Search Results (159)

Hybrid electric vehicles (HEVs) have become a hot topic in adaptive motive and academic fields owing to their high energy efficiency and low emissions. There are a large number of candidate schemes in the configuration design and parameter optimization stages of their powertrains that require performance evaluation. To address the problem of balancing computational burden and accuracy in traditional performance simulation algorithms for HEVs, such as dynamic programming (DP), an adaptive-efficient DP algorithm is proposed in this study, which introduces optimal SOC grid density identification and an optimal control point boundary relaxation method to simplify both state variable and control variable space simultaneously. Based on the series-parallel HEV configuration, the results show that the proposed method sacrifices fuel economy by 2.03% to 2.63% compared to DP, improves the simulation calculation speed by 232–319 times under various cycle conditions, and the engine operating points of the two are also relatively similar. In addition, the fuel economy performance of this method is very close to that of the Rapid-DP algorithm, and the calculation speed is also 2–3 times that of the latter, which effectively verifies that the proposed algorithm achieves high simulation accuracy while significantly improving computational efficiency. Full article

This study endeavors to project the trajectory of electric and hybrid vehicle adoption through 2030, operating under the premise that specific hybrid models can harness electricity from charging stations akin to fully electric counterparts. Employing the seasonal ARIMA (SARIMA) time series model, we preprocess the current counts of electric and hybrid passenger vehicles. Additionally, we use this model to forecast future counts. Our preprocessing findings suggest that Turkey currently experiences a deficit of approximately 26% in electric and hybrid vehicles, considering conventional market dynamics from 2018 to 2023. Furthermore, assuming the observed seasonal fluctuations in passenger vehicle sales will similarly influence electric and hybrid vehicle demand, a secondary preprocessing is conducted on the dataset. Applying this methodology, our projections indicate Turkey will approach a total of 2.6 million electric and hybrid vehicles by the close of 2029, offering insights for policymakers and private stakeholders in charting the course of charging infrastructure development. Full article

The transportation sector is witnessing a paradigm shift toward more sustainable and efficient propulsion systems, with a particular focus on public transportation vehicles such as buses. In this context, hybrid powertrains combining internal combustion engines with electric propulsion systems have emerged as prominent contenders due to their ability to offer significant fuel savings and CO₂ emission reductions compared to conventional diesel powertrains. In this study, the simulation of a complete hybrid bus vehicle is carried out to evaluate the impact of two different hybrid powertrain architectures compared to the diesel reference one. The selected vehicle is a 12 m city bus that performs typical urban driving routes represented by real measured driving cycles. First, the vehicle model was developed using a state-of-the-art diesel powertrain (internal combustion engine) and validated against literature data. This model facilitates a comprehensive evaluation of system efficiency, fuel consumption, and CO₂ emissions while incorporating the effects of driving cycle variability. Subsequently, two different hybrid configurations (parallel P1 and series) are implemented in the model and compared to predict the relative energy consumption and environmental impact, highlighting advantages and challenges. Full article

At present, the decarbonization of the public transport sector plays a key role in international and regional policies. Among the various energy vectors being considered for future clean bus fleets, green hydrogen and electricity are gaining significant attention thanks to their minimal carbon footprint. However, a comprehensive Life Cycle Assessment (LCA) is essential to compare the most viable solutions for public mobility, accounting for variations in weather conditions, geographic locations, and time horizons. Therefore, the present work compares the life cycle environmental impact of different powertrain configurations for urban buses. In particular, a series hybrid architecture featuring two possible hydrogen-fueled Auxiliary Power Units (APUs) is considered: an H2-Internal Combustion Engine (ICE) and a Fuel Cell (FC). Furthermore, a Battery Electric Vehicle (BEV) is considered for the same application. The global warming potential of these powertrains is assessed in comparison to both conventional and hybrid diesel over a typical urban mission profile and in a wide range of external ambient conditions. Given that cabin and battery conditioning significantly influence energy consumption, their impact varies considerably between powertrain options. A sensitivity analysis of the BEV battery size is conducted, considering the effect of battery preconditioning strategies as well. Furthermore, to evaluate the potential of hydrogen and electricity in achieving cleaner public mobility throughout Europe, this study examines the effect of different grid carbon intensities on overall emissions, based also on a seasonal variability and future projections. Finally, the present study demonstrates the strong dependence of the carbon footprint of various technologies on both current and future scenarios, identifying a range of boundary conditions suitable for each analysed powertrain option. Full article

The global rise of electric vehicles (EVs) is reshaping the automotive industry, driven by a 25% increase in EV sales in 2024 and mounting regulatory pressure from European countries aiming to phase out thermal and hybrid vehicle production. In this context, the development of advanced battery technologies has become a critical priority. However, progress in electrochemical storage systems remains limited due to persistent technological barriers such as gaps in data, inadequate modeling tools, and difficulties in system integration, such as thermal management and interface instability. Safety concerns like thermal runaway and the lack of long-term performance data also hinder large-scale adoption. This study presents an in-depth analysis of lithium–ion (Li–ion) batteries, with a particular focus on evaluating their charging and discharging behaviors. To facilitate this, a series of automated experiments was conducted using a custom-built test bench equipped with MATLAB (2024b) programming and dSPACE data acquisition cards, enabling precise current and voltage measurements. The acquired data were analyzed to derive mathematical models that capture the operational characteristics of Li–ion batteries. Furthermore, various state-of-charge (SoC) estimation techniques were investigated to enhance battery efficiency and improve range management in EVs. This paper contributes to the advancement of energy storage technologies and supports global ecological goals by proposing safer and more efficient solutions for the electric mobility sector. Full article

With the significant growth in the number of PHEVs, conducting in-depth research on their CO₂ emission characteristics is essential. This study used the Horiba OBS-ONE Portable Emission Measurement System (PEMS) to measure the CO₂ emissions of three Plug-in Hybrid Electric Vehicle (PHEV) types: one Series Hybrid Electric Vehicle (S-HEV), one Parallel Hybrid Electric Vehicle (P-HEV), and one Series-Parallel Hybrid Electric Vehicle (SP-HEV), during real driving conditions. The findings show a correlation between acceleration and increased CO₂ emissions for P-HEV, while acceleration has a relatively minor impact on S-HEV and SP-HEV emissions. Under urban driving conditions, the SP-HEV displays the lowest average CO₂ emission rate. However, under suburban and highway conditions, the average CO₂ emission rates follow the order S-HEV > SP-HEV > P-HEV. An analysis of CO₂ emission factors across different road types and vehicle-specific power (VSP) ranges indicates that within low VSP intervals (VSP ≤ 0 for urban, VSP ≤ 5 for suburban, and VSP ≤ 15 for highway roads), the P-HEV exhibits the best CO₂ emission control. As VSP increases, the P-HEV’s emission factors rise under all three road conditions, with its emission control capability weakening when VSP exceeds 5 in urban, 15 in suburban, and 20 on highway roads. For the SP-HEV, CO₂ emission factors increase with VSP in urban and suburban areas but remain stable on highways. The S-HEV shows minimal changes in emission factors with varying VSP. This research provides valuable insights into the CO₂ emission patterns of PHEVs, aiding vehicle optimization and policy development. Full article

The presence of toxic gas emissions from conventional vehicles is worrisome globally. Over the past few years, there has been a broad adoption of electric vehicles (EVs) to reduce energy usage and mitigate environmental emissions. The EVs are characterized by limited range, cost, and short range. This prompts the need for hybrid electric vehicles (HEVs). This study describes the conversion of a 2022 Volkswagen Crafter (VW) 35 TDI 340 delivery van from a conventional diesel powertrain into a hybrid electric vehicle (HEV) augmented with synchronous electrical machines (motor and generator) and a BMW i3 60 Ah battery pack. A downsized 1.5 L diesel engine and an electric motor–generator unit are integrated via a planetary power split device supported by a high-voltage lithium-ion battery. A MATLAB (R2024b) Simulink model of the hybrid system is developed, and its speed tracking PID controller is optimized using genetic algorithm (GA) and particle swarm optimization (PSO) methods. The simulation results show significant efficiency gains: for example, average fuel consumption falls from 9.952 to 7.014 L/100 km (a 29.5% saving) and CO₂ emissions drop from 260.8 to 186.0 g/km (a 74.8 g reduction), while the vehicle range on a 75 L tank grows by ~40.7% (from 785.7 to 1105.5 km). The optimized series–parallel powertrain design significantly improves urban driving economy and reduces emissions without compromising performance. Full article

Electric vehicles are gaining attention and being adopted by new users every day. Their widespread use creates a new scenario and challenge for the energy system due to the high energy storage demands they generate. Forecasting these loads using artificial neural networks has proven to be an efficient way of solving time series problems. This study employs a multilayer perceptron network with backpropagation training and Bayesian regularisation to enhance generalisation and minimise overfitting errors. The research aggregates real consumption data from 200 households and 348 electric vehicles. The developed method was validated using MAPE, which resulted in errors below 6%. Short-term forecasts were made across the four seasons, predicting the total aggregate demand of households and vehicles for the next 24 h. The methodology produced significant and relevant results for this problem using hybrid training, a few-neuron architecture, deep learning, fast convergence, and low computational cost, with potential for real-world application. The results support the electrical power system by optimising these loads, reducing costs and energy generation, and preparing a new scenario for EV penetration rates. Full article

Due to the limitations of current battery technologies—such as lower energy density and high cost compared to fossil fuels—electric vehicles (EVs) face constraints in applications requiring extended range or heavy payloads, such as refuse trucks. As a midterm solution, hybrid electric vehicles (HEVs) combine internal combustion engines (ICEs) and electric powertrains to enable flexible energy usage, particularly in urban duty cycles characterized by frequent stopping and idling. This study introduces a model-based energy management strategy using the Equivalent Consumption Minimization Strategy (ECMS), tailored for a retrofitted series hybrid refuse truck. A conventional ISUZU NPR 10 truck was instrumented to collect real-world driving and operational data, which guided the development of a vehicle-specific ECMS controller. The proposed strategy was evaluated over five driving cycles—including both standardized and measured urban scenarios—under varying load conditions: Tare Mass (TM) and Gross Vehicle Mass (GVM). Compared with a rule-based control approach, ECMS demonstrated up to 14% improvement in driving range and significant reductions in exhaust gas emissions (CO, NO_x, and CO₂). The inclusion of auxiliary load modeling further enhances the realism of the simulation results. These findings validate ECMS as a viable strategy for optimizing fuel economy and reducing emissions in hybrid refuse truck applications. Full article

The complexity and shortening of development cycles in the automotive industry, particularly with the rise in hybrid electric vehicle sales, increases the need for efficient calibration and testing methods. Virtualization using hardware-in-the-loop testbenches has the potential to counteract these trends, specifically for the calibration of hybrid operating strategies. This paper presents a dynamic closed-loop validation of a hardware-in-the-loop testbench designed for the virtual calibration of hybrid operating strategies for a plug-in hybrid electric vehicle. Requirements regarding the hardware-in-the-loop testbench accuracy are defined based on the investigated use case. From this, a dedicated hardware-in-the-loop testbench setup is derived, including an electrical setup as well as a plant simulation model. The model is then operated in a closed loop with a series production hybrid control unit. The closed-loop validation results demonstrate that the chassis simulation reproduces driving resistance closely aligning with the reference data. The driver model follows target speed profiles within acceptable limits, achieving an R² = 0.9993, comparable to the R² reached by trained human drivers. The transmission model replicates the gear ratios, maintaining rotational speed deviations below 30 min⁻¹. Furthermore, the shift strategy is implemented in a virtual control unit, resulting in a gear selection comparable to reference measurements. The energy flow simulation in the complete powertrain achieves high accuracy. Deviations in the high-voltage battery state of charge remain below 50 Wh in a WLTC charge-sustaining drive cycle and are thus within the acceptable error margin. The net energy change criterion is satisfied with the hardware-in-the-loop testbench, achieving a net energy change of 0.202%, closely matching the reference measurement of 0.159%. Maximum deviations in cumulative high-voltage battery energy are proven to be below 10% in both the charging and discharging directions. Fuel consumption and CO₂ emissions are modeled with deviations below 3%, validating the simulation’s representation of vehicle efficiency. Real-time capability is achieved under all investigated operating conditions and test scenarios. The testbench achieves a real-time factor of at least 1.104, ensuring execution within the hard real-time criterion. In conclusion, the closed-loop validation confirms that the developed hardware-in-the-loop testbench satisfies all predefined requirements, accurately simulating the behavior of the reference vehicle. Full article

This article introduces a novel hybrid database architecture that combines graph and time-series databases to enhance the storage and management of transportation data, particularly for electric vehicles (EVs). This model addresses a critical challenge in modern mobility: handling large-scale, high-velocity, and highly interconnected datasets while maintaining query efficiency and scalability. By comparing a naive graph-only approach with our hybrid solution, we demonstrate a significant reduction in query response times for large data contexts-up to 64% faster in the XL scenario. The scientific contribution of this research lies in its practical implementation of a dual-layer storage framework that aligns with FAIR data principles and real-time mobility needs. Moreover, the hybrid model supports complex analytics, such as EV battery health monitoring, dynamic route optimization, and charging behavior analysis. These capabilities offer a multiplier effect, enabling broader applications across urban mobility systems, fleet management platforms, and energy-aware transport planning. By explicitly considering the interconnected nature of transport and energy data, this work contributes to both carbon emission reduction and smart city efficiency on a global scale. Full article

The driving power of hybrid electric vehicles serves as a crucial foundation for optimizing energy management strategies. The substantial load carried by heavy-duty vehicles significantly impacts the driving power through slope and acceleration. To minimize energy consumption in heavy-duty series hybrid electric vehicles, key variables are identified and predicted individually, employing the predictive equivalent energy consumption minimization strategy (ECMS) to optimize power distribution. In order to accurately forecast the driving power of heavy-duty vehicles, the vehicle mass is determined using the least squares method. To enhance time series data forecasting capabilities, a CNN-LSTM hybrid network is utilized to predict future vehicle speed and road slope based on historical time series data. By applying a longitudinal dynamics model, the identified vehicle weight, predicted speed, and slope can be converted into actual vehicle driving power. Within the prediction timeframe, different rolling calculation energy distribution methods utilizing equivalent factors are employed to achieve optimal energy consumption reduction. Road experiment data demonstrate that identification errors for various vehicle weights remain below 3%. The average RMSE for single-step drive power prediction stands at 14.8 kW. Simulation results using a test road reveal that the predictive ECMS reduces energy consumption by 6.2% to 15% compared to the original rule-based strategy. Full article

With the rapid global proliferation of electric vehicles (EVs), their integration as a significant load component within power systems increasingly influences the stable operation and planning of electrical grids. However, the high uncertainty and randomness inherent in EV users’ charging behaviors render accurate load forecasting a challenging task. In this context, the present study proposes a Particle Swarm Optimization (PSO)-enhanced Long Short-Term Memory (LSTM) network forecasting model. By combining the global search capability of the PSO algorithm with the advantages of LSTM networks in time-series modeling, a PSO-LSTM hybrid framework optimized for seasonal variations is developed. The results confirm that the PSO-LSTM model effectively captures seasonal load variations, providing a high-precision, adaptive solution for dynamic grid scheduling and charging infrastructure planning. This model supports the optimization of power resource allocation and the enhancement of energy storage efficiency. Specifically, during winter, the Mean Absolute Error (MAE) is 3.896, a reduction of 6.57% compared to the LSTM model and 10.13% compared to the Gated Recurrent Unit (GRU) model. During the winter–spring transition, the MAE is 3.806, which is 6.03% lower than that of the LSTM model and 12.81% lower than that of the GRU model. In the spring, the MAE is 3.910, showing a 2.71% improvement over the LSTM model and a 7.32% reduction compared to the GRU model. Full article

Accurate remaining capacity prediction (RCP) of lithium-ion batteries (LIBs) is crucial for ensuring their safety, reliability, and performance, particularly amidst the growing energy crisis and environmental concerns. However, the complex aging processes of LIBs significantly hinder accurate RCP, as traditional prediction methods struggle to effectively capture nonlinear degradation patterns and long-term dependencies. To tackle these challenges, we introduce an innovative framework that combines evolutionary learning with deep learning for RCP. This framework integrates Temporal Convolutional Networks (TCNs), Bidirectional Gated Recurrent Units (BiGRUs), and an attention mechanism to extract comprehensive time-series features and improve prediction accuracy. Additionally, we introduce a hybrid optimization algorithm that combines the Sparrow Search Algorithm (SSA) with Bayesian Optimization (BO) to enhance the performance of the model. The experimental results validate the superiority of our framework, demonstrating its capability to achieve significantly improved prediction accuracy compared to existing methods. This study provides researchers in battery management systems, electric vehicles, and renewable energy storage with a reliable tool for optimizing lithium-ion battery performance, enhancing system reliability, and addressing the challenges of the new energy industry. Full article

The development of energy-saving vehicles is an important measure to deal with environmental pollution and the energy crisis. On this basis, more accurate and efficient energy management strategies can further tap into the energy-saving potential and energy sustainability of vehicles. The equivalent consumption minimization strategy (ECMS) has shown the ability to provide a real-time sub-optimal fuel efficiency performance. However, when taking the different market prices of fuel and electricity cost as well as battery longevity cost into account, this method is not very accurate for total operational economic evaluation. So, as an improved scheme, the instantaneous cost minimization strategy is proposed, where a comprehensive cost function, including the market price of the electricity and fuel as well as the cost of battery aging, is applied as the optimization objective. Simulation results show that the proposed control strategy for series-parallel hybrid electric buses can reduce costs by 41.25% when compared with the conventional engine-driven bus. The approach also impressively improves cost performance over the rule-based strategy and the ECMS. As such, the proposed instantaneous cost minimization strategy is a better choice for hybrid electric vehicle economic evaluation than the other main sub-optimal strategies. Full article