A5091162398- Advanced Battery Technologies Research
- Electric Vehicles and Infrastructure
- Transportation and Mobility Innovations
- Advancements in Battery Materials
- Hybrid Renewable Energy Systems
- Vehicle emissions and performance
- Integrated Energy Systems Optimization
- Fault Detection and Control Systems
- Electric and Hybrid Vehicle Technologies
- Advanced Battery Materials and Technologies
- Transportation Planning and Optimization
- Energy, Environment, and Transportation Policies
- Global Energy and Sustainability Research
- Power Systems and Renewable Energy
- Advancements in Solid Oxide Fuel Cells
- Stability and Control of Uncertain Systems
- Microgrid Control and Optimization
- Traffic control and management
- Energy Load and Power Forecasting
- Control Systems and Identification
- Vehicle Routing Optimization Methods
- Machine Learning in Materials Science
- Smart Grid Energy Management
- Advanced Control Systems Optimization
- Catalysts for Methane Reforming
Idaho National Laboratory
2017-2024
University of Notre Dame
2014-2018
Tongji University
2011
A stochastic energy aware routing framework is proposed for electric vehicles with the goal of enhancing sustainability future electrified transportation systems. decision making introduced to handle random effects environmental factors on cost. The problem optimal formulated as a programming and risk control overall applied find minimum route. original discrete optimization tackled using convex relaxation transformation. path constructed highly efficient primal-dual interior point algorithm...
(Special section 'Design, modelling and control of electric vehicles') This study provides a detailed deterministic stochastic sensitivity analysis the propulsion energy cost vehicles (EVs) with respect to environmental variables. In particular, effects wind speed, rolling resistance, parasitic power temperature are highlighted. The exact analytical expressions as well simulations illustrate key results. It is shown that consumption four variables greatly vary operating conditions vehicle....
This paper introduces an adaptive multiresolution framework for electric vehicle (EV) energy consumption estimation with real-time capability. Three key parameters, namely powertrain efficiency, wind speed, and rolling resistance, are adaptively estimated using a two-step nonlinear iterative algorithm. Based on this algorithm, multichannel high-resolution efficiency is introduced. Employing the "connected vehicles" concept, more reliable trip level estimates achieved by sharing sensed...
Aiming to improve the energy management in large areas for sustainable transportation, this paper models demand of electric vehicles (EVs) an urban area as a function location and time. One important consideration supply charging infrastructure is characterization local charge Spatiotemporal formulations are derived number scenarios, ranging from individual global demands describe their three-dimension dynamical features. Furthermore, perceived modeled by considering real situations, e.g.,...
There are tremendous economic and technical benefits to shortening battery test periods through robust predictive methods. Accurate long-term forecasting of life enables proactive planning management (e.g., cell replacements) preemptive actions modify operating conditions improve safety life. The ever-evolving landscape materials applications ensure an abiding need for early capture aging mechanisms. Herein we report on accelerated determination mechanisms together with prediction future...
This paper proposes two different event-triggered nonlinear model predictive controls (NMPC) for autonomous vehicle path tracking. The difference between the NMPCs is determination of control action when an event not triggered. In first formulation, optimal sequence computed from last triggering shifted to determine NMPC triggered, while in second a time-triggered linear parametric varying MPC (LPV-MPC) with shorter prediction horizon formulated and solved events compensate error...
Abstract A key step limiting how fast batteries can be deployed is the time necessary to provide evaluation and validation of performance. Using data analysis approaches, such as machine learning, process accelerated. However, questions on validity projecting models trained limited or simple cycling profiles, constant current cycling, real‐world scenarios with complex loads remains. Here, we present ability predict performance less than 1.2 % mean absolute percent error when cells aged using...
This paper provides a sensitivity analysis of the required EV propulsion power with respect to environmental factors such as wind speed, rolling resistance and temperature. The results obtained show degree which affect overall battery energy usage for electric transportation. analytical expressions well simulations illustrate key results. significance our findings vehicle range estimation is discussed potential avenues exploit strong dependency between are proposed.
This paper investigates the benefits of exploiting weather conditions for energy optimal driving. Optimization approaches are introduced that based on fact weather-dependent speed profiles can save transportation energy, especially electric systems. models both minimum with travel time constraints and resulting analyzed. Infinite dimensional optimization proposed exact problem descriptions approximate discretized convex derived highly efficient solutions. The tasks formulated as a...
Residential electric vehicle charging load profile is indispensable to achieve reliable control strategies for mitigating negative effects on power distribution system due emerging electrified transportation. This paper introduces a data-driven framework of generation residential plug-in vehicles. Real world historical behavior data utilized construct empirical decision making model by using machine learning algorithm. A multiple channels method with kernel density estimation proposed...
This paper focuses on finding an optimal energy-aware speed trajectory of Autonomous Electric Vehicle (AEV) considering regenerative braking capability and its limitations. A position-based (EV) energy consumption model is used to emulate vehicle-road operating conditions. It assumed that the EV driven in urban area where route only constrained by maximum limits traffic signs. The eco-driving problem formulated as a Mixed Integer Linear Programming (MILP) solved for two different case...
This paper provides new insights into the feasibility of energy optimal transportation by exploiting weather conditions using road surface and wind data. The approach proposes to alter speed profile along a route, depending on available It also incorporates routing. task is formulated as deterministic well robust optimization problem, where either are assumed be known exactly or within certain uncertainty bounds. Driving strategies suggested simulations illustrate achievable efficiency improvements.
This paper models the energy demand of electric vehicles in a city as function space (location) and time. Energy formulations are derived for number scenarios ranging from individual to global large city. One important considerations decision making sizing placement charging stations is characterization local time, which prime focus this work. Additionally, typical stay times also considered process, simply because it provides data on required charge rates, i.e. power. The chosen approach...
This paper demonstrates the use of transactive energy (TE) to maximize profitability and flexibility a nuclear power plant. Reductions in price natural gas, concurrent influx variable distributed resources electricity market U.S. have impacted economic viability traditional generation, which is currently untenable for adapting dynamic pricing trends. can be resolved by using TE concepts control coordinate an integrated system. system recuperate excess thermal at times when prices are low...