Your Destination for Mobility Engineering Resources
Announcements for SAE Mobilus
Browse AllRecent SAE Edge™ Research Reports
Browse All 150
Recent Books
Browse All 967Recently Published
Browse AllTransportation contributes 27% of the greenhouse gas emissions in the US. Governments worldwide are developing new programs to hasten the adoption of electric vehicles (EVs) in the transition to zero-emission vehicles. However, the success of EV adoption generally depends on user preferences. This study explores what we can find out about consumer preferences while accounting for unobserved heterogeneity. Consumer choices for EVs, including plug-in EVs (PEVs) and fuel-cell EVs (FCEVs), are analyzed using the California Vehicle Survey (2019) data. Several factors are examined, including the availability of clean source energy (installed solar panels) at home, preferable location for recharging PEVs, past driving experience with EVs, availability of public charging infrastructure, and sociodemographic factors. A mixed multinomial (random parameter) logit model is estimated, exploring the associations between the selected variables and EV consumer preferences while accounting for
Electric Vehicles and Battery-Fuel_Cell hybrid vehicles are increasingly becoming popular in the market, especially in the commercial vehicle segment. Range estimation and control is of paramount importance as it is the main cause of anxiety among the vehicle owners. This paper discusses application of Reinforcement Learning (RL) to achieve range control. In RL, the learning agent choses actions dependent on the state of the environment and gets a reward in return. Ultimately the agent will learn the policy of choosing the actions for each state such that his long-term reward is maximized. The technique of RL has been applied for various scenarios where in a look up table (between the states of a system and actions to be taken) needs to be developed for optimal performance. In this paper, we use RL to manipulate other energy sources and sinks like Fuel Cell and HVAC (in addition to the battery which is the main energy source) for range control, and thereby achieve the optimal
Effective thermal management is crucial for vehicles, impacting both passenger comfort and safety, as well as overall energy efficiency. Electric vehicles (EVs) are particularly sensitive to thermal considerations, as customers often experience range anxiety. Improving efficiency not only benefits customers by extending vehicle range and reducing operational costs but also provides manufacturers with a competitive edge and potential revenue growth. Additionally, efficient thermal management contributes to minimizing the environmental impact of the vehicle throughout its lifespan. Digital twins have gained prominence across various industries due to their ability to accelerate development while minimizing testing costs. Some applications have transitioned to comprehensive three-dimensional models, while others employ model reduction techniques or hybrid approaches that combine different modeling methods. The discovery of unknown working mechanisms, more efficient and effective control
In automotive air conditioning systems, compressor is used to convert low pressure low temperature refrigerant into high pressure high temperature refrigerant. Various types of compressors like swash plate, rotary vane, scroll etc. are widely used in the automotive industry for air conditioning applications. In rotary vane compressors, thermal protector is used as a safety device, designed to prevent the compressor from overheating during refrigerant compression process. When the discharge temperature exceeds the preset limit of thermal protector, the thermal protector will activate and stop the electrical supply to compressor clutch to stop the compressor operation thereby preventing potential damage to air conditioning system, engine, and other nearby parts of the vehicle. This technical paper explores the various real-world scenarios for a hot country like India, which may result into higher discharge temperatures of compressor resulting into activation of thermal protector. The
Tracking of energy consumption has become more difficult as demand and value for energy have increased. In such a case, energy consumption should be monitored regularly, and the power consumption want to be reduced to ensure that the needy receive power promptly. Our objective is to identify the energy consumption of an electric vehicle from battery and track the daily usage of it. We have to send the data to both the user and provider. We have to optimize the power usage by using anomaly detection technique by implementing deep learning algorithms. Here we are going to employ a LSTM auto-encoder algorithm to detect anomalies in this case. Estimating the power requirements of diverse locations and detecting harmful actions are critical in a smart grid. The work of identifying aberrant power consumption data is vital and it is hard to assure the smart meter’s efficiency. The LSTM auto-encoder neural network technique is used here for predicting power consumption and to detect anomalies
Energy efficiency in both internal combustion engine (ICE) and electric vehicles (EV) is a strategic advantage of automotive companies. It provides a better user experience that emanates amongst others from the reduction in operation expenses, particularly critical for fleets, and the increase in range. This is especially important in EVs where customers may experience range anxiety. The energetical impact of using the air conditioning system in vehicles is not negligible with power consumptions in the range of kilowatts, even with a stopped vehicle. This becomes particularly important in areas with high temperature and humidity levels where the usage of the air conditioning systems becomes safety factor. In such areas, drivers are effectively forced to use the air conditioning system continuously. Hence, the air conditioning system becomes an ideal choice to deploy control strategies for optimized energy usage. In this paper, we propose and implement a control strategy that allows a