• Energy Research
• 7. Clean energy close
• World Electric Vehicle Journal close
• Transport Research close

In electrified vehicle applications, understanding the battery characteristics is of great importance as it is the state-of-art principal energy source. The key battery parameters can be identified by one of the robust and nondestructive characterization techniques, such as electrochemical impedance spectroscopy (EIS). However, relaxing the battery cell before performing the EIS method is crucial for the characterization results to be standardized. In this study, the three most common and commercially available lithium-ion technologies (NMC/graphite, LFP/graphite, NCA/LTO) are investigated at 15–45 °C temperature, in the range of 20–80% state of charge (SoC) and in fresh and aged state of health (SoH) conditions. The analysis shows that the duration of the relaxation time before impedance measurement has an impact on the battery’s nonlinear behavior. A rest time of 2 h can be proposed, irrespective of battery health condition, considering neutral technology-based impedance measurement. An impedance growth in ohmic and charge transfer characteristics was found, due to aging, and the effect of rest periods was also analyzed from an electrochemical standpoint. This experimental data was fitted to develop an empirical model, which can predict the nonlinear dynamics of lithium technologies with a 4–8% relative error for longer rest time.

With the increasing demand of driving range of new energy vehicle (NEV), design optimization for energy efficiency of traction motors became more important. However, traction motor design is complex since multiple objectives should be satisfied, such as the required torque-speed operating range and package and thermal constraints. This dramatically increases the computation time of the design optimization process, while the additional energy efficiency objective of the whole driving cycle. This paper proposes an equivalent driving cycle points extraction method, based on energy consumption equivalence to facilitate the design optimization of traction motors. This paper presents necessary rules of multiobjective optimization methods, and then gives an optimization process and proves the effectiveness.

Electric vehicles are able to provide immediate power through the vehicle-to-grid function, and they can adjust their charging power level when in the grid-to-vehicle mode. This allows them to provide ancillary services such as frequency control. Their batteries differ from conventional energy storage systems in that the owner’s energy requirement constraint must be met when the vehicles participate in a frequency control system. An optimization problem was defined by considering both the owner satisfaction and frequency control performance. The main contribution of the proposed paper, compared to the literature, are (1) to keep the total available energy stored in the batteries connected to a charging station in an optimal region that favors the frequency regulation capability of the station and the proposed QoS and (2) to consider the optimal region bounded by the efficiency thresholds of the charger to allow for maximum regulation power. The problem is expressed as a multi-criteria optimization problem with time-dependent references. The paper presents an energy management strategy for frequency control, describes a concept of an optimal time-dependent state of charge for electric vehicle charging demands, and considers the power dependence of the electric vehicle charger efficiency. Finally, the simulation results are presented via Matlab/Simulink to prove the effectiveness of the proposed algorithm.

In light of the increasing number of electric vehicles (EV), disorderly charging in mountainous cities has implications for the stability and efficient utilization of the power grid. It is a roadblock to lowering carbon emissions. EV aggregators are a bridge between EV users and the grid, a platform to achieve energy and information interoperability, and a study of the orderly charging of EVs to reach carbon emission targets. As for the objective function, the EV aggregator considers the probability of EV charging access in mountainous cities, the SOC expectation of EV users, the transformer capacity constraint, the charging start time, and other constraints to maximize revenue. Considering the access probability of charging for users in mountainous cities, the optimized Lagrange relaxation method is used to solve the objective function. The disorderly charging, centralized optimized charging, and decentralized optimized charging modes are investigated using simulation calculations. Their load profiles, economic benefits, and computational efficiency are compared in three ways. Decentralized optimal charging using the Lagrange relaxation method is shown to be 50% more effective and to converge 279% faster than centralized optimal charging.

The power coupling equation and energy consumption model for enhancing the fuel economy and power performance of plug-in hybrid electric trucks (PHETs) are proposed based on the economic velocity planning strategy (EVPS-DSIDP), which takes into account the driving style and an improved dynamic programming (IDP) algorithm. This strategy employs a fuzzy controller to identify the driving style, and optimizes the efficiency and accuracy of the conventional dynamic programming (DP) algorithm by associating decision variables, dynamically adjusting the discretization step size, and restricting the state space. Additionally, a penalty function is introduced to enhance the robustness of the DP algorithm. Under our EVPS-DSIDP, the variation of velocity is liberated from the constraints of fixed driving conditions, and directly correlates with road information and driving styles, which is of significant importance for addressing energy management issues in real-time traffic conditions. Moreover, the proposed IDP algorithm can improve computational efficiency while ensuring calculation accuracy, thereby greatly enhancing the potential for the practical application of this algorithm in real-world vehicle scenarios. The simulation results demonstrate that compared to the rule-based control strategy, the application of the proposed EVPS-DSIDP in the economy velocity planning strategy can achieve an average reduction of 2.88% in economic costs and 10.6% in travel time across different driving styles. This approach offers a more comprehensive optimization of both fuel economy and power performance.

This paper proposes Python models for a photovoltaic-based charging station for electric vehicles considering technical, economic, and environmental aspects. The proposed models consider two main cases of photovoltaic-based charging systems, which are photovoltaic/grid-charging systems and photovoltaic/grid/battery-charging systems. Moreover, additional operational options, such as exporting energy to the grid and zero-export, are added to the proposed models. The proposed techno-economic models can be used to evaluate the location of the electric vehicle charging station and the financial and environmental benefits of the electric vehicle charging station that is installed in a residential, commercial, or industrial context. The models are tested by proposing a simulation based on load demand, and then different cases, including the actual size case and additional trading cases, are investigated.

Pure electric vehicle (PEV) equipped with a dual-motor coupling drive system can make full use of the high efficiency working range of the motor in order to improve vehicle efficiency. In order to further expand the application range of the system and to improve its practical application, this paper designs and proposes a new dynamic coupling drive system of three axis-double working modes, which is based on the Simpson planetary gear train. The new system adopts two planetary gears (P1 and P2), and the two sun gears of double rows, planetary carrier of P1 and gear ring of P2 are bunded. The power output of the P1 gear ring (mode 1) and P2 planetary carrier (mode 2) is realized by a controlling wet clutch. This paper adopts the linear interpolation method, least square method and 5-fold CV cross validation method to establish the full load speed characteristics and efficiency characteristics models of two motors (13 and 30 kW). This paper proposes an optimization design method based on an improved simulated annealing (I-SA) algorithm for new system parameter matching and working mode switching strategy determination. The results show that the modeling accuracy of the two motors is high, and the mean value of MAPE is 4.337%. The proposed optimization design method achieves the demand goal of PEV effectively. The I-SA algorithm has good effectiveness and fast convergence, the mean efficiency of the optimized PEV is 83.91% under all working conditions, the maximum speed is 142.56 km/h and the power utilization rate of the dual-motor is 100%. This study proposes a new hardware system and a design optimization method on software and provides a direct reference for the research of PEV drive systems by combining hardware with software.

Electric vehicles are an important part of governments’ environmental policies, and therefore understanding the factors affecting their market share is very important. So, this research is designed to investigate the factors affecting electric vehicle adoption, considering the effects of the COVID-19 pandemic and sustainable development level. Effective factors have been investigated in three categories. One is the characteristics of electric vehicles; the other is the impact of the COVID-19 pandemic on demand for these vehicles; and finally, the impact of the level of sustainable development of countries on adopting electric vehicles. Our analysis method is based on grey econometric and grey regression methods. The results show that vehicle dimensions, battery warranty conditions, battery life, and charging facilities are effective factors in the field of vehicle characteristics that can increase the adoption of electric vehicles. Also, the analysis shows that the COVID-19 pandemic has reduced the adoption of electric vehicles. Finally, we have shown that the market share of electric vehicles is higher in countries with a higher sustainable development level because of better economic, social, and cultural infrastructures.

The dual-motor EV (Electric Vehicle) is increasingly favored by manufacturers for its excellent performance in terms of power and economy. How to further reduce its energy consumption and make full use of the dual-motor energy recovery is an important support to improve the overall vehicle economy and realize the “dual carbon” strategy. For the dual-motor EV architecture, the motor model, power battery loss model and vehicle longitudinal braking force model are established and the energy recovery-dominated regenerative braking torque distribution (RBD) rule of the dual motors is designed. Based on genetic algorithm (GA) theory and taking into account SOC, vehicle speed and braking intensity, a regenerative-braking torque optimization method is proposed that integrates energy recovery and braking stability. The braking intensity of 0.3 and the initial vehicle speed of 90 km/h are selected for verification. Compared with the rule method, the energy recovery and stability are improved by 22.8% and 4.8%, respectively, under the genetic algorithm-based and energy recovery-dominated regenerative-braking torque distribution (GA-RBD) strategy. A variety of conditions are selected for further strategy validation and the result shows that compared with the rule-based method, both energy recovery and braking stability are improved as braking speed and braking intensity increase under the GA-RBD strategy.

Governance is central for sustainability transitions in maritime transport. Norwegian authorities can directly influence developments in maritime transport through green public procurement, and low- or zero-emission technologies have been or are being considered for more than 250 operational ferry and high-speed vessel connections. This study explores practices of green public procurement and investigates procuring authorities and operators’ perspectives on the efficiency of green public procurement in accelerating sustainability transitions. Through review of calls for tender and interviews with procuring authorities and operators, the study identifies critical issues for green public procurement to accelerate maritime passenger transport toward sustainability. The critical issues are related to either procedural or service delivery issues. Procedural issues refer to timelines with multiple calls on the same time and different views of procurers and operators on the ideal duration of contracts. The most demanding service delivery issues are infrastructure for charging and energy capacity in remote areas where operators have to pre-book energy needs based on rough estimations which in turn increase the tender price.