term and short-term driving style were defined. The former reflectedthe overall and periodic trends of driving style, and the latter reflected the driver’sinstantaneous driving intention. The method of predicting long-term driving style based onthe exponential smoothing method, classifying short-term driving style based onclustering analysis and extracting fuzzy rules of short-term driving style recognitionautomatically based on the clustering results were proposed. At last, the recognition resultsof two driving styles were merged together into the driver power demand factor thatreflected the driver’s power requirement to vehicle. The gearshift decision method basedon the driver power demand factor was proposed to realize the adaption to the driver’slong-tern driving habit and instantaneous driving intent.
4) General driving environment recognition based on load degree and correspondingadaptive gearshift decision method. The concept of load degree was defined, whichsynthetically reflected the traveling resistance produced by slope, load, weather conditionand road condition. Then, the load degree based driving environment recognition methodwas proposed. In fact, the load degree essentially reflects the power demand of drivingenvironment. And then, the positive and negative load degree based gearshift decisionmethod were proposed, which were applied to conditions such as uphill, large load, anddownhill. Compared with dynamic three-parameter power shift law, gearshift decisionmethods based on load degree also has the adaptive ability to general driving environment.But its presentation was concise and is easier to implement, so it can be easily used in thereal vehicle control.
5) Special driving environment recognition and corresponding gearshift decisionmethod. By extracting the vehicle lateral acceleration and its change rate from the fourwheel speed signals, meanwhile considering the current vehicle velocity, the fuzzy methodto recognize the urgency degree of curve driving based on four-wheel speeds was proposed. And then, the gear correction strategy was formulated and the experimentalverification was carried out. Through the time-domain and frequency-domain analysis ofthe engine speed variation rate signal on suburb and mountain area, the rough road featuredetection method based on engine speed variation rate was proposed, which was appliedto prevent unexpected or busy shift problems on rough road.
6) Gearshift decision method on braking condition. Based on the analysis of thenecessity of engine assisted braking, shift decision method to improve braking effect anddriving safety based on fuzzy reasoning was proposed. The fuzzy reasoning rulesconsidered braking time, braking deceleration, load degree of driving environment andvehicle velocity were set up and road test was carried out. The results showed that thisgearshift decision method on braking condition can enhance the braking effect, extend thelife of the brake system, and improve the driving safety.
7) Vehicle test. On the basis of vehicle experimental platform, a series of vehicle testsfor checking and verifying the driver-vehicle-environment recognition methods andcorresponding adaptive gearshift decision strategy were carried out. The results showedthat the driver and the driving environment were identified effectively without anyadditional sensors, the intelligent gearshift decision strategy based on these recognitionresults were realized, and these methods have more practical application values.
1）换档规律基础理论研究。构建了自动变速车辆动力传动系统仿真平台，对换档规律的基础理论、制定方法和适应性能进行分析，为智能化档位决策奠定理论基础。分析可知，两参数换档规律基于发动机和车辆的稳态模型设计，只能针对一种工况实现性能最优；而动态三参数动力性换档规律考虑了加速度参数，具有对环境的适应能力。
2）驾驶风格量化与定量分析。在分析驾驶风格与驾驶员动力需求之间本质联系的基础上，提出基于驾驶员动力需求的驾驶风格量化方法，开展了驾驶风格量化试验与分析工作。基于驾驶员动力需求的驾驶风格量化方法能够实现对驾驶风格的综合量化以及对驾驶风格主客观影响因素的单独量化，量化结果可用于识别驾驶风格。
3）驾驶风格识别及档位决策方法研究。提出长效和短效驾驶风格的概念，长效驾驶风格体现驾驶风格的总体和阶段性趋势，短效驾驶风格体现驾驶员瞬时驾驶意图。研究基于指数平滑法的长效驾驶风格预测方法，以及基于聚类分析的短效驾驶风格分类和模糊识别规则自动提取方法。用驾驶员动力需求因数综合反映驾驶员对整车动力的需求程度，进而提出基于驾驶员动力需求因数的档位决策方法，实现对驾驶员长期驾驶习惯和瞬时意图的自适应。
4）基于负荷度的行驶环境识别及档位决策方法研究。定义了负荷度的概念，其综合地反映了行驶环境中坡道、载重、天气和路面条件所产生的行驶阻力情况，也是行驶环境对整车动力需求程度的体现。研究基于负荷度的行驶环境识别方法以及基于正、负负荷度的档位决策方法，用于上坡、大载重等工况以及下坡工况。基于负荷度的档位决策方法和动态三参数动力型换档规律一样，具有对一般行驶环境的自适应能力，但其表达方式简洁、易于实现，可方便的用于实车控制。
5）特殊行驶环境识别及档位决策方法研究。利用轮速信号提取车辆侧向加速度及其变化率，结合当前车速信息，提出基于轮速的弯道工况缓急程度模糊识别方法，进而制定相应的弯道档位修正策略；通过对郊区、山区等颠簸路段上发动机转速变化率信号的频域和时域分析，研究基于发动机转速变化率的路面不平特征识别方法，相应的档位决策策略可抑制颠簸路段上的意外及频繁换档问题。
6）制动工况档位决策方法研究。在分析发动机辅助制动必要性的基础上，提出了综合考虑制动时间、制动减速度、行驶环境信息及车速情况的制动工况模糊推理档位决策方法。利用发动机辅助制动的制动工况模糊档位决策方法可增强制动效果、延长制动系统寿命、提高行驶安全性。
7）整车试验验证。基于整车试验平台，对人-车-环境识别方法以及相应的档位决策方法开展实车试验研究。试验结果可知，在不增加额外传感器的条件下，可实现对驾驶员和行驶环境的有效识别，进而用于智能档位决策与控制过程，具有工程实际应用价值。
Moving vehicle is a complex driver-vehicle-environment closed-loop system.However, the traditional shift law only works in fixed mode, which cannot satisfy thecustomers’ improving demands for vehicle performance. Actually, with the developmentof intelligent control technology, gearshift decision strategy has entered intelligent andadaptive stage. The first problem of adaptive gearshift decision method is how torecognize driver-vehicle-environment system, and the next problem is how to gearshiftdecision-making based on the recognition results. The paper focused on the several keyissues on the adaptive gearshift decision field and carried out the following researches:
1) Theoretical basis of shift law. To provide the theoretical foundation for adaptivegearshift decision strategy, the simulation platform of powertrain system of automaticvehicles was established. Then the basic theory, the formulating method as well as theadaptability of the shift law were analyzed. The analysis results showed that thetwo-parameter shift law is based on the steady-state model of the engine and vehicle,which can only achieve the optimal performance for a designed working condition;however, the dynamic three-parameter power shift law has the ability to adapt to theenvironment because the vehicle acceleration is considered.
2) Quantization method and quantitative analysis of driving style. By analyzing theessential relationship between driving style and driver power demand, the method ofquantifying driving style based on the driver power demand was proposed. Then thedriving style quantization experiment and the corresponding quantitative analysis workwere carried out. The quantization method is able to achieve the comprehensivequantification of the driving style and the separate quantification of the subjective and objective factors influencing driving style. The quantitative results can be used torecognize the driving style.
3) Driving style recognition and corresponding adaptive gearshift decision method.The concepts of long-term and short-term driving style were defined. The former reflectedthe overall and periodic trends of driving style, and the latter reflected the driver’sinstantaneous driving intention. The method of predicting long-term driving style based onthe exponential smoothing method, classifying short-term driving style based onclustering analysis and extracting fuzzy rules of short-term driving style recognitionautomatically based on the clustering results were proposed. At last, the recognition resultsof two driving styles were merged together into the driver power demand factor thatreflected the driver’s power requirement to vehicle. The gearshift decision method basedon the driver power demand factor was proposed to realize the adaption to the driver’slong-tern driving habit and instantaneous driving intent.
4) General driving environment recognition based on load degree and correspondingadaptive gearshift decision method. The concept of load degree was defined, whichsynthetically reflected the traveling resistance produced by slope, load, weather conditionand road condition. Then, the load degree based driving environment recognition methodwas proposed. In fact, the load degree essentially reflects the power demand of drivingenvironment. And then, the positive and negative load degree based gearshift decisionmethod were proposed, which were applied to conditions such as uphill, large load, anddownhill. Compared with dynamic three-parameter power shift law, gearshift decisionmethods based on load degree also has the adaptive ability to general driving environment.But its presentation was concise and is easier to implement, so it can be easily used in thereal vehicle control.
5) Special driving environment recognition and corresponding gearshift decisionmethod. By extracting the vehicle lateral acceleration and its change rate from the fourwheel speed signals, meanwhile considering the current vehicle velocity, the fuzzy methodto recognize the urgency degree of curve driving based on four-wheel speeds was proposed. And then, the gear correction strategy was formulated and the experimentalverification was carried out. Through the time-domain and frequency-domain analysis ofthe engine speed variation rate signal on suburb and mountain area, the rough road featuredetection method based on engine speed variation rate was proposed, which was appliedto prevent unexpected or busy shift problems on rough road.
6) Gearshift decision method on braking condition. Based on the analysis of thenecessity of engine assisted braking, shift decision method to improve braking effect anddriving safety based on fuzzy reasoning was proposed. The fuzzy reasoning rulesconsidered braking time, braking deceleration, load degree of driving environment andvehicle velocity were set up and road test was carried out. The results showed that thisgearshift decision method on braking condition can enhance the braking effect, extend thelife of the brake system, and improve the driving safety.
7) Vehicle test. On the basis of vehicle experimental platform, a series of vehicle testsfor checking and verifying the driver-vehicle-environment recognition methods andcorresponding adaptive gearshift decision strategy were carried out. The results showedthat the driver and the driving environment were identified effectively without anyadditional sensors, the intelligent gearshift decision strategy based on these recognitionresults were realized, and these methods have more practical application values.
引文
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