Charging Policy Research Articles

Simulation-based quantitative methods for vehicle emissions and a CO2 charging policy

With the worsening vehicle emissions, carbon emission charges are becoming an increasingly popular policy to reduce emissions. This paper proposes a policy to charge for the additional CO2 emissions due to the increasing traffic flow for each vehicle, thereby extending traditional travel time-based traffic assignment to emissions-charged traffic assignment. Considering that vehicle movements in the network are affected by signal timings and the car-following together with lanechanging interactions of different types of vehicles, microscopic traffic flow simulation is combined with a CO2 emission model to formulate the relations between link flow and emissions of different types of vehicles. Accordingly, the additional CO2 emissions due to increasing traffic flow are quantified and charged for each vehicle, leading to multi-vehicle-type and multi-criteria traffic assignment. Through an example network, the proposed flow-emissions model is verified and the impacts of different CO2 charging prices in the morning peak hour are investigated. Analysis of the results shows monotone increasing relations between price and reduced total emissions as well as increased revenue within the price range. In addition, the charging policy also leads to traffic assignment that achieves a system-optimal assignment since the CO2 pricing aligns with the general concept of pricing externality, thereby reducing the total travel time.

Predicting future capacity of lithium-ion batteries using transfer learning method

Lithium-ion (Li-ion) batteries have numerous applications, such as electric vehicles, power tools, medical devices, drones, satellites, and utility-scale storage. Remaining useful life (RUL) prediction is a challenging task due to the highly nonlinear degradation behavior and prediction of batteries dealing with unknown future data. Online RUL prediction for Li-ion batteries plays an important role in proper battery health management. To improve the prediction accuracy of RUL, we propose a novel hybrid method based on transfer learning to predict the future capacity of Li-ion batteries. The proposed method consists of integrating an ensemble empirical mode decomposition algorithm, a support vector regression model, and a bidirectional long short-term memory with attention mechanism model to predict the state of health (SOH), where SOH is the battery cycle life and discharge capacity measured in number of cycles. In addition, we also consider keen-onset effects. The performance of RUL predictions using different starting RUL prediction points is compared with experiments. The proposed method shows that the larger the cycle number of the RUL prediction starting point, the more accurate the RUL prediction results. The relative error values for the three different charging policy target batteries using the proposed method are 6.96 %, 0.6 %, and 6.25 %, respectively. The proposed method is effective for predicting the future capacity of Li-ion batteries and can be applied to predictive maintenance.

A Novel Fine-Tuning Model Based on Transfer Learning for Future Capacity Prediction of Lithium-Ion Batteries

Future capacity prediction of lithium-ion batteries is a highly researched topic in the field of battery management systems, owing to the gradual degradation of battery capacity over time due to various factors such as chemical changes within the battery, usage patterns, and operating conditions. The accurate prediction of battery capacity can aid in optimizing its usage, extending its lifespan, and mitigating the risk of unforeseen failures. In this paper, we proposed a novel fine-tuning model based on a deep learning model with a transfer learning approach comprising of two key components: offline training and online prediction. Model weights and prediction parameters were transferred from offline training using source data to the online prediction stage. The transferred Bi-directional Long Short-Term Memory with an Attention Mechanism model weights and prediction parameters were utilized to fine-tune the model by partial target data in the online prediction phase. Three battery batches with different charging policy were used to evaluate the proposed approach’s robustness, reliability, usability, and accuracy for the three charging policy batteries’ real-world data. The experiment results show that the proposed method’s efficacy improved, with an increase in the cycle number of the starting point, exhibiting a linear relationship with the starting point. The proposed method yields relative error values of 8.70%, 6.38%, 9.52%, 7.58%, 1.94%, and 2.29%, respectively, for the six target batteries in online prediction. Thus, the proposed method is effective in predicting the future capacity of lithium-ion batteries and holds potential for use in predictive maintenance applications.

SEM-based Analysis of Factors Influencing the Purchase Intention of Electric Vehicle Consumers

Consumers' purchase intention plays an important role in the development of the electric vehicle industry. Based on the SOR model, this paper investigates the factors influencing the purchase intention of electric vehicle consumers in four dimensions: product attributes, environmental perceptions, charging policies, and subjective norms. The results show that: product attributes and environmental perceptions may influence consumers' perceived risk, and improving product performance and reducing environmental pollution in the production process will increase consumers' willingness to purchase; charging policy and subjective regulation have significant effects on consumers' willingness to purchase through consumers' perceived value and perceived risk, and improving charging policy will help improve consumers' willingness to purchase. Improving the charging policy helps to enhance consumers' purchase intention; consumers pay more attention to the perceived value of electric vehicles than to the perceived risk. Accordingly, this paper proposes that car companies should focus on the performance of electric vehicles, strictly control the production process, develop marketing strategies, and improve the charging policy jointly with relevant departments to enhance consumers' purchase intention.

Implications of de-carbonization policies using an innovative urban transport simulator

Urban transportation is responsible for most GHG and half of NOX emissions, causing health morbidity. New innovative tools are required to accurately calculate emissions and, at the same time, analyze the far-reaching impacts of urban transportation emissions in large-scale areas. To address this gap, we combined SimMobility's agent-based demand simulator with Aimsun-Next's dynamic traffic assignment model and developed a new mesoscopic emission model. In this study, we demonstrate the use of our improved simulation framework by investigating the effects of carbon-related transportation policies in the Tel-Aviv metropolitan area. We examined the change in demand, fuel consumption, various emissions levels, as well as analyzing environmental equity impacts. Our results show that limiting car ownership at the household level is more effective in restraining emissions than the examined geographical congestion charging policy. While reducing car ownership, carbon dioxide emissions and child exposure to PM2.5 are reduced by 11% and 19%, respectively.

Optimizing Battery Charging Using Neural Networks in the Presence of Unknown States and Parameters.

This work investigates the effectiveness of deep neural networks within the realm of battery charging. This is done by introducing an innovative control methodology that not only ensures safety and optimizes the charging current, but also substantially reduces the computational complexity with respect to traditional model-based approaches. In addition to their high computational costs, model-based approaches are also hindered by their need to accurately know the model parameters and the internal states of the battery, which are typically unmeasurable in a realistic scenario. In this regard, the deep learning-based methodology described in this work was been applied for the first time to the best of the authors' knowledge, to scenarios where the battery's internal states cannot be measured and an estimate of the battery's parameters is unavailable. The reported results from the statistical validation of such a methodology underline the efficacy of this approach in approximating the optimal charging policy.

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.

Immigrants’ Enforcement Experiences and Concern about Accessing Public Benefits or Services

Although exclusionary immigration policies are associated with fear of deportation and avoidance of public benefits, relationships between immigration enforcement policy and public charge policies are largely unknown. Using a California population-based survey of 1103 Asian and Latinx immigrants in 2018, we tested the relationship between immigrants’ experiences with law enforcement and their concern about public charge. Direct encounters with various forms of law enforcement, including being asked to show proof of citizenship by law enforcement, staying inside to avoid police or immigration officials, and having known someone who had been deported, were associated with immigrants’ avoidance of public benefits due to public charge concerns. Latinx immigrants were more likely to be concerns about public charge than Asians. Intersections among immigration policies deserve further consideration. There is a need to provide accurate and reliable information to immigrant communities about public benefits and advocate for inclusive immigration policies.

Dispatching a fleet of electric towing vehicles for aircraft taxiing with conflict avoidance and efficient battery charging

Following the Paris Accords, the aviation industry aims to become climate neutral by 2050. In this line, electric vehicles that tow aircraft during taxiing are a promising emerging technology to reduce emissions at airports. This paper proposes an end-to-end optimization framework for electric towing vehicles (ETVs) dispatchment at large airports. We integrate the routing of the ETVs in the taxiway system where minimum separation distances are ensured at all times, with the assignment of these ETVs to aircraft towing tasks and scheduling ETV battery recharging. For ETV recharging, we consider a preemptive charging policy where the charging times depend on the residual state-of-charge of the battery. We illustrate our model for one day of operations at a large European airport. The results show that the 913 arriving and departing flights can be towed with 38 ETVs, with battery charging distributed throughout the day. The fleet size is shown to increase approximately linear with the number of flights in the schedule. We also propose a greedy dispatchment of the ETVs, which is shown to achieve an optimality gap of 6% with respect to the number of required vehicles and with 22% with respect to the maximum delay during towing. We also show that both algorithms can be leveraged to account for flight delays using a rolling horizon approach, and that over 95% of the flights can be reallocated if delays occur. Overall, we propose a roadmap for ETV management at large airports, considering realistic ETV specifications (battery capabilities, kinematic properties) and requirements for aircraft collision avoidance during towing.

Effects of congestion charging and subsidy policy on vehicle flow and revenue with user heterogeneity.

Traffic congestion is a major issue in urban traffic networks. Both congestion charging and subsidy policy can solve traffic congestion to some extent, but which one is better? Based on this, this paper constructs a typical transit network consisting of three travel tools in four common travel modes. Travelers' values of time affect their choice of transportation in the congestion network, thus a stochastic user equilibrium model is established by considering travelers' heterogenous values of time to evaluate the effects of different combinations of congestion charging and subsidy policies on vehicle flow and revenue. Numerical results indicate that the effectiveness of congestion charging and subsidy policy in alleviating traffic congestion depends on the object of charging or subsidizing. Congestion charging for private cars can reduce traffic flow and alleviate traffic congestion, but charging for ridesharing cars does not reduce traffic flow and may even cause traffic congestion. Subsidizing public buses does not reduce traffic flow, but it can ease congestion by coordinating traffic flow on both edges of the dual-modal transport. The combination of no subsidy for public buses and charging for both private cars and ridesharing cars can obtain the greatest revenue, but it does not alleviate traffic congestion. Although the combination of charging for private cars and subsidizing public buses does not bring the most benefits, it can reduce traffic flow, and its revenue is also considerable. This study can provide quantitative decision support for the government to ease traffic congestion and improve government revenue.

Policies on tax, fees, charges, and land use payments and some recommendations

This study was conducted with the primary objective of compiling, analyzing, and commenting on tax policies, fees, and charges related to land. Particularly, these policies will be considered in the light of two important Resolutions: Resolution 19-NQ/TW dated 31/10/2012, and Resolution 18-NQ/TW dated 16/6/2022. These resolutions have set out some basic guidelines for the management and taxation related to land in Vietnam. The study reveals that despite differences in culture, politics, and economy, many countries have effectively implemented tax policies, fees, and charges related to land. These policies often aim to create a significant source of tax revenue, ensure fairness and efficiency in land use, and contribute to sustainable development. To make appropriate policy recommendations, this study scrutinizes current land-related tax, fee, and charge policies in Vietnam, and the status and trends of the real estate and land markets. Data was collected and analyzed from various sources, including official reports, research documents, and reputable online sources. The authors propose several policy recommendations for improving policies related to taxes, fees, and charges on land in the future. These recommendations not only aim to enhance efficiency in tax collection but also focus on providing a reasonable and transparent environment for citizens and businesses while ensuring sustainable and harmonious social development.

A socioeconomic analysis for a green traffic restrictions scheme in Athens

The objective of this paper is the appraise of the socioeconomic impacts until 2035 of a proposed annual charging policy called Green Car Access Card (GCAC) for the daily access of a passenger car in the city center of Athens, Greece with the charging being adjusted according to the Euro class. A questionnaire survey was conducted using stated preference while a binary logistic model was developed to determine the acceptability of GCAC. A socioeconomic analysis was developed to quantify the impact on traffic, road safety, and air pollution until 2035 due to GCAC implementation. For the “Do Nothing” and the “GCAC” Scenarios the road casualties, the travel time, the fuel consumption and the emissions from motorized transport are estimated and expressed in monetary units. Results indicate a positive NPV which proves that the proposed investment provides a significant benefit to society.

“Drowning in bureaucracy”: The unintended consequences of NHS charging policies for the provision of care to migrant populations

“Drowning in bureaucracy”: The unintended consequences of NHS charging policies for the provision of care to migrant populations

Cooperative Learning for Smart Charging of Shared Autonomous Vehicle Fleets

We study the operational problem of shared autonomous electric vehicles that cooperate in providing on-demand mobility services while maximizing fleet profit and service quality. Therefore, we model the fleet operator and vehicles as interactive agents enriched with advanced decision-making aids. Our focus is on learning smart charging policies (when and where to charge vehicles) in anticipation of uncertain future demands to accommodate long charging times, restricted charging infrastructure, and time-varying electricity prices. We propose a distributed approach and formulate the problem as a semi-Markov decision process to capture its stochastic and dynamic nature. We use cooperative multiagent reinforcement learning with reshaped reward functions. The effectiveness and scalability of the proposed model are upgraded through deep learning. A mean-field approximation deals with environment instabilities, and hierarchical learning distinguishes high-level and low-level decisions. We evaluate our model using various numerical examples based on real data from ShareNow in Berlin, Germany. We show that the policies learned using our decentralized and dynamic approach outperform central static charging strategies. Finally, we conduct a sensitivity analysis for different fleet characteristics to demonstrate the proposed model’s robustness and provide managerial insights into the impacts of strategic decisions on fleet performance and derived charging policies. Supplemental Material: The online appendix is available at https://doi.org/10.1287/trsc.2022.1187 .

Blocking Charges 2.0

For decades, the NLRB's blocking charge policy was viewed by unions as a magical pause button on events that might lead to a majority of “no” voters in a decertification election. Unions that saw their support among workers decline leading up to election day could file an unfair labor practice charge with the NLRB that, many times, would “block” the processing of the petition until the charge was fully investigated and ultimately resolved. The delay, which often spanned months, provided unions with valuable time to shore up their support and reverse momentum that they perceived might be leading to their defeat.

“Drowning in bureaucracy”: The unintended consequences of NHS charging policies for the provision of care to migrant populations

“Drowning in bureaucracy”: The unintended consequences of NHS charging policies for the provision of care to migrant populations

A constrained DRL-based bi-level coordinated method for large-scale EVs charging

With the vigorous development of battery electric vehicles (BEVs), BEVs’ charging scheduling is essential for better economy and safety. In this paper, we aim to minimize the electricity purchasing cost considering a large number of BEVs and distributed energy. This problem is challenging to get the optimal charging policy due to a large number of uncertainties and dimension disasters caused by a large scale of BEVs and renewable energy. To meet these challenges, we propose an improved bi-level schedule framework, which decomposes the primal problem into two sub-problems to reduce the computational complexity and designs a communication mechanism to ensure the consistency of optimality between different levels. Then the problem is modeled as constrained multi-level Markov decision processes (CMMDP). In the upper level, a constrained deep reinforcement learning method (CDRL) is proposed to get the total charging or discharging energy of BEV groups. An action constraint module is constructed to ensure the feasibility of chosen actions and a novel reward shaping function is designed to optimize action selection. In the lower level, an optimal descending order charging policy (DOCP) is taken to fast decide the charging or discharging behavior for each BEV based on the upper level’s decision. Numerical experiments show that our method has obvious superiority in training efficiency and solution accuracy compared with state of art DRL methods, and reduces the cost by 12% to 28% compared with an experience charging policy.

Dynamic evaluation of environmental-economic performance of vehicle emission reduction policy from the perspective of the loss-aversion effect

With increasing intensification of vehicle pollutants and CO2 emissions, it has become difficult to realize the co-control of pollution reduction and carbon reduction. Considering the limitations of the single-congestion charging policy, this study first analyzed the dual effect of charging on vehicle travel using the loss-aversion effect and constructed a decision algorithm of vehicle trips based on this effect. In the second step, the strategy of public publicity and public transport priority policy (PTPP) were introduced to establish a vehicle energy conservation and emission reduction (ECER) management model based on the mode of “charging + public publicity + public transport priority” (CPP). Finally, the following conclusions were drawn based on a dynamic simulation: (1) The single-charging mode can induce side effects, leading to a rebound in the number of vehicle trips. (2) Increasing the investment in publicity funds and expanding the scope of publicity forms can significantly raise the public's awareness of environmental protection and have a restraining effect on vehicle trips. (3) Compared with the single-charging policy, the CPP mode has multiple performance levels for the environment and economy. Moreover, the CO, CO2 emissions, and economic costs decreased by 45.67%, 31.93%, and 45%, respectively.

Performance evaluation and optimization of design parameters for electric vehicle-sharing platforms by considering vehicle dynamics

Global adoption of electric vehicles (EVs) faces many challenges such as range anxiety, high cost of EVs, and inadequate charging infrastructure. EV-sharing platforms resolve such concerns by setting up an optimal configuration for charging infrastructure and optimizing the charging decisions for depleted EVs. These platforms manage the vehicles’ flow to different charging stations and decide when and to what energy level the depleted vehicles should be recharged. Station-based platforms are one of the mainstream vehicle sharing systems where the customer picks-up and drops-off the vehicle at the designated stations. If a vehicle’s battery energy level falls below a threshold after completing the customer trip, it is charged either partially or fully at the charging station. This study addresses various operational and strategic decisions (such as the number of chargers, vehicle repositioning, and partial charging policy) for a one-way station-based EV-sharing platform using a stylized three-stage analytical framework. We use vehicle dynamics to model the EV powertrain and regenerative braking under different traffic conditions and simulate them using AVL CRUISE™. We model the platform operations using an open queuing network and provide a mixed-integer non-linear optimization program using inputs from the queuing network and vehicle dynamics simulation. We also provide a bound-based heuristic to solve this NP-hard optimization problem. We generate various managerial insights for an efficient implementation of the partial charging policy for EV-sharing platforms. The increase in the partial charging probability (the fraction of depleted vehicles charged partially) reduces the effective charging demand, resulting in fewer chargers and a higher profit. On the other hand, if we increase the target battery energy level for partial charging, the platform’s profit decreases due to higher effective charging demand dominating the benefits of lower charging frequency of vehicles.

Distributed Joint Channel Assignment and Power Control for Sum Rate Maximization of D2D-Enabled Massive MIMO System

Device-to-device (D2D) communications underlaid massive multiple-input multiple-output (MIMO) systems have been recognized as a promising candidate technology to achieve the challenging fifth-generation (5G) network requirements. This integration enhances network throughput, improves spectral efficiency, and offloads the traffic load of base stations. However, the co/cross-tier interferences between cellular and D2D communications caused by resource sharing is a significant challenge, especially when dense D2D users exist in an underlay mode. In this paper, we jointly optimize the channel assignment and power allocation to maximize the sum data rate while maintaining the interference constraints of cellular links. Due to the lack of network-wide information in large scale networks, resource management and interference coordination is hard to be implemented in a centralized way. Therefore, we propose a three-stage stable and distributed resource allocation and interference management scheme based on local information and requires little coordination and communication between devices. We model the channel allocation optimization problem in the first stage as a many-to-one matching game. In the second stage, the algorithm adopts a cost charging policy to solve each user’s power control problem as a non-cooperative game. In the third stage, the algorithm search for swap blocking pairs until stable matching exist. It is shown in this paper that the proposed algorithm converges to a stable matching and terminates after finite iterations. Simulation results show that the proposed algorithm can achieve more than 86% of the average transmission rate performance of the optimal matching with lower complexity.