Predictive Energy Management in Connected Vehicles: Utilizing Route Information Preview for Energy Saving

This dissertation formulates algorithms that use preview information of road terrain and traffic flow for reducing energy use and emissions of modern vehicles with conventional or hybrid powertrains. Energy crisis, long term energy deficit, and more restrictive environmental protection policies require developing more efficient and cleaner vehicle powertrain systems. An alternative to making advanced technology engines or electrifying the vehicle powertrain is utilizing ambient terrain and traffic information in the energy management of vehicles, a topic which has not been emphasized in the past. Today\u27s advances in vehicular telematics and advances in GIS (Geographic Information System), GPS (Global Positioning Systems), ITS (Intelligent Transportation Systems), V2V (Vehicle to Vehicle) communication, and VII (Vehicle Infrastructure Integration ) create more opportunities for predicting a vehicle\u27s trip information with details such as the future road grade, the distance to the destination, speed constraints imposed by the traffic flow, which all can be utilized for better vehicle energy management. Optimal or near optimal decision-making based on this available information requires optimal control methods, whose fundamental theories were well studied in the past but are not directly applicable due to the complexity of real problems and uncertainty in the available preview information. This dissertation proposes the use of optimal control theories and tools including Pontryagin minimum principle, Dynamic Programming (DP) which is a numerical realization of Bellman\u27s principle of optimality, and Model Predictive Control (MPC) in the optimization-based control of hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and conventional vehicles based on preview of future route information. The dissertation includes three parts introduced as follows: First, the energy saving benefit in HEV energy management by previewing future terrain information and applying optimal control methods is explored. The potential gain in fuel economy is evaluated, if road grade information is integrated in energy management of hybrid vehicles. Real-world road geometry information is taken into account in power management decisions by using both Dynamic Programming (DP) and a standard Equivalent Consumption Minimization Strategy (ECMS), derived using Pontryagin minimum principle. Secondly, the contribution of different levels of preview to energy management of plug-in hybrid vehicles (PHEVs) is studied. The gains to fuel economy of plug-in hybrid vehicles with availability of velocity and terrain preview and knowledge of distance to the next charging station are investigated. Access to future driving information is classified into full, partial, or no future information and energy management strategies for real-time implementation with partial future preview are proposed. ECMS as well as Dynamic Programming (DP) is systematically utilized to handle the resulting optimal control problems with different levels of preview. We also study the benefit of future traffic flow information preview in improving the fuel economy of conventional vehicles by predictive control methods. According to the time-scale of the preview information and its importance to the driver, the energy optimization problem is decomposed into different levels. In the microscopic level, a model predictive controller as well as a car following model is employed for predictive adaptive cruise control by stochastically forecasting the driving behavior of the lead car. In the macroscopic level, we propose to incorporate the estimated macroscopic future traffic flow information and optimize the cost-to-go by utilizing a two-dimension Dynamic Programming (2D-DP). The algorithm yields the optimal trip velocity as the reference velocity for the driver or a low level controller to follow. Through the study, we show that energy use and emissions can be reduced considerably by using preview route information. The methodologies discussed in this dissertation provide an alternative mean for the automotive industry to develop more efficient and environmentally friendly vehicles by relying mostly on software and information and with minimal hardware investments

Optimal electric vehicle scheduling : A co-optimized system and customer perspective

Electric vehicles provide a two pronged solution to the problems faced by the electricity and transportation sectors. They provide a green, highly efficient alternative to the internal combustion engine vehicles, thus reducing our dependence on fossil fuels. Secondly, they bear the potential of supporting the grid as energy storage devices while incentivizing the customers through their participation in energy markets. Despite these advantages, widespread adoption of electric vehicles faces socio-technical and economic bottleneck. This dissertation seeks to provide solutions that balance system and customer objectives under present technological capabilities. The research uses electric vehicles as controllable loads and resources. The idea is to provide the customers with required tools to make an informed decision while considering the system conditions. First, a genetic algorithm based optimal charging strategy to reduce the impact of aggregated electric vehicle load has been presented. A Monte Carlo based solution strategy studies change in the solution under different objective functions. This day-ahead scheduling is then extended to real-time coordination using a moving-horizon approach. Further, battery degradation costs have been explored with vehicle-to-grid implementations, thus accounting for customer net-revenue and vehicle utility for grid support. A Pareto front, thus obtained, provides the nexus between customer and system desired operating points. Finally, we propose a transactive business model for a smart airport parking facility. This model identifies various revenue streams and satisfaction indices that benefit the parking lot owner and the customer, thus adding value to the electric vehicle --Abstract, page iv

Plug-in electric vehicle deployment and integration with the electric grid

Key battery, semiconductor, and software technologies have sufficiently progressed over the past few decades to enable viable plug-in electric vehicle (PEV) alternatives to conventional vehicles. Alternatives to petroleum-based fuels for transportation are sought to address concerns over energy security, foreign oil derived U.S. trade deficits, oil related geopolitical entanglements, and emissions. The various types of PEVs have substantially different characteristics. The types and key attributes of PEVs, charging standards, and charging locations are described. The likely scenario for PEV-Grid interactions over the next decade is synthesized from the analysis of the technologies available to and circumstances of vehicle manufacturers, utilities, and supplier firms. PEV adoption considerations are evolving. Many lessons have been learned from the first generation of PEVs that were introduced starting in late 2010. Technology, market, and policy drivers of emerging trends in the diffusion of PEVs are explored more in-depth. PEVs as electric loads are unique in that they are large, flexible, and intelligent. These attributes can not only provide utilities a new source of revenue, but also improve grid stability and economics. Actions, technologies, and policies that utilities can deploy to increase adoption are discussed. Actions are explored to make the overall PEV ownership experience superior to a conventional vehicle. This dissertation also describes research of the capability for PEVs in Vehicle to Home (V2H) scenarios, where the vehicle acts as a residential battery storage system and/or a backup generator in a residential microgrid configuration during a grid outage. Residential energy data collected from a smart grid testbed is used with a custom PEV model to assess the performance (in terms of duration and power output) of a BEV or PHEV used for backup power. Our earlier results quantify the extent to which photovoltaic (PV) generation and the characteristics of a PEV (battery size, gasoline availability) affect the backup duration during an electric grid outage. Strategies to further increase backup duration and non-continuous self-sustaining off-grid alternatives were found in our early V2H research. Varied amounts of load curtailment and PHEV engine-generator control improvements are modeled in subsequent research.Electrical and Computer Engineerin

Efficient operation of recharging infrastructure for the accommodation of electric vehicles: a demand driven approach

Large deployment and adoption of electric vehicles in the forthcoming years can have significant environmental impact, like mitigation of climate change and reduction of traffic-induced air pollutants. At the same time, it can strain power network operations, demanding effective load management strategies to deal with induced charging demand. One of the biggest challenges is the complexity that electric vehicle (EV) recharging adds to the power system and the inability of the existing grid to cope with the extra burden. Charging coordination should provide individual EV drivers with their requested energy amount and at the same time, it should optimise the allocation of charging events in order to avoid disruptions at the electricity distribution level. This problem could be solved with the introduction of an intermediate agent, known as the aggregator or the charging service provider (CSP). Considering out-of-home charging infrastructure, an additional role for the CSP would be to maximise revenue for parking operators. This thesis contributes to the wider literature of electro-mobility and its effects on power networks with the introduction of a choice-based revenue management method. This approach explicitly treats charging demand since it allows the integration of a decentralised control method with a discrete choice model that captures the preferences of EV drivers. The sensitivities to the joint charging/parking attributes that characterise the demand side have been estimated with EV-PLACE, an online administered stated preference survey. The choice-modelling framework assesses simultaneously out-of-home charging behaviour with scheduling and parking decisions. Also, survey participants are presented with objective probabilities for fluctuations in future prices so that their response to dynamic pricing is investigated. Empirical estimates provide insights into the value that individuals place to the various attributes of the services that are offered by the CSP. The optimisation of operations for recharging infrastructure is evaluated with SOCSim, a micro-simulation framework that is based on activity patterns of London residents. Sensitivity analyses are performed to examine the structural properties of the model and its benefits compared to an uncontrolled scenario are highlighted. The application proposed in this research is practice-ready and recommendations are given to CSPs for its full-scale implementation.Open Acces

SEEV4City INTERIM 'Summary of the State of the Art' report

This report summarizes the state-of-the-art on plug-in and full battery electric vehicles (EVs), smart charging and vehicle to grid (V2G) charging. This is in relation to the technology development, the role of EVs in CO2 reduction, their impact on the energy system as a whole, plus potential business models, services and policies to further promote the use of EV smart charging and V2G, relevant to the SEEV4-City project

Combined Design and Control Optimization: Application to Optimal PHEV Design and Control for Multiple Objectives.

This dissertation develops algorithms for optimal design and control solutions of dynamic systems in a computationally efficient manner. These methods are demonstrated by applying them to a Plug-in Hybrid Electric Vehicle (PHEV) powertrain’s optimal design and control. Since a PHEV draws energy from the grid it is important to consider these interactions in its optimal design and control decisions. The battery size also affects the amount of grid energy transferred to propulsion and consequently the on-road power management decisions. Thus, we develop algorithms to determine the optimal PHEV battery size and control decisions considering conditions on the electric grid. First, we develop a Dynamic Programming (DP) based algorithm for optimal on-road power management of a series PHEV. A backward looking implementation of the PHEV powertrain’s dynamic model with the DP algorithm avoided the need to interpolate the value function or enforce constraints through penalty functions, thereby alleviating computational concerns. This algorithm is extended to consider optimal charging on the electric grid by utilizing conditions at the boundaries of the optimal charging and driving problems. The results exposed tradeoffs between the two problems. This algorithm was further applied to determine the optimal CO2 reduction benefits in propulsion depending on wind penetration on the grid. Since PHEVs are expected to address emissions, cost, and participate in grid services, we develop a multi-objective dynamic programming (MODP) algorithm. This algorithm utilizes the idea of crowding distance from Non-Dominated Sort Genetic Algorithms (NSGA) literature to represent the Pareto front with fewer points, easing the computational time and memory requirements. Tradeoffs in achieving minimum CO2 vs. minimum operational costs are discussed. Finally, we utilize the theory on combined optimization of a system’s design and control for PHEV battery sizing considering its optimal charging and power management. Salient features of the algorithm such as calculation and use of a sensitivity term and the reduction of computational effort are demonstrated by solving a beam mass reduction and vibration attenuation problem. Then, this algorithm is applied for optimal battery sizing of a PHEV. The sensitivity values provided insights for optimal PHEV battery sizing while considering the optimal control.PHDMechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/93962/1/rakeshmp_1.pd

Improving smart charging for electric vehicle fleets by integrating battery and prediction models

With increasing electrification of vehicle fleets there is a rising demand for the effective use of charging infrastructure. Existing charging infrastructures are limited by undersized connection lines and a lack of charging stations. Upgrades require significant financial investment, time and effort. Smart charging represents an approach to making the most of existing charging infrastructure while satisfying charging needs. Smart charging involves scheduling for electric vehicles (EVs). In other words, smart charging approaches decide which EV may charge at which charging station and at which current during which time periods. Planning flexibility is determined by the length of stay and the available electrical supply. First, we present an approach for smart charging combining day-ahead planning with real-time planning. For day-ahead planning, we use a mixed integer programming model to compute optimal schedules while making use of information available ahead of time. We then describe a schedule guided heuristic which adapts precomputed schedules in real-time. Second, we address uncertainty in smart charging. For example, EV departure times are an important component in prioritization but are uncertain ahead of time. We use a regression model trained on historical data to predict EV departure times. We integrate predictions directly in the smart charging heuristic used in the first approach. Experimental results show a more accurate EV departure time leads to a more accurate EV prioritization and a higher amount of delivered energy. Third, we present two approaches which allow the smart charging heuristic to take EV charging behavior into account. In practice, EVs charge using nonlinear charge profiles where power declines towards the end of each charging process. There is thus a gap between the scheduled power and the actual charging power if nonlinear charge profiles are not taken into account. The first approach uses a traditional equivalent circuit model (ECM) to model EV charging behavior but in practice is limited by the availability of battery parameters. The second approach relies on a regression model trained on historical data to directly predict EV charging profiles. In each of the two approaches, the model of the EV's charging profile is directly integrated into the smart charging heuristic which allows the heuristic to produce more accurate charge plans. Experimental results show EVs charge significantly more energy because the charging infrastructure is used more effectively. Finally, we present an open source package containing the smart charging heuristic and describe results from applying the heuristic in a one-year field test. Experimental results from the field test show EVs at six charging stations can be scheduled for charging when the grid connection only allows two EVs to charge concurrently. Runtime measurements demonstrate the heuristic is applicable in real time and scales to large fleet sizes

Electric vehicles in Smart Grids: Performance considerations

Distributed power system is the basic architecture of current power systems and demands close cooperation among the generation, transmission and distribution systems. Excessive greenhouse gas emissions over the last decade have driven a move to a more sustainable energy system. This has involved integrating renewable energy sources like wind and solar power into the distributed generation system. Renewable sources offer more opportunities for end users to participate in the power delivery system and to make this distribution system even more efficient, the novel Smart Grid concept has emerged. A Smart Grid: offers a two-way communication between the source and the load; integrates renewable sources into the generation system; and provides reliability and sustainability in the entire power system from generation through to ultimate power consumption. Unreliability in continuous production poses challenges for deploying renewable sources in a real-time power delivery system. Different storage options could address this unreliability issue, but they consume electrical energy and create signifcant costs and carbon emissions. An alternative is using electric vehicles and plug-in electric vehicles, with two-way power transfer capability (Grid-to-Vehicle and Vehicle-to-Grid), as temporary distributed energy storage devices. A perfect fit can be charging the vehicle batteries from the renewable sources and discharging the batteries when the grid needs them the most. This will substantially reduce carbon emissions from both the energy and the transportation sector while enhancing the reliability of using renewables. However, participation of these vehicles into the grid discharge program is understandably limited by the concerns of vehicle owners over the battery lifetime and revenue outcomes. A major challenge is to find ways to make vehicle integration more effective and economic for both the vehicle owners and the utility grid. This research addresses problems such as how to increase the average lifetime of vehicles while discharging to the grid; how to make this two-way power transfer economically viable; how to increase the vehicle participation rate; and how to make the whole system more reliable and sustainable. Different methods and techniques are investigated to successfully integrate the electric vehicles into the power system. This research also investigates the economic benefits of using the vehicle batteries in their second life as energy storage units thus reducing storage energy costs for the grid operators, and creating revenue for the vehicle owners