Vehicle-to-grid aggregator to support power grid and reduce electric vehicle charging cost

This paper presents an optimised bidirectional Vehicle-to-Grid (V2G) operation, based on a fleet of Electric Vehicles (EVs) connected to a distributed power system, through a network of charging stations. The system is able to perform day-ahead scheduling of EV charging/discharging to reduce EV ownership charging cost through participating in frequency and voltage regulation services. The proposed system is able to respond to real-time EV usage data and identify the required changes that must be made to the day-ahead energy prediction, further optimising the use of EVs to support both voltage and frequency regulation. An optimisation strategy is established for V2G scheduling, addressing the initial battery State Of Charge (SOC), EV plug-in time, regulation prices, desired EV departure time, battery degradation cost and vehicle charging requirements. The effectiveness of the proposed system is demonstrated using a standardized IEEE 33-node distribution network integrating five EV charging stations. Two case studies have been undertaken to verify the contribution of this advanced energy supervision approach. Comprehensive simulation results clearly show an opportunity to provide frequency and voltage support while concurrently reducing EV charging costs, through the integration of V2G technology, especially during on-peak periods when the need for active and reactive power is high

Predictive control for energy management in all/more electric vehicles with multiple energy storage units

The paper describes the application of Model Predictive Control (MPC) methodologies for application to electric and hybrid-electric vehicle drive-train formats incorporating multiple energy/power sources. Particular emphasis is given to the co-ordinated management of energy flow from the multiple sources to address issues of extended vehicle range and battery life-time for all-electric drive-trains, and emissions reduction and drive-train torsional oscillations, for hybrid-electric counterparts, whilst accommodating operational constraints and, ultimately, generic non-standard driving cycles

Using mobility information to perform a feasibility study and the evaluation of spatio-temporal energy demanded by an electric taxi fleet

Half of the global population already lives in urban areas, facing to the problem of air pollution mainly caused by the transportation system. The recently worsening of urban air quality has a direct impact on the human health. Replacing today’s internal combustion engine vehicles with electric ones in public fleets could provide a deep impact on the air quality in the cities. In this paper, real mobility information is used as decision support for the taxi fleet manager to promote the adoption of electric taxi cabs in the city of San Francisco, USA. Firstly, mobility characteristics and energy requirements of a single taxi are analyzed. Then, the results are generalized to all vehicles from the taxi fleet. An electrificability rate of the taxi fleet is generated, providing information about the number of current trips that could be performed by electric taxis without modifying the current driver mobility patterns. The analysis results reveal that 75.2% of the current taxis could be replaced by electric vehicles, considering a current standard battery capacity (24–30 kWh). This value can increase significantly (to 100%), taking into account the evolution of the price and capacity of the batteries installed in the last models of electric vehicles that are coming to the market. The economic analysis shows that the purchasing costs of an electric taxi are bigger than conventional one. However, fuel, maintenance and repair costs are much lower. Using the expected energy consumption information evaluated in this study, the total spatio-temporal demand of electric energy required to recharge the electric fleet is also calculated, allowing identifying optimal location of charging infrastructure based on realistic routing patterns. This information could also be used by the distribution system operator to identify possible reinforcement actions in the electric grid in order to promote introducing electric vehicles

An energy management strategy for plug-in hybrid electric vehicles

This dissertation formulates a proposal for a real time implementable energy management strategy (EMS) for plug-in hybrid electric vehicles. The EMS is developed to minimize vehicle fuel consumption through the utilisation of stored electric energy and high-efficiency operation of powertrain components. This objective is achieved through the development of a predictive EMS, which, in addition to fuel efficiency, is optimized in terms of computational cost and drivability. The requirement for an EMS in hybrid powertrain vehicles stems from the integration of two energy stores and converters in the powertrain; in the case of hybrid electric vehicles (HEVs) usually a combustion engine and one or more electric machines powered by a battery. During operation of the vehicle the EMS controls power distribution between engine and electric traction motor. Power distribution is optimized according to the operating point dependent efficiencies of the components, energy level of the battery and trip foreknowledge. Drivability considerations, e.g. frequency of engine starts, can also be considered. Due to high oil prices and legislative requirements caused by the environmental impact of greenhouse emissions, fuel economy has gained importance in recent years. In addition to increased fuel economy, powertrain hybridization permits the substituton of fuel for electrical energy by implementing an external recharging option for the battery. This vehicle class, incorporating a battery rechargeable via the electrical grid, is known as a plug-in HEV (PHEV). PHEV share characteristics of both HEVs and all-electric vehicles combining several advantages of both technologies. The rechargeable battery feature of the PHEVs makes their EMS development espe-cially challenging. For minimal fuel consumption, the battery is discharged optimally over the whole trip length, prioritising electrical energy when driving conditions are such that its use maximises the fuel saving that can be achieved. Therefore, an EMS for a PHEV depends heavily on the availability of a priori knowledge about the trip, i.e. the knowledge about future vehicle speed and road grade. This requires the driver to indi-cate the route before trip start. The route knowledge in combination with GPS or Galileo based next generation navigation systems using information from a geographic in-formation system (GIS) about terrain height profile, road type (e.g. motorway or country road), and legal speed limits can be evaluated by a speed prediction algorithm including information about the driver's behaviour for a detailed prediction of the trip. These navigation systems and algorithms in combination with expected future advances and the deployment of technologies such as intelligent transport systems (ITS) and vehicle-to-vehicle communication (V2V), will make more exact traffic information available to further improve prediction. Despite expected advances in prediction quality, inaccuracy of prediction data has to be considered and is therefore regarded in this work. The EMS proposed in this dissertation combines different approaches which are exe-cuted step by step. A first approximation of the energy distribution during the trip is based on a mixed integer linear program (MILP), which gives the optimal energy state of the battery during the trip. This is especially important for trips with long uphill, downhill or urban phases, i.e. sections with a particularly high or lower power requirement. The results from MILP are then used by a dynamic programming (DP) algorithm to calculate optimal torque and gear using a receding prediction horizon. Using a receding prediction horizon, an important reduction of computational cost is achieved. Lastly, from the DP results a rule-based strategy is extracted using a support vector machine (SVM). This last step is necessary to ensure the drivability of the vehicle also for inaccurate prediction data

Numerical Analysis of National Travel Data to Assess the Impact of UK Fleet Electrification

Accurately predicting the future power demand of electric vehicles is important for developing policy and industrial strategy. Here we propose a method to create a representative set of electricity demand profiles using survey data from conventional vehicles. This is achieved by developing a model which maps journey and vehicle parameters to an energy consumption, and applying it individually to the entire data set. As a case study the National Travel Survey was used to create a set of profiles representing an entirely electric UK fleet of vehicles. This allowed prediction of the required electricity demand and sizing of the necessary vehicle batteries. Also, by inferring location information from the data, the effectiveness of various charging strategies was assessed. These results will be useful in both National planning, and as the inputs to further research on the impact of electric vehicles

Integrated Generation Management for Maximizing Renewable Resource Utilization

Two proposed methods to reduce the effective intermittency and improve the efficiency of wind power generation in the grid are spatial smoothing of wind generation and utilization of short term electrical storage to deal with lulls in production. In this thesis, based on a concept called integrated generation management (IGM), we explore the impact of spatial smoothing and the use of emerging plug-in hybrid electric vehicles (PHEVs) as a potential storage resource to the smart-grid. IGM combines nuclear, slow load-following coal, fast load-following natural gas, and renewable wind generation with an optimal control method to maximize the renewable generation and minimize the fossil generation. With the increasing penetration of PHEVs, the power grid is seeing new opportunities to make itself smarter than ever by utilizing those relatively large batteries. Based on current projections of PHEV market penetration and various wind generation scenarios, we demonstrate the potential for efficient wind integration at levels of approaching 30% of the aver- age electrical load with utilization efficiency exceeding 65%. At lower levels of integration (e.g. 15%), efficiencies are possible exceeding 85%

Novel battery model of an all-electric personal rapid transit vehicle to determine state-of-health through subspace parameter estimation and a Kalman Estimator

Abstract--The paper describes a real-time adaptive battery model for use in an all-electric Personal Rapid Transit vehicle. Whilst traditionally, circuit-based models for lead-acid batteries centre on the well-known Randles’ model, here the Randles’ model is mapped to an equivalent circuit, demonstrating improved modelling capabilities and more accurate estimates of circuit parameters when used in Subspace parameter estimation techniques. Combined with Kalman Estimator algorithms, these techniques are demonstrated to correctly identify and converge on voltages associated with the battery State-of-Charge, overcoming problems such as SoC drift (incurred by coulomb-counting methods due to over-charging or ambient temperature fluctuations). Online monitoring of the degradation of these estimated parameters allows battery ageing (State-of-Health) to be assessed and, in safety-critical systems, cell failure may be predicted in time to avoid inconvenience to passenger networks. Due to the adaptive nature of the proposed methodology, this system can be implemented over a wide range of operating environments, applications and battery topologies