Impact of electric vehicle charging demand on power distribution grid congestion.

California, a pioneer in EV adoption, has enacted ambitious electric vehicle (EV) policies that will generate a large burden on the state's electric distribution system. We investigate the statewide impact of uncontrolled EV charging on the electric distribution networks at a large scale and high granularity, by employing an EV charging profile projection that combines travel demand model, EV adoption model, and real-world EV charging data. We find a substantial need for infrastructure upgrades in 50% of feeders by 2035, and 67% of feeders by 2045. The distribution system across California must upgrade its capacity by 25 GW by 2045, corresponding to a cost between $6 and $20 billion. While the additional infrastructure cost drives the electricity price up, it is offset by the downward pressure from the growth of total electricity consumption and leads to a reduction in electricity rate between $0.01 and $0.06/kWh by 2045. We also find that overloading conditions are highly diverse spatially, with feeders in residential areas requiring twice as much upgrade compared to commercial areas. Our study provides a framework for evaluating EVs' impact on the distribution grid and indicates the potential to reduce infrastructure upgrade costs by shifting home-charging demand. The imminent challenges confronting California serve as a microcosm of the forthcoming obstacles anticipated worldwide due to the prevailing global trend of EV adoption.

The effects of local interventions on global technological change through spillovers: A modeling framework and application to the road-freight sector.

To address global sustainability challenges, (public) policy interventions are needed to induce or accelerate technological change. While most policy interventions occur on the local level, their innovation effects can spill over to other jurisdictions, potentially having global impact. These spillovers can increase or reduce the incentive for interventions. Lacking to date are computational models that capture these spillover dynamics. Here, we devise a conceptual and methodological approach to quantify ex ante the effects of local demand-side interventions on global competition between incumbent and novel technologies. We introduce two factors that moderate global spillovers-relative size of selection environments and relative innovation potential of competing technologies. Our approach incorporates both factors in a techno-economic discrete choice model that evaluates technology competition over time through endogenized technological learning. We apply this modeling framework to the case of road freight. Different demand-pull interventions and shocks are modeled to assess spillover effects. In the case of road freight, electric vehicles experience growth in most application segments but can still be accelerated substantially through public policy intervention-spillovers occur if strong public interventions are introduced in large regions or in multiple combined regions under club policy interventions. These findings are discussed in the context of club policy interventions and a modeled geopolitical shock in China. A full sensitivity analysis of model input parameters and intervention or shock dynamics reveals high model robustness. Finally, we discuss the implications of the road-freight case study as it might inform the progress of other niche technologies in transitioning sectors.

Technology advancement is driving electric vehicle adoption.

Electric vehicle sales have been growing rapidly in the United States and around the world. This study explores the drivers of demand for electric vehicles, examining whether this trend is primarily a result of technology improvements or changes in consumer preferences for the technology over time. We conduct a discrete choice experiment of new vehicle consumers in the United States, weighted to be representative of the population. Results suggest that improved technology has been the stronger force. Estimates of consumer willingness to pay for vehicle attributes show that when consumers compare a gasoline vehicle to its battery electric vehicle (BEV) counterpart, the improved operating cost, acceleration, and fast-charging capabilities of today's BEVs mostly or entirely compensate for their perceived disadvantages, particularly for longer-range BEVs. Moreover, forecasted improvements of BEV range and price suggest that consumer valuation of many BEVs is expected to equal or exceed their gasoline counterparts by 2030. A suggestive market-wide simulation extrapolation indicates that if every gasoline vehicle had a BEV option in 2030, the majority of new car and near-majority of new sport-utility vehicle choice shares could be electric in that year due to projected technology improvements alone.

How policies and actor strategies affect electric vehicle diffusion and wider sustainability transitions.

Implementing electromobility is a central component in the de-carbonization of personal mobility. In recent years, the absolute number of electric vehicles (EVs) and their market share has increased sharply in many countries. This paper focuses on Norway, a pioneer market for EVs that other countries can learn from. The analysis highlights how a combination of local and national policies over a 30-y period, which targeted both industry development and vehicle demand, were important drivers of this development. It also highlights the importance of advocacy groups and strong networks in promoting EVs, as well as changes in user preferences. The paper demonstrates how the EV diffusion has been driven by alignments of multiple processes across different levels, involving interactions between multiple actors and social groups with different interests and views about desirable futures as described by the multi-level perspective (MLP). Building on the MLP, the study of EV diffusion in Norway illustrates how niches are often sustained through demonstrations, experimentation, strategic alliances, and actors securing favorable political and economic conditions. Further, it shows how local or national niches may depend on international regime actors, such as the car manufacturing industry and policies developed abroad. The paper also explores how the introduction of EVs has opened for wider effects, including innovation within production-consumption systems beyond mobility. Based on this analysis, we argue for a nuanced perspective on the relationship between incremental, regime-internal innovation, and wider transformative changes, where the merits of societal learning and experience with battery electricity for transportation are highlighted.

Large Decreases in Tailpipe Criteria Pollutant Emissions from the U.S. Light-Duty Vehicle Fleet Expected in 2020-2040.

The U.S. EPA MOVES3 model was used to assess the impact of the large-scale introduction of electric vehicles on emissions of criteria pollutants (CO, hydrocarbons [HC], NOx, and particulate matter [PM]) and CO2 from the U.S. light-duty vehicle fleet. Large reductions in emissions of these criteria pollutants occurred in 2000-2020. These trends are expected to continue through 2040 driven by turnover of the conventional fleet with old vehicles being replaced by battery electric vehicles (BEVs) and by new internal combustion engine vehicles (ICEVs) with modern emission control systems. Without the introduction of BEVs, the absolute emissions of CO, NOx, HC, and PM2.5 from the U.S. light-duty vehicle fleet are expected to decrease by approximately 61, 88, 55, and 20% from 2020 to 2040. Introduction of BEVs with market share increasing linearly to 100% in 2040 provides additional benefits, which, combined with ICEV fleet turnover, would lead to decreases of absolute emissions of CO, NOx, HC, and PM2.5 of approximately 77, 94, 71, and 37% from 2020 to 2040. Reductions in CO2 emissions follow a similar pattern. Large decreases in criteria pollutant and CO2 emissions from light duty vehicles lie ahead.

Cleaning up while Changing Gears: The Role of Battery Design, Fossil Fuel Power Plants, and Vehicle Policy for Reducing Emissions in the Transition to Electric Vehicles.

Plug-in electric vehicles (PEVs) can reduce air emissions when charged with clean power, but prior work estimated that in 2010, PEVs produced 2 to 3 times the consequential air emission externalities of gasoline vehicles in PJM (the largest US regional transmission operator, serving 65 million people) due largely to increased generation from coal-fired power plants to charge the vehicles. We investigate how this situation has changed since 2010, where we are now, and what the largest levers are for reducing PEV consequential life cycle emission externalities in the near future. We estimate that PEV emission externalities have dropped by 17% to 18% in PJM as natural gas replaced coal, but they will remain comparable to gasoline vehicle externalities in base case trajectories through at least 2035. Increased wind and solar power capacity is critical to achieving deep decarbonization in the long run, but through 2035 we estimate that it will primarily shift which fossil generators operate on the margin at times when PEVs charge and can even increase consequential PEV charging emissions in the near term. We find that the largest levers for reducing PEV emissions over the next decade are (1) shifting away from nickel-based batteries to lithium iron phosphate, (2) reducing emissions from fossil generators, and (3) revising vehicle fleet emission standards. While our numerical estimates are regionally specific, key findings apply to most power systems today, in which renewable generators typically produce as much output as possible, regardless of the load, while dispatchable fossil fuel generators respond to the changes in load.

Electric vehicles and health: A scoping review.

BACKGROUND: The health impacts of the rapid transition to the use of electric vehicles are largely unexplored. We completed a scoping review to assess the state of the evidence on use of battery electric and hybrid electric vehicles and health. METHODS: We conducted a literature search of MEDLINE, Embase, Global Health, CINAHL, Scopus, and Environmental Science Collection databases for articles published January 1990 to January 2024. We included articles if they presented observed or modeled data on the association between battery electric or hybrid electric cars, trucks, or buses and health-related outcomes. We abstracted data and summarized results. RESULTS: Out of 897 reviewed articles, 52 met our inclusion criteria. The majority of included articles examined transitions to the use of electric vehicles (n = 49, 94%), with fewer studies examining hybrid electric vehicles (n = 11, 21%) or plug-in hybrid electric vehicles (n = 8, 15%). The most common outcomes examined were premature death (n = 41, 79%) and monetized health outcomes such as medical expenditures (n = 33, 63%). We identified only one observational study on the impact of electric vehicles on health; all other studies reported modeled data. Almost every study (n = 51, 98%) reported some evidence of a positive health impact of transitioning to electric or hybrid electric vehicles, although magnitudes of association varied. There was a paucity of information on the environmental justice implications of vehicle transitions. CONCLUSIONS: The results of the current literature on electric vehicles and health suggest an overall positive health impact of transitioning to electric vehicles. Additional observational studies would help expand our understanding of the real-world health effects of electric vehicles. Future research focused on the environmental justice implications of vehicle fleet transitions could provide additional information about the extent to which the health benefits occur equitably across populations.

Review of Integrated Chassis Control Techniques for Automated Ground Vehicles.

Integrated chassis control systems represent a significant advancement in the dynamics of ground vehicles, aimed at enhancing overall performance, comfort, handling, and stability. As vehicles transition from internal combustion to electric platforms, integrated chassis control systems have evolved to meet the demands of electrification and automation. This paper analyses the overall control structure of automated vehicles with integrated chassis control systems. Integration of longitudinal, lateral, and vertical systems presents complexities due to the overlapping control regions of various subsystems. The presented methodology includes a comprehensive examination of state-of-the-art technologies, focusing on algorithms to manage control actions and prevent interference between subsystems. The results underscore the importance of control allocation to exploit the additional degrees of freedom offered by over-actuated systems. This paper systematically overviews the various control methods applied in integrated chassis control and path tracking. This includes a detailed examination of perception and decision-making, parameter estimation techniques, reference generation strategies, and the hierarchy of controllers, encompassing high-level, middle-level, and low-level control components. By offering this systematic overview, this paper aims to facilitate a deeper understanding of the diverse control methods employed in automated driving with integrated chassis control, providing insights into their applications, strengths, and limitations.

Electric vehicles' choice behaviour: An emerging market scenario.

Worldwide, the adoption of electric automobiles is gaining momentum, owing to a steady rise in customers' sustainability consciousness. So far, electric vehicle-related studies have investigated consumer motives, attitudes, and intentions toward adoption. However, empirical research on enablers and inhibitors of electric vehicle choice behaviour has not been fully explored, particularly in an emerging market context, (e.g., India). The present study employed a judicious mix of three notable theoretical lenses of dual-factor theory, innovation resistance theory, and the stimulus-organism-response model to empirically scrutinize electric vehicle adoption enablers and inhibitors by analysing data collected from 391 young Indian sustainability-oriented electric vehicle users. The sample was gathered via the purposive sampling method, and the data was analysed employing structural equation and PROCESS macro modelling. The research posits that consumer sustainability consciousness (CSC) is a stimulus with a positive influence on enablers (e.g., personal motives, social motives, and incentive policy) as well as inhibitors (e.g., usage, value, and risk barriers). Additionally, product involvement and perceived marketplace influence significantly moderate the relationship between choice behaviour and facilitators and inhibitors. The research offers a few useful strategic decision-making insights for electric vehicle manufacturers, green marketers, and policymakers from emerging markets.

Should Transportation Be Transitioned to Ethanol with Carbon Capture and Pipelines or Electricity? A Case Study.

An important issue today is whether gasoline vehicles should be replaced by flex-fuel vehicles (FFVs) that use ethanol-gasoline blends (e.g., E85), where some carbon dioxide (CO2) from ethanol's production is captured and piped, or battery-electric vehicles (BEVs) powered by wind or solar. This paper compares the options in a case study. It evaluates a proposal to capture fermentation CO2 from 34 ethanol refineries in 5 U.S. states and build an elaborate pipeline to transport the CO2 to an underground storage site. This "ethanol plan" is compared with building wind farms at the same cost to provide electricity for BEVs ("wind plan A"). Compared with the ethanol plan, wind plan A may reduce 2.4-4 times the CO2, save drivers in the five states $40-$66 billion (USD 2023) over 30 years even when BEVs initially cost $21,700 more than FFVs, require 1/400,000th the land footprint and 1/10th-1/20th the spacing area, and decrease air pollution. Even building wind to replace coal ("wind plan B") may avoid 1.5-2.5 times the CO2 as the ethanol plan. Thus, ethanol with carbon capture appears to be an opportunity cost that may damage climate and air quality, occupy land, and saddle consumers with high fuel costs for decades.

Policy Analysis of CO2 Capture and Sequestration with Anaerobic Digestion for Transportation Fuel Production.

Low carbon fuel and waste management policies at the federal and state levels have catalyzed the construction of California's wet anaerobic digestion (AD) facilities. Wet ADs can digest food waste and dairy manure to produce compressed natural gas (CNG) for natural gas vehicles or electricity for electric vehicles (EVs). Carbon capture and sequestration (CCS) of CO2 generated from AD reduces the fuel carbon intensity by carbon removal in addition to avoided methane emissions. Using a combined lifecycle and techno-economic analysis, we determine the most cost-effective design under current and forthcoming federal and state low carbon fuel policies. Under many scenarios, designs that convert biogas to electricity for EVs (Biogas to EV) are favored; however, CCS is only cost-effective in these systems with policy incentives that exceed $200/tonne of CO2 captured. Adding CCS to CNG-producing systems (Biogas to CNG) only requires a single unit operation to prepare the CO2 for sequestration, with a sequestration cost of $34/tonne. When maximizing negative emissions is the goal, incentives are needed to either (1) fund CCS with Biogas to EV designs or (2) favor CNG over electricity production from wet AD facilities.

Assessing Short-Term Supply Disruption Impacts within Life Cycle Sustainability Assessment─A Case Study of Electric Vehicles.

In this article, the recently published SPOTTER approach, which allows for identifying potential supply disruption impacts along the entire supply chain within life cycle sustainability assessment in the short term (i.e., < 5 years), is applied to a case study addressing the cobalt and aluminum supply chains of electric vehicles (EVs) used in Switzerland. Existing studies within the field assessing supply disruption impacts for EVs and other technologies focus on impacts related to raw material supply and thus neglect impacts along full supply chains. The present study identifies hotspots and overall impacts along the full supply chains by analyzing six supply disruption events (i.e., geopolitical instability, child labor restrictions, trade barriers, price volatility, limited recyclability, and economic resource depletion) for two impact categories (i.e., cost variability and limited availability). Identified hotspots suggest that supply chains are potentially disrupted mainly through events occurring in Asian, African, or other developing countries and affecting the Western economies. The highest risks are indicated in relation to the supply of EVs, EV wiring, traction batteries, cobalt powder, and cobalt ore. Suitable measures to mitigate these supply risks are suggested showing that some of the suggestions could not have been made based on the results of existing studies.

A Measurement Method of Power Transferred to an Electric Vehicle Using Wireless Charging.

The increasing number of zero-emission vehicles on the roads demands novel vehicle charging solutions that ensure convenience, safety, increased charging infrastructure availability, and aesthetics. Wireless charging technology is seen as the one that could assure these desirable properties and could be applied not just in conventional implementations but also in off-grid solutions together with roadway energy harvesting systems. Both approaches require proper transfer of energy metering methods. In this paper, a method for measuring the power transferred to the load in a wireless charging system is presented, and its systematic error is assessed in the relevant range of influencing factors. The novelty of the method is that it does not require any metrologically certified measurement instrumentation on the receiver side of the wireless charging system. The error analysis is performed using a numerical simulation. Considered error-influencing factors included secondary side electrical load, coils' coupling coefficient and quality factor, current and voltage quantization resolution, and compensation topology type (serial-serial (SS) and serial-parallel (SP)). It was determined that the systematic error of the power assessment does not exceed 0.7% for SS and 1.1% for SP topologies when the coupling coefficient is in the range of 0.05 to 0.4 and the quality factor of the resonant system is in the range of 100 to 800.

Multi-Objective Message Routing in Electric and Flying Vehicles Using a Genetics Algorithm.

With progressive technological advancements, the time for electric vehicles (EVs) and unmanned aerial vehicles (UAVs) has finally arrived for the masses. However, intelligent transportation systems need to develop appropriate protocols that enable swift predictive communication among these battery-powered devices. In this paper, we highlight the challenges in message routing in a unified paradigm of electric and flying vehicles (EnFVs). We innovate over the existing routing scheme by considering multi-objective EnFVs message routing using a novel modified genetics algorithm. The proposed scheme identifies all possible solutions, outlines the Pareto-front, and considers the optimal solution for the best route. Moreover, the reliability, data rate, and residual energy of vehicles are considered to achieve high communication gains. An exhaustive evaluation of the proposed and three existing schemes using a New York City real geographical trace shows that the proposed scheme outperforms existing solutions and achieves a 90%+ packet delivery ratio, longer connectivity time, shortest average hop distance, and efficient energy consumption.

A High-Efficiency Capacitor-Based Battery Equalizer for Electric Vehicles.

Technology in electric vehicles has increased substantially in the past decade. Moreover, it is projected to grow at record highs in the coming years since these vehicles are needed to reduce the contamination related to the transportation sector. One of the essential elements of an electric car is its battery, due to its cost. Batteries comprise parallel and series-connected cell arrangements to meet the power system requirements. Therefore, they require a cell equalizer circuit to preserve their safety and correct operation. These circuits keep a specific variable of all cells, such as the voltage, within a particular range. Within cell equalizers, capacitor-based ones are very common as they have many desirable characteristics of the ideal equalizer. In this work, an equalizer based on the switched-capacitor is proposed. A switch is added to this technology that allows the disconnection of the capacitor from the circuit. In this way, an equalization process can be achieved without excess transfers. Therefore, a more efficient and faster process can be completed. In addition, it allows another equalization variable to be used, such as the state of charge. This paper studies the operation, power design, and controller design of the converter. Moreover, the proposed equalizer was compared to other capacitor-based architectures. Finally, simulation results were presented to validate the theoretical analysis.

A Two-Stage Multi-Agent EV Charging Coordination Scheme for Maximizing Grid Performance and Customer Satisfaction.

Advancements in technology and awareness of energy conservation and environmental protection have increased the adoption rate of electric vehicles (EVs). The rapidly increasing adoption of EVs may affect grid operation adversely. However, the increased integration of EVs, if managed appropriately, can positively impact the performance of the electrical network in terms of power losses, voltage deviations and transformer overloads. This paper presents a two-stage multi-agent-based scheme for the coordinated charging scheduling of EVs. The first stage uses particle swarm optimization (PSO) at the distribution network operator (DNO) level to determine the optimal power allocation among the participating EV aggregator agents to minimize power losses and voltage deviations, whereas the second stage at the EV aggregator agents level employs a genetic algorithm (GA) to align the charging activities to achieve customers' charging satisfaction in terms of minimum charging cost and waiting time. The proposed method is implemented on the IEEE-33 bus network connected with low-voltage nodes. The coordinated charging plan is executed with the time of use (ToU) and real-time pricing (RTP) schemes, considering EVs' random arrival and departure with two penetration levels. The simulations show promising results in terms of network performance and overall customer charging satisfaction.

A Synthetic Data Generation Technique for Enhancement of Prediction Accuracy of Electric Vehicles Demand.

In terms of electric vehicles (EVs), electric kickboards are crucial elements of smart transportation networks for short-distance travel that is risk-free, economical, and environmentally friendly. Forecasting the daily demand can improve the local service provider's access to information and help them manage their short-term supply more effectively. This study developed the forecasting model using real-time data and weather information from Jeju Island, South Korea. Cluster analysis under the rental pattern of the electric kickboard is a component of the forecasting processes. We cannot achieve noticeable results at first because of the low amount of training data. We require a lot of data to produce a solid prediction result. For the sake of the subsequent experimental procedure, we created synthetic time-series data using a generative adversarial networks (GAN) approach and combined the synthetic data with the original data. The outcomes have shown how the GAN-based synthetic data generation approach has the potential to enhance prediction accuracy. We employ an ensemble model to improve prediction results that cannot be achieved using a single regressor model. It is a weighted combination of several base regression models to one meta-regressor. To anticipate the daily demand in this study, we create an ensemble model by merging three separate base machine learning algorithms, namely CatBoost, Random Forest (RF), and Extreme Gradient Boosting (XGBoost). The effectiveness of the suggested strategies was assessed using some evaluation indicators. The forecasting outcomes demonstrate that mixing synthetic data with original data improves the robustness of daily demand forecasting and outperforms other models by generating more agreeable values for suggested assessment measures. The outcomes further show that applying ensemble techniques can reasonably increase the forecasting model's accuracy for daily electric kickboard demand.

Parameter Optimization of Model Predictive Direct Motion Control for Distributed Drive Electric Vehicles Considering Efficiency and the Driving Feeling.

This paper presents a novel motion control strategy based on model predictive control (MPC) for distributed drive electric vehicles (DDEVs), aiming to simultaneously control the longitudinal and lateral motion while considering efficiency and the driving feeling. Initially, we analyze the vehicle's dynamic model, considering the vehicle body and in-wheel motors, to establish the foundation for model predictive control. Subsequently, we propose a model predictive direct motion control (MPDMC) approach that utilizes a single CPU to directly follow the driver's commands by generating voltage references with a minimum cost function. The cost function of MPDMC is constructed, incorporating factors such as the longitudinal velocity, yaw rate, lateral displacement, and efficiency. We extensively analyze the weighting parameters of the cost function and introduce an optimization algorithm based on particle swarm optimization (PSO). This algorithm takes into account the aforementioned factors as well as the driving feeling, which is evaluated using a trained long short-term memory (LSTM) neural network. The LSTM network labels the response under different weighting parameters in various working conditions, i.e., "Nor", "Eco", and "Spt". Finally, we evaluate the performance of the optimized MPDMC through simulations conducted using MATLAB and CarSim software. Four typical scenarios are considered, and the results demonstrate that the optimized MPDMC outperforms the baseline methods, achieving the best performance.

Vehicle State Joint Estimation Based on Lateral Stiffness.

In this study, a vehicle state joint estimation method based on lateral stiffness was applied to estimate the running states of electric vehicles driven by rear-drive, in-wheel motors. Different from the estimation methods used in other research, the joint estimator designed in this study uses the least-squares (LS) algorithm to estimate the lateral stiffness of the front and rear axles of the vehicle, deploying the high-degree cubature Kalman filter algorithm to estimate the vehicle state. We establish a three-degree-of-freedom nonlinear vehicle model with longitudinal velocity, lateral velocity, and yaw rate, and the lateral stiffness of the front and rear axles as the principal parameters. For the low-speed running state of the vehicle, a linearized magic tire model with high fitting accuracy was used to calculate the lateral force of the entire vehicle. The LS algorithm with a forgetting factor was used to design a lateral stiffness estimator to assess the front-axle and rear-axle lateral stiffness of the entire vehicle. The generalized high-degree cubature Kalman filter (GHCKF) algorithm was used to design the vehicle state estimator and further improve the GHCKF algorithm. A vehicle state estimator, using the square root generalized high-degree cubature Kalman filter (SRGHCKF), was designed. Therefore, the joint estimator, comprising a lateral stiffness estimator and a vehicle state estimator, adopts the LS-GHCKF/SRGHCKF algorithm and enables the estimation of the lateral stiffness, the longitudinal velocity, the lateral velocity, and the yaw rate of the entire vehicle during the driving process. A double lane change and slalom simulation were performed to analyze the feasibility and accuracy of the joint estimation algorithm and verify the results of the LS-GHCKF algorithm and the LS-SRGHCKF algorithm. Further, a low-speed driving experiment was carried out for electric vehicles driven by rear in-wheel motors. The inertial navigation system (INS), the global positioning system (GPS), the real-time kinematic (RTK), and an angle sensor were used to collect real-time vehicle data. The results were compared to verify the feasibility of the joint estimator and the progressiveness of the algorithm. The experimental verification and simulation both show that the vehicle state joint estimator, designed based on the LS-GHCKF/SRGHCKF algorithm, can accurately estimate the real-time state of the vehicle. Additionally, the LS-SRGHCKF algorithm shows better effectiveness and robustness than the LS-GHCKF algorithm.

A Novel A-ECMS Energy Management Strategy Based on Dragonfly Algorithm for Plug-in FCEVs.

The mechanical coupling of multiple powertrain components makes the energy management of 4-wheel-drive (4WD) plug-in fuel cell electric vehicles (PFCEVs) relatively complex. Optimizing energy management strategies (EMSs) for this complex system is essential, aiming at improving the vehicle economy and the adaptability of operating conditions. Accordingly, a novel adaptive equivalent consumption minimization strategy (A-ECMS) based on the dragonfly algorithm (DA) is proposed to achieve coordinated control of the powertrain components, front and rear motors, as well as the fuel cell system and the battery. To begin with, the equivalent consumption minimization strategy (ECMS) with extraordinary instantaneous optimization ability is used to distribute the vehicle demand power into the front and rear motor power, considering the different motor characteristics. Subsequently, under the proposed novel hierarchical energy management framework, the well-designed A-ECMS based on DA empowers PFCEVs with significant energy-saving advantages and adaptability to operating conditions, which are achieved by precise power distribution considering the operating characteristics of the fuel cell system and battery. These provide state-of-the-art energy-saving abilities for the multi-degree-of-freedom systems of PFCEVs. Lastly, a series of detailed evaluations are performed through simulations to validate the improved performance of A-ECMS. The corresponding results highlight the optimal control performance in the energy-saving performance of A-ECMS.