Search Results (4)

This paper analyzes the electrical test items of the EOL testing line in automotive manufacturers. On this basis, this paper proposes and designs an integrated and automated testing strategy to deal with the problems of slow testing speed, high dependence on manual labor and low efficiency. This article mainly analyzes the various tests of the two main tests in battery EOL testing: Battery Management System (BMS) testing and electrical testing. We propose an innovative integrated solution based on various testing items, including the reception, transmission, and self-analysis of different UDS protocol messages, a unique automated electrical performance measurement scheme, and a requirement and logic design of an integrated software end based on Python. The experimental results of actual testing show that the implementation of the integrated strategy greatly reduces the complexity of the testing steps, improves the testing efficiency, and reduces errors caused by human operation. Full article

Vehicular ad hoc networks (VANETs) allow communication between stationary or moving vehicles with the assistance of wireless technology. Among various existing issues in smart VANETs, secure communication is the key challenge in VANETs with a 5G network. Smart vehicles must communicate with a broad range of advanced road systems including traffic control and smart payment systems. Many security mechanisms are used in VANETs to ensure safe transmission; one such mechanism is cryptographic digital signatures based on public key infrastructure (PKI). In this mechanism, secret private keys are used for digital signatures to validate the identity of the message along with the sender. However, the validation of the digital signatures in fast-moving vehicles is extremely difficult. Based on an improved perceptron model of an artificial neural network (ANN), this paper proposes an efficient technique for digital signature verification. Still, manual signatures are extensively used for authentication across the world. However, manual signatures are still not employed for security in automotive and mobile networks. The process of converting manual signatures to pseudo-digital-signatures was simulated using the improved Elman backpropagation (I-EBP) model. A digital signature was employed during network connection to authenticate the legitimacy of the sender’s communications. Because it contained information about the vehicle on the road, there was scope for improvement in protecting the data from attackers. Compared to existing schemes, the proposed technique achieved significant gains in computational overhead, aggregate verification delay, and aggregate signature size. Full article

Gears are a vital component in many complex mechanical systems. In automotive systems, and in particular vehicle transmissions, we rely on them to function properly on different types of challenging environments and conditions. However, when a gear is manufactured with a defect, the gear’s integrity can become compromised and lead to catastrophic failure. The current inspection process used by an automotive gear manufacturer in Guelph, Ontario, requires human operators to visually inspect all gear produced. Yet, due to the quantity of gears manufactured, the diverse array of defects that can arise, the time requirements for inspection, and the reliance on the operator’s inspection ability, the system suffers from poor scalability, and defects can be missed during inspection. In this work, we propose a machine vision system for automating the inspection process for gears with damaged teeth defects. The implemented inspection system uses a faster R-CNN network to identify the defects, and combines domain knowledge to reduce the manual inspection of non-defective gears by 66%. Full article

An experimentally validated tribo-dynamic model has been developed to predict the gear teeth frictional losses considering the properties of the diamond-like-carbon (DLC)-coated and tungsten carbide carbon (WCC)-coated surface. The operating conditions used are snapshots of the Real Driving Emissions (RDE) driving cycle. The results demonstrate that the use of these coatings can improve the frictional losses up to 50%. The gear teeth boundary friction model is enriched by experimentally measured coefficients of the surface asperity boundary shear strength using an atomic force microscope (AFM). The computationally efficient model enables the efficiency prediction in a complete transmission. Such an approach, considering the contact mechanics of coated gear and their effect on the viscous and boundary friction, has not been hitherto reported. Full article