信息二维码

手机看新闻

0

实现汽车故障诊断系统的挑战

   日期:2018-11-23 22:01     来源:洞云书屋    
核心提示:(注:本文是译文,Original author: Giorgio Rizzoni)Why diagnosis in the automotive field? The field of auto-motive engin
(注:本文是译文,Original author: Giorgio Rizzoni)

Why diagnosis in the automotive field? The field of auto-motive engineering has seen an explosion in the presence of electronic components and systems on-board vehicles since the 1970s. This growth was initially motivated by the introduction of emissions regulations that led to the widespread application of electronic engine controls. In addition, the presence of microcontrollers on-board the vehicle led to a proliferation of functions implemented through electronic systems and related soft-ware, related to safety and customer convenience, creating the need for more sophisticated on-board diagnostics.
为什么要在汽车领域进行诊断?自20世纪70年代以来,汽车动力工程领域出现了电子元件和车载系统的快速增长。这种增长最初是由引入排放法规导致的,这导致了电子发动机控制的广泛应用。车辆上的微控制器的存在导致通过电子系统和相关软件实现的功能的扩散,涉及到安全性和客户便利性,从而需要更复杂的车载诊断。

The original motivation for the introduction of real-time on-board diagnostics in automotive vehicles originates from the California Air Resources Board (CARB) requirements introduced in the early 1990’s to guarantee the integrity of the engine exhaust emissions control systems. The idea behind the original on-board diagnostics regula-tions [obd05], [EPA] was to guarantee that the exhaust emissions control system would be functional for a pe-riod of time associated with warranty or with regulated requirements. OBD regulations mandate that any fault in the emission control system affecting software algorithms, sensors, actuators or other hardware that could lead to an increase of tailpipe emissions such that the vehicle would no longer meet the emissions regulations, should be detected in real-time and codified according to a set of on-board diagnostic codes that are described in the OBD legislation. These regulations first came into effect in 1988 and were further expanded in 1994 through OBD-II regulations, and affect every single component or subsys-tem that could increase engine exhaust emissions above a pre-specified threshold. With the growth in complexity in exhaust emissions regulations, and the attendant increase in complexity in the hardware and software required to meet such regulations, the task of meting OBD regulations has become quite challenging.在汽车上引入实时车载诊断的最初动机源自于1990年早期引入的加利福尼亚空气资源委员会(CARB)要求,以保证发动机排气排放控制系统的完整性。最初的车载诊断规则是为了保证废气排放控制系统在保证或调节要求的时间内起作用。OBD法规规定,排放控制系统中的任何故障,如软件算法、传感器、执行器或其他硬件,可能导致尾气排放的增加,使得车辆将不再满足排放法规,应实时检测。根据OBD法规中描述的一组车载诊断代码进行修改。这些法规于1988年首次进入E.E.ECT,并于1994年通过OBD-II法规进一步扩展,并对每一个部件或子系统进行了改进,使发动机废气排放量高于预先规定的阈值。随着排放法规的复杂性的增长,以及满足这些法规所需的硬件和软件的复杂性的增加,制定OBD法规的任务变得非常具有挑战性。

A second motivation for the introduction of on-board diagnostic algorithms has been the introduction of safety systems on-board vehicles. In recent years, increasing attention to safety has led to the introduction of anti-lock braking systems, traction control systems, electronic stability control systems, and passive and active restraints. Many safety functions are also the subject of increasingly stringent regulations. The introduction of active systems that can affect the safety of a vehicle, such as braking, traction and stability control, and the introduction of by-wire systems to implement these functions, has generated deferent needs in diagnostics. In this context, diagnosis is a precursor to fault-tolerant control: if a safety-critical component is malfunctioning because of a fault or failure in a sensor, actuator or other component, or a malfunction in one of the software algorithms, then it is necessary to identify such safety-critical failures very quickly so as to be able to take corrective actions and ensure the safety and reliability of the vehicle.
引入车载诊断算法的第二个动机是引入车载系统的安全系统。近年来,人们越来越重视安全性,引入了防抱死制动系统、牵引控制系统、电子稳定控制系统以及被动和主动约束。许多安全功能也是越来越严格的法规的主题。介绍了一种能够控制车辆的安全性的主动系统,例如制动、牵引和稳定控制,以及引入有线系统来实现这些功能,这就产生了诊断的双重需求。在这种情况下,诊断是容错控制的先驱:如果一个安全关键部件由于传感器、致动器或其他组件中的故障或软件算法中的一个错误而发生故障,那么有必要识别这样的安全关键故障,以便能够采取纠正措施,并确保车辆的安全性和可靠性。

The third area that has seen a growth in diagnostcs is related to customer satisfaction. There may be some significant advantages in having diagnostic algorithms on-board the vehicle for the purpose of guaranteeing customer satisfaction and overall quality.
第三个领域的增长与客户满意度有关。在车辆上具有诊断算法的一些显著优点是为了保证客户满意度和整体质量。

PROBLEMS AND CHALLENGES
问题与挑战

In the face of the deferent requirements outlined in the preceding section, there is growing interest on the part of the automotive industry in the ability to systematically design diagnostic algorithms. Further, automakers have also shown a desire to extend warranty periods to provide consumers with a worry-free experience. As a consequence, in addition to on-board diagnosis of deferent functions, the prognosis of various functions and subsystems in the vehicle has also become important. Manufacturers would like to be able to predict when maintenance or replacement may be needed for specific components, for example the 12V battery, or components in subsystems related to the emissions control system. So, prognosis is beginning to take on a role in automotive electronic systems that was not on the horizon even just five or ten years ago.
面对前面部分所概述的要求,汽车工业的部分系统设计诊断算法的能力越来越受关注。此外,汽车制造商也表现出延长保修期的愿望,为消费者提供无忧体验。因此,除了车载诊断功能之外,各种功能和子系统在车辆中的预测也变得更重要。制造商希望能够预测何时需要维护或更换特定的组件,例如12V电池,或者与排放控制系统相关的子系统中的组件。因此,即使在五年或十年前,汽车电子系统还没有出现,预测已开始在汽车电子系统中起作用。

The implementation of diagnostic and prognostic algo-rithms of this type in automotive systems presents a num-ber of challenges due to the scale of the implementation. Such algorithms must be adaptable to millions of vehicles and must be robust enough to be valid over a broad range of different realizations of the same vehicle platform, with choice of different engines, transmissions, and accessories. Further, vehicles that might be architecturally identical, will unavoidably require different software calibrations in different markets. Thus, the design and implementation of OBD algorithms is not a ‘one size fits all’ kind of design approach.
这种类型的诊断和预测算法在汽车系统中的实施由于规模巨大而带来了大量的挑战。这样的算法必须适应数百万辆车,并且必须足够强大,以在同一车辆平台的广泛的实现范围内有效,并选择不同的发动机、变速器和附件。此外,可能在建造相同的车辆不可避免地需要在二级市场上进行二次软件校准。因此,OBD算法的设计与实现不是一种“一刀切”的设计方法。

The second issue is related to the fact that automo-tive systems tend to be complex and highly nonlinear. For example, engine and exhaust emissions processes are characterized by complex thermochemical behavior (com-bustion processes, exhaust emissions formation), that is strongly affected by chemical reaction kinetics, fluid mo-tion and heat transfer. Further, the presence of sensors and actuators, such as fuel injectors, or systems that could be pneumatically or hydraulically actuated increases the overall complexity of an engine emission control system. Therefore, it is difficult to imagine that simple, linear algo-rithms could be very effective unless a substantial amount of thinking and a deep understanding of the physics of the processes goes into their design.
第二个问题与自动系统往往是复杂的和高度非线性的事实有关。例如,发动机和废气排放过程的特点是复杂的热化学行为(燃烧过程,废气排放形成),它受到化学反应动力学、流体模拟和传热所影响。此外,传感器和致动器的存在,例如燃料喷射器或可气动或液压驱动的系统增加了发动机排放控制系统的整体复杂性。因此,进入这种设计不能想象它是简单、线性的数学表达式,要对过程物理有深刻的理解。

Another important aspect is the speed of execution, in the face of limited computational capabilities (both CPU speed and memory). On-board computers used in automotive applications have relatively low power relative to the number of functions that they perform, because cost is a significant constraint in the automotive industry. So, one of the main challenges is to develop effective diagnosis algorithms that can be implemented in fixed-point arithmetic microcontrollers with limited amount of memory and limited CPU speed. Some algorithms may require truly real-time implementation. For instance, in safety-critical diagnosis algorithms (e.g.: vehicle stability control, or brake-by wire or steer-by wire applications), one is obviously concerned with the implementation of these algorithms in real-time so that any fault that is detected can be compensated for in a fault-tolerant control scheme or by entering a limp-home mode as safely and as quickly as possible. On the other hand, other types of algorithms, such as those that may be used to diagnose malfunctions in the emission control systems, may not have such stringent real-time requirements, in the sense that on-board diagnostics regulations typically require that the diagnosis be carried out within what is called a ‘one trip’
另一个重要方面是有限的计算能力(CPU速度和内存)和执行速度。在自动化应用中,车载计算机相对于它们所执行的功能具有相对较低的能力,因为成本是汽车工业中的一个重要约束。因此,一个主要的挑战是开发有效的诊断算法,可以在有限的内存量和有限的CPU速度的定点算术微控制器中实现。一些算法可能需要真正的实时实现。例如,在安全关键的诊断算法(例如:车辆稳定性控制,或线控制动或线控转向应用)中,显然要关注这些算法的实时实现,从而可以在容错中补偿所检测到的任何故障。另一方面,其他类型的算法,例如可用于诊断排放控制系统中的故障的算法,可能不具有如此严格的实时要求,在这种意义上,车载诊断规范通常要求在一个行程内进行诊断。

Finally, diagnosis must be as transparent as possible to the user, while the designer must be very cognizant of the relative weight of false alarms vs. missed detections: such weighting will vary depending on the application, with missed detections being especially costly (to the user) in safety-critical applications, while false alarms can be very costly (to the manufacturer and consumer alike) when non-safety-critical applications that have warranty implications are considered.
最后,诊断必须尽可能对用户透明,而设计者必须非常了解错误警报相对于错过检测的相对权重:这样的权重将根据应用而变化,而在安全关键应用中错过检测尤其昂贵(对用户)。在考虑有保修意义的非安全关键应用时,错误警报对于制造商和消费者来说都是非常昂贵的。

In short, the subject of system diagnosis in the most complex consumer device in existence today - the automobile is one that presents numerous technical challenges that range from the theory of estimation and detection, to real-time software implementation issues.
简而言之,汽车诊断是当今世界上最复杂的消费设备系统。这提出了许多技术挑战,范围从评估和检测理论,到实时软件实现问题。 
 
 
更多>本文相关推荐
0相关评论

推荐阅读
广告
网站首页  |  关于我们  |  联系方式  |  使用协议  |  版权隐私  |  网站地图  |  排名推广  |  广告服务  |  积分换礼  |  网站留言  |  RSS订阅  |  违规举报  |  沪ICP备11026917号-26