Energy is one of the major challenges of our century. I aim to address some aspects of this problem by developing innovative approaches, computational tools, and theory at the intersection of design, control and optimization. My main application area has been plug-in hybrid electric vehicles [J1, J4-J7, J10, C10, C12, C13], vehicle routing [J6, J10, C10], power systems [J2-J5, C16-C21] and battery management systems [J11, J12, C11, C12, J9]. Electric vehicles and battery systems are becoming increasingly popular and in demand with many new calls for proposals from various agencies as well as interest for collaboration from industry. Therefore, I expect to continue working on this line of research for years to come. I aim to advance the state-of-the-art in design, control, and optimization of electric vehicles, vehicle routing/navigation systems, and battery management systems.
Here, I provide a brief summary of my research areas and future directions mainly categorized as two application areas: (1) Electric Vehicles and (2) Battery Management Systems.
I develop novel algorithms for vehicle routing where I integrate powertrain dynamics into the routing decisions to minimize transportation cost and emissions. I am the co-inventor of routing strategy called “Vehicle Powertrain Connected Route Optimization (VPCRO)”.
Most navigation systems use data from satellites to provide drivers with the shortest-distance, shortest-time or highway-preferred paths. However, when the routing decisions are made for advanced vehicles, there are other factors affecting cost, such as vehicle powertrain type, battery state of charge and the change of component efficiencies under traffic conditions, which are not considered by traditional routing systems. The impact of the trade-off between distance and traffic on the cost of the trip might change with the type of vehicle technology and component dynamics. As a result, the least-cost paths might be different from the shortest-distance or shortest-time paths. About 81% and 58% of trips were replaced by different optimal paths with the proposed VPCRO strategy when the vehicle type was Conventional Vehicle and Electrified Vehicle, respectively.
In our previous work we have showed the influence of driving patterns on life cycle cost and emissions of vehicles. This work received more than 60 citations until now, and featured by major US media such as Bloomberg, Reuters, Fox42 News, Green Car Congress, Environmental Leader and etc. Publications in this and related area: [J1], [J6], [J6], [J10], [C10]
Electrified transportation and power systems are mutually coupled networks. In this area, I develop novel framework and methodology to optimize the operation of interdependent power and transportation networks. My approach constitutes solving an iterative multi-objective vehicle routing process, which utilizes the communication of electrified vehicles (EVs) with competing charging stations, to exchange data such as electricity price, energy demand, and time of arrival. EV routing problem is solved to minimize the total cost of travel using optimization algorithms with the input from EVs battery management system, electricity price from charging stations, powertrain component efficiencies and transportation network traffic conditions. Through the bidirectional communication of EVs with competing charging stations, EVs charging demand estimation is done much more accurately. Then the optimal power flow problem is solved for the power system, to find the locational marginal price at load buses where charging stations are connected. Finally, the electricity prices were communicated from the charging stations to the EVs, and the loop is closed. My novel approach that combines the electrified transportation with power system operation, holds tremendous potential for solving electrified transportation issues and reducing energy costs [J10]. My other work in this area is power demand estimation [J2], [J3], optimal allocation of EV parking lots [J4], and optimal autonomous EV charging algorithms [C12]. Other publications in this area: [C14] – [C21].
I develop a holistic framework and methodology for the combined design and control optimization of energy systems which are subject to variations in the load conditions for the entire lifetime economics of the system. I have applied this framework specifically to the co-design of plug-in electric vehicle powertrains. My goal is to achieve reductions in energy consumption, smaller engine, motor and battery sizes, reduced vehicle mass and lesser life cycle costs in electric vehicles.
My research focuses on determining the impact of intelligent energy management algorithms on downsizing potential of the expensive powertrain components such as battery, motor and engine [J1]. This is done by using dynamic programming (DP) nested within a static design optimization setting since DP gives an upper bound estimate for the performance of the predictive control algorithms. My contributions in co-design of plug-in vehicles has the potential to reduce life cycle cost and size of battery packs significantly. I demonstrated that we can downsize batteries up to 15%. This might increase mass adoption of EVs.
I develop novel algorithms to make batteries cheaper, smarter and long lasting. The laboratory I set up in my previous university is equipped with a highly accurate battery cell test station, module test station, and an environmental chamber (to test and control the batteries under the influence different environments such as temperature and humidity). I test our state of charge estimation and cell balancing algorithms using our battery-in-the-loop research setup. My virtual vehicle simulator allows us collect driving data and study the effect of driving behavior, terrain and etc. on the battery performance. I also develop battery management systems for cell balancing. Battery performance depends on both material and control. I develop accurate state of charge (SOC) estimation algorithms for aging batteries using in-house developed aging models, Unscented Kalman Filter approach, and online system identification for accurately estimating the changing battery model parameters. Using my SOC algorithms, efficiency and range of vehicles can be significantly increased. I also collaborated on this research with industry partners which externally funded my research program. Publications: [J11, J12, C11, C12, J9]
We study the power systems as a part of future intelligent transportation networks. In this context, we develop methods to solve large scale power systems optimization and control problems. On the other hand, we evaluate the impact of transportation networks on the stochastic operation of power systems. We build on highly-accurate demand forecasters to predict the electric vehicle charging demand. Then, we solve chance-constrained unit commitment problem for optimal power system operation.