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

ACN-Sim: An Open-Source Simulator for Data-Driven Electric Vehicle Charging Research

ACN-Sim is a data-driven, open-source simulation environment designed to accelerate research in the field of smart electric vehicle (EV) charging. It fills the need in this community for a widely available, realistic simulation environment in which researchers can evaluate algorithms and test assumptions. ACN-Sim provides a modular, extensible architecture, which models the complexity of real charging systems, including battery charging behavior and unbalanced three-phase infrastructure. It also integrates with a broader ecosystem of research tools. These include ACN-Data, an open dataset of EV charging sessions, which provides realistic simulation scenarios and ACN-Live, a framework for field-testing charging algorithms. It also integrates with grid simulators like MATPOWER, PandaPower and OpenDSS, and OpenAI Gym for training reinforcement learning agents.Comment: 9 pages, 8 figures. [v2] Update timezone issue with Fig. 8 where x-axis and background load was shifted by 3 hour

DClEVerNet: Deep Combinatorial Learning for Efficient EV Charging Scheduling in Large-scale Networked Facilities

With the electrification of transportation, the rising uptake of electric vehicles (EVs) might stress distribution networks significantly, leaving their performance degraded and stability jeopardized. To accommodate these new loads cost-effectively, modern power grids require coordinated or ``smart'' charging strategies capable of optimizing EV charging scheduling in a scalable and efficient fashion. With this in view, the present work focuses on reservation management programs for large-scale, networked EV charging stations. We formulate a time-coupled binary optimization problem that maximizes EV users' total welfare gain while accounting for the network's available power capacity and stations' occupancy limits. To tackle the problem at scale while retaining high solution quality, a data-driven optimization framework combining techniques from the fields of Deep Learning and Approximation Algorithms is introduced. The framework's key ingredient is a novel input-output processing scheme for neural networks that allows direct extrapolation to problem sizes substantially larger than those included in the training set. Extensive numerical simulations based on synthetic and real-world data traces verify the effectiveness and superiority of the presented approach over two representative scheduling algorithms. Lastly, we round up the contributions by listing several immediate extensions to the proposed framework and outlining the prospects for further exploration

Cost Minimization for Charging Electric Bus Fleets

Recent attention for reduced carbon emissions has pushed transit authorities to adopt battery electric buses (BEBs). One challenge experienced by BEB users is extended charge times, which create logistical challenges and may force BEBs to charge when energy is more expensive. Furthermore, BEB charging leads to high power demands, which can significantly increase monthly power costs and may push the electrical infrastructure beyond its present capacity, requiring expensive upgrades. This work presents a novel method for minimizing the monthly cost of BEB charging while meeting bus route constraints. This method extends previous work by incorporating a more novel cost model, effects from uncontrolled loads, differences between daytime and overnight charging, and variable rate charging. A graph-based network-flow framework, represented by a mixed-integer linear program, encodes the charging action space, physical bus constraints, and battery state of the charge dynamics. The results for three scenarios are considered: uncontested charging, which uses equal numbers of buses and chargers; contested charging, which has more buses than chargers; and variable charge rates. Among other findings, we show that BEBs can be added to the fleet without raising the peak power demand for only the cost of the energy, suggesting that conversion to electrified transit is possible without upgrading power delivery infrastructure

A hybrid power heronian function-based multi-criteria decision-making model for workplace charging scheduling algorithms.

This study proposes a new multi-criteria decision-making model to determine the best smart charging scheduling that meets electric vehicle (EV) user considerations at work-places. An optimal charging station model is incorporated into the decision-making for a quantitative evaluation. The proposed model is based on a hybrid Power Heronian functions in which the linear normalization method is improved by applying the inverse sorting algorithm for rational and objective decision-making. This enables EV users to specify and evaluate multi-criteria for considering their aspects at workplaces. Five different charging scheduling algorithms with AC dual port L2 and DC fast charging electric vehicle supply equipment (EVSE) are investigated. Based on EV users from the field, the required charging time, EVSE occupancy, the number of EVSE units, and user flexibility are found to have the highest importance degree, while charging cost has the lowest importance degree. The experimental results show that, in terms of meeting EV users' considerations at workplaces, scheduling EVs based on their charging energy needs performs better as compared to scheduling them by their arrival and departure times. While the scheduling alternatives display similar ranking behavior for both EVSE types, the best alternative may differ for the EVSE type. To validate the proposed model, a comparison against three traditional models is performed. It is demonstrated that the proposed model yields the same ranking order as the alternative approaches. Sensitivity analysis validates the best and worst scheduling alternatives

Co-simulation of optimal EVSE and techno-economic system design models for electrified fleets

As the transition to electric mobility is expanding at a rapid pace, operationally feasible and economically viable charging infrastructure is needed to support electrified fleets. This paper presents a co-simulation of optimal electric vehicle supply equipment (EVSE) and techno-economic system design models to investigate the behaviors of various EVSE configurations from cost and technical aspects. While the system design optimization is performed for a grid-tied PV system, the optimal EVSE model considers all EVSE options which are currently installed at workplaces. To investigate the impact of EV utilization rate, three fleet sizes are considered that are generated based on real EV fleet data. Furthermore, the impact of electricity rates is also explored through an innovative EV-specific (BEV) rate and a conventional time-of-use (ToU) tariff. It is shown that investing in grid-tied renewable energy technologies for workplace charging infrastructure supply can lower charging costs. Cost savings differ from EVSE types and fleet size under the BEV rate while EVSEs display similar cost-saving behavior under the ToU tariff irrespective of fleet size. DC Fast Charging (DCFC) EVSE is found to be highly sensitive to fleet size as compared to AC EVSEs. Moreover, DCFCs make better use of the BEV rate which makes their economics competitive as much as AC EVSEs. Finally, it is found that the fleet size and AC EVSE types have a minor effect on the use of renewable energy in contrast to the DCFC case