Secured Vehicle Life Cycle Tracking Using Blockchain and Smart Contract

Life Cycle Tracking(LCT)involves continuous monitoring and analy-sis of various activities associated with a vehicle.The crucial factor in the LCT is to ensure the validity of gathered data as numerous supply chain phases are involved and the data is assessed by multiple stakeholders.Frauds and swindling activities can be prevented if the history of the vehicles is made available to the interested parties.Blockchain provides a way of enforcing trustworthiness to the supply chain participants and the data associated with the various actions per-formed.Machine learning techniques when combined decentralized nature of blockchains can be used to develop a robust Vehicle LCT model.In the proposed work,Harmonic Optimized Gradient Descent andŁukasiewicz Fuzzy(HOGD-LF)Vehicle Life Cycle Tracking in Cloud Environment is proposed and it involves three stages.First,the Progressive Harmonic Optimized User Registra-tion and Authentication model is designed for computationally efficient registra-tion and authentication.Next,for the authentic user,the Gradient Descent Blockchain-based SVM Data Encryption model is designed with minimum CPU utilization.Finally,Łukasiewicz Fuzzy Smart Contract Verification is per-formed with encrypted data to ensure accurate and precise fraudulent activity deduction.The experimental analysis shows that the proposed method achieves significant performance in terms of life cycle’s prediction time,overhead,and accuracy for a different number of users.

Connectivity-Enhanced Route Selection and Adaptive Control for the Chevrolet Volt

The National Renewable Energy Laboratory and General Motors evaluated connectivity-enabled efficiency enhancements for the Chevrolet Volt. A high-level model was developed to predict vehicle fuel and electricity consumption based on driving characteristics and vehicle state inputs. These techniques were leveraged to optimize energy efficiency via green routing and intelligent control mode scheduling, which were evaluated using prospective driving routes between tens of thousands of real-world origin/destination pairs. The overall energy savings potential of green routing and intelligent mode scheduling was estimated at 5% and 3%, respectively. These represent substantial opportunities considering that they only require software adjustments to implement.

Real drive cycles analysis by ordered power methodology applied to fuel consumption,C0_(2),NO_(x) and PM emissions estimation

In this work three fuel consumption and exhaust emission models,ADVISOR,VT-MICRO and the European Environmental Agency Emission factors,have been used to obtain fuel consumption(FC)and exhaust emissions.These models have been used at micro-scale,using the two signal treatment methods presented.The manuscript presents:1)a methodology to collect data in real urban driving cycles,2)an estimation of FC and tailpipe emissions using some available models in literature,and 3)a novel analysis of the results based on delivered wheel power.The results include Fuel Consumption(FC),CO_(2),NO_(x) and PM_(10) emissions,which are derived from the three simulators.In the first part of the paper we present a new procedure for incomplete drive cycle data treatment,which is necessary for real drive cycle acquisition in high density cities.Then the models are used to obtain second by second FC and exhaust emissions.Finally,a new methodology named Cycle Analysis by Ordered Power(CAbOP)is presented and used to compare the results.This method consists in the re-ordering of time dependant data,considering the wheel mechanical power domain instead of the standard time domain.This new strategy allows the 5 situations in drive cycles to be clearly visualized:hard breaking zone,slowdowns,idle or stop zone,sustained speed zone and acceleration zone.The complete methodology is applied in two real drive cycles surveyed in Barcelona(Spain)and the results are compared with a standardized WLTC urban cycle.