Charging Decisions Research Articles

Driving change: Electric vehicle charging behavior and peak loading

Electric vehicles (EVs) are projected to comprise 40 % of Aotearoa New Zealand's light vehicle fleet by 2040. However, charging decisions made by EV drivers, such as whether to charge immediately or delay charging, will affect peak electricity demand and lifetime of distribution network components. This study uses an agent-based model (ABM) of EV charging to investigate the effect of different EV penetration levels and owner charging decisions on components in New Zealand's residential electricity networks, although the methodology is wholly generalizable to other countries or regions. Monte Carlo simulation is performed for EV charging in a neighborhood of 71 houses, based on a representative residential distribution network, and simulated for 20 days. The key outcome measure is the rate of ‘Exceedance’ of the 300 kVA baseline transformer limit, where greater Exceedance entails shorter lifecycle and increased maintenance or capital costs to the provider. Results show delayed-charging algorithms (‘Altruistic charging’) decrease peak electricity demand and Exceedance, while drivers charging immediately (‘Selfish charging’) increases Exceedance. New Zealand's residential electricity networks are expected to accommodate a 40 % EV transition with 100 % Altruistic charging, as Exceedance is expected to increase less than 20 % from Exceedance without EVs. However, Selfish charging increases the rate of Exceedance by more than 250 %. Longer-term, increasing EV penetration and household electricity demand will require increased workplace charging infrastructure, electricity network upgrades, and/or automated and Internet of Things (IoT)-enabled Demand Side Management (DSM) of EV charging to avoid high rates of Exceedance and increased maintenance and replacement costs.

Matheuristics for a multi-day electric bus assignment and overnight recharge scheduling problem

To limit greenhouse gas emissions in the public transportation sector, transit authorities are constantly increasing the proportion of electric buses (EBs) in their fleets. Due to space and budget constraints, more often than not, the number of chargers that can be installed at the depots is less than the number of EBs in the fleet. Moreover, even if there is one charger for each EB, it is virtually impossible to use all the chargers at the same time due to maximum power grid constraints. Thus, there is a need to devise optimization techniques that help to recharge the buses in an efficient and acute way, taking into account these charging infrastructure capacity constraints. In this study, we consider a multi-day electric bus assignment and overnight recharge scheduling problem that can be briefly stated as follows. Given a set of vehicle blocks (sequences of timetabled bus trips performed by the same bus on a given day) to be operated over several days, a set of identical EBs, and a set of chargers available at the depot whose charging function is a piecewise linear function, the problem consists in assigning an EB to each block and to schedule the overnight recharging operations at the depot such that the EBs never run out of energy when performing their blocks and the depot charging capacity is never exceeded. The objective is twofold: minimizing the total charging costs and minimizing the impact of the charging decisions in the long-term health of the batteries. We first model this problem as a mixed integer linear program (MILP) that can be solved with a commercial solver. Then, to yield faster computational times, we develop two multi-phase matheuristics based on the MILP. To show the interest of considering a multiple-day horizon, we introduce two other matheuristics that mimic current industrial practices and solve the problem sequentially, one day at a time. To evaluate all these algorithms, we use a set of 264 instances generated from data of an EB operator.

Cognitive Biases and Shared Decision Making in Acute Brain Injury.

Many patients hospitalized after severe acute brain injury are comatose and require life-sustaining therapies. Some of these patients make favorable recoveries with continued intensive care, while others do not. In addition to providing medical care, clinicians must guide surrogate decision makers through high-stakes, emotionally charged decisions about whether to continue life-sustaining therapies. These consultations require clinicians first to assess a patient's likelihood of recovery given continued life-sustaining therapies (i.e., prognosticate), then to communicate that prediction to surrogates, and, finally, to elicit and interpret the patient's preferences. At each step, both clinicians and surrogates are vulnerable to flawed decision making. Clinicians can be imprecise, biased, and overconfident when prognosticating after brain injury. Surrogates can misperceive the choice and misunderstand or misrepresent a patient's wishes, which may never have been communicated clearly. These biases can undermine the ability to reach choices congruent with patients' preferences through shared decision making (SDM). Decision science has extensively studied these biases. In this article, we apply that research to improving SDM for patients who are comatose after acute brain injury. After introducing SDM and the medical context, we describe principal decision science results as they relate to neurologic prognostication and end-of-life decisions, by both clinicians and surrogates. Based on research regarding general processes that can produce imprecise, biased, and overconfident prognoses, we propose interventions that could improve SDM, supporting clinicians and surrogates in making these challenging decisions.

Justice in conflict: Principle of complementarity or principle of competition?

The establishment of a permanent international criminal court was a necessity and the fear of it infringing on a state's sovereignty was real and ever present. As a result of this fear the International Criminal Court could not be awarded primary jurisdiction, and a compromise had to be reached in which it would operate under a regime of complementarity. This article focuses on the Simone Gbagbo case, as the first woman to be charged by the Court, with the object of nuancing the principle of complementarity in the various stages of an international criminal trial and the extent to which it portrays the tension of state sovereignty, tracing it from its infant historical or rudimentary practices to the current practice and making the necessary recommendations. All of this will be done by contextualising it all within the Cote d'lvoire situation, particularly as it relates to complementarity. The article makes recommendations that focus on how and why the lCC should avoid seeking to dictate and impose its prosecutorial strategy on the domestic officials so as to avoid a crisis of its legitimacy being questioned, and the state's refusal to cooperate with the Court. lt concludes with the caution that when the practices of the lCC and its Prosecutor make charging decisions for the state and embrace undermining the prosecutorial discretion of the domestic authorities, then the principle of complementarity will have been officially decimated and the principle of complementarity officially birthed.

Development of a multi criteria model for assisting EV user charging decisions

Development of a multi criteria model for assisting EV user charging decisions

Estimating the electric vehicle charging demand of multi-unit dwelling residents in the United States

Early battery electric vehicle (EV) adopters can access home chargers for reliable charging. As the EV market grows, residents of multi-unit dwellings (MUDs) may face barriers in owning EVs and charging them without garage or parking availability. To investigate the mechanisms that can bridge existing disparities in home charging and station deployment, we characterized the travel behavior of MUD residents and estimated their EV residential charging demand. This study classifies the travel patterns of MUD residents by fusing trip diary data from the National Household Travel Survey and housing features from the American Housing Survey. A hierarchical agglomerative clustering method was used to cluster apartment complex residents’ travel profiles, considering attributes such as dwell time, daily vehicle miles traveled (VMT), income, and their residences’ US census division. We propose a charging decision model to determine the charging station placement demand in MUDs and the charging energy volume expected to be consumed, assuming that MUD drivers universally operate EVs in urban communities. Numerical experiments were conducted to gain insight into the charging demand of MUD residents in the US. We found that charging availability is indispensable for households that set out to meet 80% state of charge by the end of the day. When maintaining a 20% comfortable state of charge the entire day, the higher the VMT are, the greater the share of charging demand and the greater the energy use in MUD chargers. The upper-income group requires a greater share of MUD charging and greater daily kWh charged because of more VMT.

Real-Time Coordinated Operation of Power and Autonomous Electric Ride-Hailing Systems

Integration of self-driving functions in electric vehicles is radically changing the transportation systems, and representing an opportunity for power utilities to develop innovative solutions for harnessing the spatio-temporal charging flexibility of autonomous electric vehicles (AEVs). This paper develops a multi-agent reinforcement learning model for intelligent real-time coordinated operation of interdependent autonomous electric ride-hailing system (AERS) and power distribution system (PDS), which enables the coordinated routing and charging of AEVs while ensuring the quality of service by meeting spatio-temporal passenger demand and regularly charging the batteries. The proposed model adopts a modified deep Q-network method with multi-agent rollout to determine the near-optimal solution for the coordinated operation problem that determines the routing and charging of numerous AEVs in real-time. The proposed model is tested on a 13-node transportation network, adopted from the Salt Lake City transportation system, and the IEEE 33-bus test power distribution system to showcase the efficiency of the proposed model in determining the real-time routing and charging decisions for AEVs while meeting the operational constraints of both AERS and PDS.

EV Fast Charging Station Planning Considering Competition Based on Stochastic Dynamic Equilibrium

The rapid growth of electric vehicles (EVs) and the deployment of fast charging infrastructures bring considerable impacts on the planning and operation of power systems. Integrating photovoltaic (PV) and energy storage system (ESS) with fast charging station (FCS) can alleviate the negative impacts and bring benefits to both power system operation and charging service provider. In this paper, an integrated procedure is proposed for the charging service provider to optimally plan fast charging stations integrated with PV and ESS in urban area, while taking the influence of its competitors, EV users' decision-making psychology, the uncertainties of charging demand and PV power output into consideration. Firstly, a charging demand estimation method based on average travel speed is presented. Secondly, a stochastic dynamic user choice equilibrium (SDUCE) model and its solution algorithm based on method of successive averages (MSA) are proposed. The model considers the key factors influencing the charging decision of EV users as well as the uncertainty of their perceptions of utility values. In addition, the time-varying queuing time and the temporal coupling among EVs arriving at a charging station in different time periods are also considered. Thirdly, based on the equilibrium model, a multi-scenario benefit maximization model is built from the perspective of charging service provider, and solved by genetic algorithm. Finally, a distributionally robust optimization (DRO) planning model based on the Wasserstein-metric is formulated to determine the capacities of PV and ESS. The proposed planning method is tested using real traffic data of Shenzhen City. Simulation results verify the effectiveness of the proposed method.

Charging Cost-Aware Fleet Management for Shared On-Demand Green Logistic System

With the advancement of transportation electrification and Internet of Things (IoT), the cloud based shared on-demand logistic fleet management platforms, such as Lalamove and Gogovan, become more and more popular. Under this setting, the scheduling platform needs to dispatch vehicles while dealing with the increasing charging demands of logistic fleet and serving the dynamically arrived logistic requests. Based on the information obtained via IoT technology, the platform shall coordinate the fleet management decisions, such as logistic order matching, vehicle routing and charging decisions to optimize the operational profit of fleet. To solve the fleet management problem in the shared on-demand green logistic system in an online manner, we propose a deep reinforcement learning based scheduling method. We use the real-world data to model the stochastic arriving of logistic requests, and conduct experiments to explore the navigation effect of different charging pricing schemes. The simulation results demonstrate that the proposed method can adaptively optimize the fleet management decisions of logistic fleet while enabling multiple pickups operation and maintaining the quality of delivery service.

Distributed collaborative optimization for coupled transportation and power systems operation considering carbon emission and elastic travel demand

With the continuous expansion of the scale of electric vehicles (EV), the coupling between the distribution system and the urban transportation system is deepening. In the context of global decarbonization, it is necessary to formulate reasonable incentives to optimize the operation of coupled transportation and power systems towards an economical and low-carbon system. To achieve this goal, we propose a collaborative optimization model that considers the overall carbon emission cost of the coupled system. To respect the actual living habits of users, elastic travel needs are also considered. First, on the transportation system side, considering two main participants, electric vehicles and gasoline vehicles (GV), a game-theoretic based traffic assignment model using user equilibrium criteria is built. To reduce the computational complexity of the sub-problem in achieving the user equilibrium, an efficient path generation model is designed, which considers the charging decision and range anxiety problems of electric vehicles and the cost of carbon emissions of gasoline vehicles. Second, on the power grid side, the optimal power flow using second-order cone programming (SOCP) for power distribution systems is built, considering the cost and carbon emission of various energy sources. Third, considering the incomplete information between the coupled system, the entire model is decoupled and solved by a distributed collaborative optimization model using the alternating direction method of multipliers (ADMM) algorithm. Finally, the proposed model and solution methods are verified by comprehensive case studies. Simulation results show that the distributed collaborative optimization model can effectively reduce the emissions of the coupled system, and provide a scientific basis for the formulation of charging prices.

EV-Station-Grid Coordination Optimization Strategy Considering Psychological Preferences

This paper proposes the electric vehicle (EV)-station-grid coordination optimization strategy considering user preferences, which regulates the charging behaviors of EV users from the user side to ensure the stable and safe operation of the power grid. Firstly, the spatio-temporal prediction model of charging load based on speed-temperature is developed. The model of EV power consumption per unit mileage affected by temperature and EV speed is constructed, and the shortest path algorithm is applied to determine the driving paths of EVs so as to judge the charging demand in combination with the state of charge (SOC) of the battery and to determine the charging periods and locations of the EVs, thus obtaining the spatio-temporal information of the charging load. Secondly, a multi-attribute charging decision model considering user preferences is constructed. Fuzzy clustering and rough set theory are applied to mine user behavior preferences, combined with behavioral economics to describe users’ irrational charging decision-making psychology. Lastly, a real-time charging price model considering voltage fluctuation index and user charging cost is constructed to analyze the impact of price on guiding charging behaviors. The simulation results verify the effectiveness and performance of the collaborative optimization strategy.

Optimal dispatch of hydrogen/electric vehicle charging station based on charging decision prediction

The hydrogen/electric vehicle charging station (HEVCS) is widely regarded as a highly attractive system for facilitating the popularity of hydrogen and electric vehicles in the future. However, conventional optimal dispatch of HEVCS could lead to poor performance due to the lack of adequate consideration of vehicle charging decision behaviours and neglection of the impacts of different information sources on it. This paper investigates a charging demand prediction method that considers multi-source information and proposes a multi-objective optimal dispatching strategy of HEVCS. First, an information interaction framework of integrated road network, vehicles and HEVCS is introduced. Road network model and HEVCS model are established based on the proposed framework. To improve the flexibility of dispatch, two charging modes are designed, which are intended to guide drivers to adjust their consumption behaviour by electricity price incentives. Furthermore, psychologically based hybrid utility-regret decision model and Weber-Fechner (W–F) stimulus model are developed to reasonably predict drivers' choice of charging stations and charging modes. The daily revenue of HEVCS and the total queuing time of drivers are the objective functions considered in this paper simultaneously. The above multi-objective optimization results that the proposed strategy can effectively improve the benefits of HEVCS and reduce energy waste. Additionally, this paper discusses the results of a sensitivity analysis conducted by varying incentive discount, which reveals the combined benefits of the HEVCS and the vehicles are effectively increased by setting reasonable incentive discounts.

An online reinforcement learning approach to charging and order-dispatching optimization for an e-hailing electric vehicle fleet

Given the uncertainty of orders and the dynamically changing workload of charging stations, how to dispatch and charge electric vehicle (EV) fleets becomes a significant challenge facing e-hailing platforms. The common practice is to dispatch EVs to serve orders by heuristic matching methods but enable EV drivers to independently make charging decisions based on their experiences, which may compromise the platform’s performance. This study proposes a Markov decision process to jointly optimize the charging and order-dispatching schemes for an e-hailing EV fleet, which provides pick-up services for passengers only from a designated transportation hub (i.e., no pick-up from different locations). The objective is to maximize the total revenue of the fleet throughout a finite horizon. The complete state transition equations of the EV fleet are formulated to track the state-of-charge of their batteries. To learn the charging and order-dispatching policy in a dynamic stochastic environment, an online approximation algorithm is developed, which integrates the model-based reinforcement learning (RL) framework with a novel SARSA(Δ)-sample average approximation (SAA) architecture. Compared with the model-free RL algorithm and approximation dynamic programming (ADP), our algorithm explores high-quality decisions by an SAA model with empirical state transitions and exploits the best decisions so far by an SARSA(Δ) sample-trajectory updating. Computational results based on a real case show that, compared with the existing heuristic method and the ADP in the literature, the proposed approach increases the daily revenue by an average of 31.76% and 14.22%, respectively.

The history and future of in vitro fertilization in the United States: the complex interrelationships among basic science, human medicine, and politics

Although much of the foundational basic scientific and clinical research was conducted in the United States, the first invitro fertilization (IVF) birth occurred in the United Kingdom. Why? For centuries, all research surrounding the field of "reproduction" has elicited bipolar passionate responses by the American public, and the issue of "test tube babies" has been no different. The history of conception in the United States is defined by complex interrelationships among scientists, clinicians, and politically charged decisions by various branches of the US government. With a focus on research in the United States, this review summarizes the early scientific and clinical advances important to the development of IVF and then addresses the potential future developments in IVF. We also consider what future advances are possible in the United States given the current regulations, laws, and funding.

Interaction Strategy Between the Vehicle-station-grid Considering Privacy Protection

The number of electric vehicles (EVs) is increasing daily, and the charging demand for large-scale EVs has a greater impact on the distribution network, so it is urgent to study the cooperative interaction optimization strategy of the “EV-charging station-distribution grid.” In this paper, we take the charging load of EVs in the regional distribution network as the research object. We establish a multi-intelligence reinforcement learning-based optimization strategy for the interaction between EVs and the grid to obtain the optimal charging decision to minimize the peak-to-valley difference between the user-side economy and the grid-side load. The results show that the proposed EV-grid interaction optimization strategy can effectively reduce the peak-to-valley difference in charging station load and realize the efficient cooperative interaction of the “vehicle-station-grid.”

ROMPIENDO LAS AUTOCONCEPCIONES ERRONEAS IMPULSORES DE LOS CICLOS EMOCIONALES DE LOS ESTUDIANTES DE MBA PARA AYUDARLES A CONVERTIRSE EN LÍDERES RESPONSABLES

Responsible leadership training requires development of individuals who are both knowledgeable and emotionally mature so that they can overcome personal biases to make honest and ethical decisions that have a positive social impact within and outside the organization. Current MBA class exercises use a few trait-based surveys and basic techniques to manage one’s emotions along with leadership definitions that can be misinterpreted by students to be devoid of liable behaviors. Consequently, the problem of self-misconception persists with no change in students’ reasoning about the core problem that is causing their emotionally charged decision. Hence, most students fail to sustain their emotional management processes. To address this need to recognize and correct one’s self-misconceptions to uphold emotional maturity, our specific course of action is to address it holistically based on a preexisting Upanishadic model. The primary contribution of this paper is to bring to the forefront a practical, and useable model that can provide clear steps to refine one’s habitual orientations caused by self-misconceptions. We present the causal mechanism underlying the cognitive-emotional mechanisms wherein the core constructs are Knowing, Active and Inert qualities along six behavior influencing areas which elicit three distinct groups of emotions resulting in consequent decisions. Using a short case scenario-based exercise, we put forth steps students can take to develop responsible leadership qualities. Implications in the form of less stressful and happier workplaces are briefly discussed. A new definition of leadership is presented that helps one distinguish true leadership from notorious ones. The model and the accompanying steps help MBA students develop into fair, thoughtful, knowledgeable, compassionate, and truthful leaders, who work for the benefit of the entire society.

Electric vehicle charging pile capacity planning based on normal distribution Monte Carlo sampling model

Abstract In recent years, environmental issues have gradually become one of the most concerning issues in the world. New energy vehicles have attracted wide attention by their good environmental and social benefits such as zero exhaust emissions and low noise pollution. This paper proposes a regional charging demand forecasting method for electric vehicles (EVs) based on hierarchical charging decision model to solve the problem of charging pile capacity planning, which affects the charging decision behavior of EVs users. The Monte Carlo sampling model is adopted to obtain the time distribution of electric vehicle arriving at each functional area, the distribution of battery state of charge when arriving, and the distribution of average daily mileage. The negative exponential distribution model is used to obtain the average mileage distribution. The fuzzy system theory is proposed to calculate the charging probability, and the fuzzy rule base is adjusted according to the actual data to ensure that the fuzzy rule base is real and effective. Finally, the charging demand distribution curve of electric vehicles in each functional area is obtained by sampling simulation. Through experimental analysis, optimizing the capacity configuration of charging facilities in the station can well describe how electric vehicle users make charging decisions in the actual situation. The research on electric vehicle charging stations can slow down the waiting time of electric vehicle charging, and then affect the process of replacing fuel vehicles with electric vehicles to achieve the development goal of popularizing electric vehicles. It is of great significance for the promotion of new energy vehicles to rationally plan the location and capacity of electric vehicle charging stations.

Policing and Prosecuting Sexual Assault Against Women of Color: Applying Critical Race Theory and Intersectionality Frameworks to Examine Arrest and Charging Decisions in Cases Involving Black and Latina Victims

Using Critical Race Theory and an intersectional framework, the present study focuses on the case processing of sexual assault (SA) incidents involving Black and Latina victims. Importantly, much of the prior literature assessing arrest and charging decisions have been whitewashed by failing to consider that the factors influencing the case progression of white victims’ SA experiences may not appropriately apply to women of Color. Oftentimes, this scholarship merely controls for victim and suspect race without considering how legal and extralegal incident and case factors may operate differently for victims of Color. The present study addresses this oversight.

Multiobjective optimal operation strategy for electric vehicle battery swapping station considering battery degradation

The study aims to analyze a futuristic view of the automobile industry conducive to the much-needed penetration of Electric Vehicles (EVs) as per the current environmental and economic scenario. The study suggests the roll-out of EVs in tandem with the supporting Charging Infrastructure, which is a prerequisite for adopting the former. Although transport electrification is a much-accentuated and researched solution to the deteriorating environment and plummeting conventional resources, the design, production, manufacturing, use, degradation, and disposal of an exponential number of lithium-ion batteries for the same have environmental, economic, and social impacts. Thus, emphasis has been made on the sustainable use of charging infrastructure that curbs unnecessary and early battery aging from fast charging technology. Battery swap requests at a Battery Swapping Station (BSS) can be served via batteries from either available battery stock or by charging previously incoming discharged batteries. The study suggests an optimal strategy for the same via a mathematical model representing the operation cost of a BSS consisting of three components, namely, cost of battery utilization, damage cost associated with different charging methods, and dynamic electricity cost. The solution to the multiobjective optimization problem gave the optimum number of batteries that should be used from the battery stock and the charging decision for incoming discharged batteries, given the possible charging options and the constraints on demand satisfaction. Finally, the results from two different optimization tools, Solver in MS Excel and Lingo software, were compared.

Charging behavior modeling of battery electric vehicle drivers on long-distance trips

Using the data from an interactive stated choice experiment, we analyzed battery electric vehicle (BEV) users’ fast-charging choices on long-distance trips using both static and dynamic discrete choices models (SDCMs and DDCMs). The results show that battery state of charge (SOC) and the ability to reach the next station without deviating from the original plan are the primary factors influencing charging decisions. Charging cost, time, the detour time to reach a station, and the amenities are statistically significant predictors, but less important than SOC and the ability to complete the trip as planned. The comparison of the SDCMs and DDCMs shows that SDCMs have better goodness-of-fit than more complicated DDCMs. By comparing the relative size of the coefficient, we estimated the monetary value of increasing charging power, moving the charging stations closer to highway exits, and having amenities such as restrooms, restaurants, and Wi-Fi at the charging stations.