Operation Classification of Heavy Vehicles

Abstract: Within Scania there is a need to classify vehicles depending on the prevailing operation conditions. Different vehicles are exposed to various loads and this will influence the need for R&M greatly. The systems that are presently used for classification purposes are subjective and the owner’s negotiation skills can affect the outcome. Simultaneously there is a lack of knowledge concerning the influence specific factors have on vehicle wear. The goal with the project was to develop a method which could be used for classification, based on information that is possible to measure. Information which can be extracted from the engine control unit, which all vehicles are equipped with, served as a starting point. This type of data is commonly called operation data. Since a new classification, in order to be meaningful, must be based on factors which influence the need for R&M activity it became an important part of the project to identify such factors. In order to investigate the connection between operation data and wear several different types of information were evaluated and used. In the initial analysis operation data was compared with warranty information but no link could be discovered. The conclusion was that warranty information is unsuitable for the evaluation of operation data. Warranty information mainly reflects problems which arise due to quality problems and not due to wear. Problems caused by wear can be expected to develop over time. Hence, it is important to have access to R&M statistics which cover a rather longer period of time. The only source of information available within Scania which contain comprehensive R&M information is RAMAS, used by the market organisation to evaluate R&M contracts. Unfortunately it is in most cases not possible to access operation data from contract vehicles. A collection of such data has been initiated but an evaluation of the material was not possible to fit within the timeframe of the project. However, an analysis of the available material showed that there most likely is a connection between wear on certain parts of the drive train and fuel consumption. The collection of operation information requires a coordinated strategy. Investigating why damage is initiated and how it propagates is important to identify crucial operation factors. A thorough analysis of operation data needs to be done and the connection between wear and fuel consumption must be verified. A classification system based on the influence of operation conditions will not be static but develop over time.