Combination of simulation and model-checking for the analysis of autonomous vehicles’ behaviors: A case study

International audienceAutonomous vehicles’ behavioural analysis represents a major challenge in the automotive world. In order to ensure safety and fluidity of driving, various methods are available, in particular, simulation and formal verification. The analysis, however, has to cope with very complex environments depending on many parameters evolving in real time. In this context, none of the aforementioned approaches is fully satisfactory, which lead us to propose a combined methodology in order to point out suspicious behaviours more efficiently. We illustrate this approach by studying a non deterministic scenario involving a vehicle, which has to react to some perilous situation

battery storage using response surface methodology

This paper aims to show the use of the response surface methodology (RSM) in size optimization of an autonomous PV/wind integrated hybrid energy system with battery storage. RSM is a collection of statistical and mathematical methods which relies on optimization of response surface with design parameters. In this study, the response surface, output performance measure, is the hybrid system cost, and the design parameters are the PV size, wind turbine rotor swept area and the battery capacity. The case study is realized in ARENA 10.0, a commercial simulation software, for satisfaction of electricity consumption of the global system for mobile communications (GSM) base station at Izmir Institute of Technology Campus Area, Urla, Turkey. As a result, the optimum PV area, wind turbine rotor swept area, and battery capacity are obtained to be 3.95 m(2), 29.4 m(2), 31.92 kWh, respectively. These results led to $37,033.9 hybrid energy system cost, including auxiliary energy cost. The optimum result obtained by RSM is confirmed using loss of load probability (LLP) and autonomy analysis. (C) 2008 Elsevier Ltd. All rights reserved

EXPERIMENTAL DESIGN AND REGRESSION ANALYSIS FOR PERFORMANCE OF A CHILLER

In this paper, we perform experimental design and regression analysis for performance of a chiller system. First, we complete design of experiment (DOE) on response so that determine the main and the interaction effects of the pre-defined factors. Second, we omit the insignificant factors from the analysis and, develop regression functions by considering the significant factors (inputs). Four factors are considered in DOE, these are: the water temperature, water flow rate, electronic expansion valve (EEV) opening percentage and compressor speed. Eight responses (outputs) are considered in DOE. They are the coefficient of performance (COP), capacity of evaporator, capacity of condenser, power consumption of the compressor, temperature of condensing, temperature of evaporation, superheating, and sub-cooling. The DOE results are analyzed, and regression functions are developed by MINITAB - a statistical software - at a 95% confidence level

EXPERIMENTAL DESIGN AND REGRESSION ANALYSIS FOR PERFORMANCE OF A CHILLER

In this paper, we perform experimental design and regression analysis for performance of a chiller system. First, we complete design of experiment (DOE) on response so that determine the main and the interaction effects of the pre-defined factors. Second, we omit the insignificant factors from the analysis and, develop regression functions by considering the significant factors (inputs). Four factors are considered in DOE, these are: the water temperature, water flow rate, electronic expansion valve (EEV) opening percentage and compressor speed. Eight responses (outputs) are considered in DOE. They are the coefficient of performance (COP), capacity of evaporator, capacity of condenser, power consumption of the compressor, temperature of condensing, temperature of evaporation, superheating, and sub-cooling. The DOE results are analyzed, and regression functions are developed by MINITAB - a statistical software - at a 95% confidence level

battery storage using simulated annealing

In this paper, we perform Simulated Annealing (SA) algorithm for optimizing size of a PV/wind integrated hybrid energy system with battery storage. The proposed methodology is a heuristic approach which uses a stochastic gradient search for the global optimization. In the study, the objective function is the minimization of the hybrid energy system total cost. And the decision variables are PV size, wind turbine rotor swept area and the battery capacity. The optimum result obtained by SA algorithm is compared with our former study's result. Consequently, it is come up with that the SA algorithm gives better result than the Response Surface Methodology (RSM). The case study is realized for a campus area in Turkey. (C) 2009 Elsevier Ltd. All rights reserved

study

In this study, performance evaluation of an autonomous vehicle storage and retrieval system (AVS/RS) is completed under various pre-defined design scenarios. Design scenarios are considered based on the rack configuration of the warehouse, namely the number of aisles, bays, and tiers, number of autonomous vehicles (AVs), and number of lifts. The system is simulated using ARENA 12-a commercial software. Seven performance measures are observed from the simulation results. These are: the average cycle time, average utilization of the AVs, average utilization of the lifts, average waiting time in the AVs' queue, average waiting time in the lifts' queue, average number of transactions waiting in the AVs' queue, and average number of transactions waiting in the lifts' queue. Total cost of the system is also integrated into the analysis. The results are analyzed via graphs. Various design options and their costs facilitate the evaluation of the best design for the warehouse manager. The case study is completed for a company that utilizes AVS/RS in France

conversion system with battery storage - A case study

This paper aims to show an optimum sizing procedure of autonomous PV/wind hybrid energy system with battery storage and a break-even analysis of this system and extension of transmission line. We use net present value (NPV) method for the comparison of autonomous hybrid energy system and extension of transmission line cases. The case study is completed for the satisfaction of the electricity consumption of global system for mobile communication base station (GSM) at Izmir Institute of Technology Campus Area, Urla, Izmir, Turkey. First, we optimize the PV/wind energy system using response surface methodology (RSM) which is a collection of statistical and mathematical methods relying on optimization of response surface with design parameters. As a result of RSM, the optimum PV area, wind turbine rotor swept area, and battery capacity are obtained as 3.95 m(2), 29.4 m(2), 31.92 kW h, respectively. These results led to $37,033.9 hybrid energy system cost. Second, break-even analysis is done to be able to decide the optimum distance where the hybrid energy system is more economical than the extension of the transmission line. The result shows that, if the distance between national electricity network and the GSM base station location where the hybrid energy system is assumed to be installed is at a distance more than 4817 m, the installation of hybrid energy system is more economical than the electricity network. (C) 2008 Elsevier Ltd. All rights reserved

autonomous vehicle storage and retrieval system

In this paper, a simulation-based regression analysis for the rack configuration of an autonomous vehicle storage and retrieval system (AVS/RS) is presented. The aim of this study is to develop mathematical functions for the rack configuration of an AVS/RS that reflects the relationship between the outputs (responses) and the input variables (factors) of the system under various scenarios. In the regression model, we consider five outputs: the average cycle time of storage and retrieval transactions, the average waiting time for vehicle transactions, the average waiting time of vehicles (transactions) for the lift, the average utilisation of vehicles and the average utilisation of the lifts. The input variables are the number of tiers, aisles and bays that determine the size of the warehouse. Thirty regression models are developed for six warehouse scenarios. The simulation model of the system is developed using ARENA 12.0 commercial software and the statistical analyses are completed using MINITAB statistical software. Two different approaches are used to fit the regression functions-stepwise regression and the best subsets. After obtaining the regression functions, we optimise them using the LINGO software. We apply the approach to a company that uses AVS/RS in France

Performance comparison of two material handling systems: AVS/RS and

The purpose of this study is to compare the performance of two material handling systems (MHSs) - autonomous vehicle storage and retrieval systems (AVS/RSs) and traditional, crane-based, automated storage and retrieval systems (CBAS/RSs) - with respect to key performance measures. First, the two MHSs are simulated using ARENA 12.0, commercial software, via 198 experiments based on the rack configurations of the warehouses, number of storage and retrieval (S/R) devices and S/R transaction rates. Five performance measures are considered - average flow time, S/R device utilisation, average waiting time in S/R device queue, average number of jobs waiting in S/R device queue and cost. We also complete a paired-t test comparison to find out the best warehouse design based on cost, flow time and utilisation of the S/R devices. An examination of the results, specifically the flow time of the S/R devices, suggests that in many cases the AVS/RS performs better than the CBAS/RS. However, the best design, when the system costs are considered, is obtained with a CBAS/RS