Hybrid Electric Vehicles Research Articles

Realistic Drive Cycle-Based Testing Methodology for Batteries in Micro Hybrid Electric Vehicles

Realistic Drive Cycle-Based Testing Methodology for Batteries in Micro Hybrid Electric Vehicles

Vehicle or power grid? Subsidy policy performance evaluation of China's plug in hybrid electric vehicles promotion and power grid cleaning construction

China has implemented strong incentives to promote the market of plug-in hybrid electric vehicles (PHEVs). The increase of PHEV will lead to an increase in electricity consumption, and more power generation will also lead to more carbon dioxide emissions. Therefore, it is necessary to evaluate the environmental benefits of the subsidy policy. In this study, we calculated the CO2 emission reduction and the increase of grid power generation and the CO2 emission from the investment and construction of clean energy power generation facilities caused by the subsidy of PHEV from 2016 to 2020, and evaluated the effect of PHEV subsidy through the multi-period DID model. The results show that replacing ICEV with a PHEV will reduce CO2 emissions by 12.38 kg per 100 km. Under the existing grid structure and power generation conditions, the promotion of PHEV will have a positive impact on CO2 emissions, but the effect is short-lived. Without considering other costs, the investment in clean energy power generation facilities at this stage will achieve better CO2 emission reduction effect than the promotion of PHEV.

Evaluating a novel bidirectional soft-switching DC-DC converter for electric vehicles

This research aims to build unique zero voltage transition (ZVT) non-isolated bidirectional DC-DC converters for hybrid electric vehicle battery storage. First, a high-voltage gain bidirectional converter (BDC) is examined. This converter can soft-switch insulated gate bipolar transistors (IGBTs). The primary insulated-gate bipolar transistors (IGBTs) are operated under zero-current conditions throughout the turn-on to turn-off commutation phase to reduce switching losses and increase efficiency. A soft-switched cell with a resonant inductor, capacitor, and additional IGBTs achieves zero-current turn-off. A new converter uses insulated-gate bipolar transistors with zero-voltage transition operation. Soft-switched cells improve the hard-switched bridgeless DC-DC converter (BDC). Resonant inductors, capacitors, and auxiliary switching devices make up the soft-switched cell. Soft-switched cells enable zero voltage turn-on of primary insulated-gate bipolar transistors. This converter charges the battery in buck mode and boosts it to provide the necessary output voltage. This study examined a 70 V/300 V power system's high-gain bidirectional converter (BDC) design simulation. The converter was tested at 50 kHz with 800 W output power. The high-gain soft-switched BDC has 96.5% boost and 97% buck efficiency. Operating principles, design analysis, and simulation assessments are included in this study.

An energy management strategy for fuel-cell hybrid electric vehicles based on model predictive control with a variable time domain

Fuel-cell hybrid electric vehicles (FCHEVs) are emerging as a pivotal model for future new energy vehicles due to their high efficiency and zero pollution. Crafting an effective energy management strategy is crucial for enhancing the robustness and economic viability of these vehicles. Therefore, an energy management strategy (EMS) for FCHEVs was proposed based on model predictive control (MPC) with a variable time domain. This approach integrated driving pattern recognition and model predictive control to overcome the robustness challenges faced by conventional EMS. Variational modal decomposition (VMD) was integrated with a radial basis function neural network for velocity predictions across various frequencies, which mitigated the effect of prediction accuracy on vehicles’ energy efficiency. Additionally, driving pattern recognition (DPR) was used to determine the driving condition online, which enhanced the influence of prediction duration on energy efficiency. This system adapted the prediction duration to the current driving condition to optimize hydrogen consumption. The velocity prediction model achieved 75.61% of error reduction, which saved 4.40% of hydrogen based on simulations. Besides, the model demonstrated enhanced adaptability to unpredictable driving environments.

Initial analysis and improvements to a stepped piston segregated scavenge engine for ultra-low emission range extender and hybrid electric vehicles

Initial analysis and improvements to a stepped piston segregated scavenge engine for ultra-low emission range extender and hybrid electric vehicles

Optimizing hardware configuration for solar powered energy management in battery ultracapacitor hybrid electric vehicles

The design and construction of an adaptive energy management system incorporating a 12 V–2 Ah battery and a 1F ultracapacitor for solar powered hybrid electric vehicles are presented in this paper. The primary storage battery’s longevity and overall system efficiency are intended to be increased by the EMS’s ability to forecast driving circumstances and lessen the load on it. The method optimizes power distribution among numerous energy storage sources by using sophisticated hardware configurations. Using Arduino Uno software, the EMS continuously modifies power distribution in response to driving circumstances and vehicle energy needs. To conduct a thorough assessment of the system, a mechanical configuration was created to replicate the vehicle’s dynamics. This included situations in which the vehicle moved only by using its moment of inertia and not the accelerator. Testing under diverse operating conditions, such as braking, acceleration, and varying slopes is made possible by this configuration. According to experimental data, peak battery stress is significantly reduced by about 33.33%, resulting in longer battery life. With minimal energy losses of 4.3–4.9%, the system regularly achieves power efficiency ranging from 95.1 to 95.7%. Furthermore, the suggested hardware-based EMS lowers overall energy loss by roughly 10.36%, highlighting its dependability and effectiveness in contrast to traditional techniques. The longevity and efficiency of the system are enhanced by the balanced power distribution between the battery, ultracapacitor, and solar sources, which prevents any one source from being overloaded. Overall, the suggested energy management system is successful in improving energy distribution and extending the lifespan and performance of HEV components.

Deep Learning Forecasting Model for Market Demand of Electric Vehicles

The increasing demand for electric vehicles (EVs) requires accurate forecasting to support strategic decisions by manufacturers, policymakers, investors, and infrastructure developers. As EV adoption accelerates due to environmental concerns and technological advances, understanding and predicting this demand becomes critical. In light of these considerations, this study presents an innovative methodology for forecasting EV demand. This model, called EVs-PredNet, is developed using deep learning methods such as LSTM (Long Short-Term Memory) and CNNs (Convolutional Neural Networks). The model comprises convolutional, activation function, max pooling, LSTM, and dense layers. Experimental research has investigated four different categories of electric vehicles: battery electric vehicles (BEV), hybrid electric vehicles (HEV), plug-in hybrid electric vehicles (PHEV), and all electric vehicles (ALL). Performance measures were calculated after conducting experimental studies to assess the model’s ability to predict electric vehicle demand. When the performance measures (mean absolute error, root mean square error, mean squared error, R-Squared) of EVs-PredNet and machine learning regression methods are compared, the proposed model is more effective than the other forecasting methods. The experimental results demonstrate the effectiveness of the proposed approach in forecasting the electric vehicle demand. This model is considered to have significant application potential in assessing the adoption and demand of electric vehicles. This study aims to improve the reliability of forecasting future demand in the electric vehicle market and to develop relevant approaches.

A switched model predictive control with parametric weights-based mode transition strategy for a novel parallel hybrid electric vehicle

In a novel parallel hybrid electric vehicle (HEV) configuration, the transition from pure electric mode to hybrid mode encompasses critical operations such as engine startup, coordinated control of motor and engine torque, and engagement of the clutch. Addressing the intricate challenges associated with enhancing speed tracking performance during and after mode transition, mitigating jerk during mode transition, and minimizing mode transition time, this paper conducts a meticulous analysis of the vehicle configuration and mode transition process. The mode transition process is systematically delineated into four stages, with each stage characterized by the establishment of dynamic models. Subsequently, a mode transition strategy is proposed, leveraging switched model predictive control with parametric weights (SMPC-PW). This controller framework includes the design of two model predictive controllers (MPC) tailored for two pivotal stages, the formulation of a parametric weights pattern based on pre-transition acceleration, and the development of a stage switching strategy to ensure seamless switches between controllers. The efficacy of the proposed strategy is validated through co-simulations in the Simulink and GT-Power environment. The fine-tuning of MPC parameters is grounded in multiple sets of prediction horizons and sampling time simulation results. In comparison to strategies based on MPC and PID under various acceleration scenarios, the SMPC-PW strategy consistently maintains acceleration control below 10 m/s3. It not only achieves superior speed tracking during and after mode transition but also reduces mode switch time by 0.1 s-0.3 s. These compelling results unequivocally demonstrate that the proposed mode transition strategy significantly elevates the quality of mode transition for this specific parallel HEV configuration.

Designing high-performance asymmetric and hybrid energy devices via merging supercapacitive/pseudopcapacitive and Li-ion battery type electrodes

We report a strategic development of asymmetric (supercapacitive–pseudocapacitive) and hybrid (supercapacitive/pseudocapacitive–battery) energy device architectures as generation–II electrochemical energy systems. We derived performance-potential estimation regarding the specific power, specific energy, and fast charge–discharge cyclic capability. Among the conceived group, pseudocapacitor–battery hybrid device is constructed with a high-rate intrinsic asymmetric pseudocapacitive (α − MnO2/rGO) and a high-capacity Li-ion intercalation battery type (po-nSi/rGO) electrodes. The experimental setup was developed to measure the current sharing between the two different active materials in a single device allowing us to distinguish the contribution of each active electrode material. The combined potentiostatic cyclic voltammograms and galvanostatic charge–discharge cycling profiles provided gravimetric capacity exceeding 600 F/g (or 180.5 mAh g−1 and ≥ 35mC/cm2) resulting in higher specific power and specific energy densities of 6.5 kW kg−1 and 33.5 Wh kg−1 with Coulombic efficiency (CE) and capacitance retention exceeding ≥ 85–90%, reported to date for full cell configuration, compared with symmetric or half-cell configurations (ca. 0.1 kW kg−1 and 13.7 Wh kg−1). Other systems studied provided specific energy ranged between 28 Wh kg−1 and 50 Wh kg−1 and specific power between 6.5 kW kg−1and 1.3 kW kg−1. Moreover, the behavior of such asymmetric hybrid devices represented a linear combination of the two active electrode material systems. The use of aqueous (and organic) electrolytes for asymmetric electrodes dramatically improved device performance and stability depending upon the electrode combination forming hybrid energy devices. We attribute the observed efficient performance of these hybrid devices induced by hybridized and emergent redox chemistries of merged electrode materials and dynamical processes at the electrode-electrolyte interfaces (intrinsic electroactivity, optimized double-layer and quantum capacitance) which play multiple roles. These energy devices are commercially relevant due to their potential viability in future hybrid electric vehicles, smart electric grids, electrocatalytic fuel production, space (micro-satellites), and miniaturized flexible electronic and wearable biomedical devices.

Optimizing renewable energy-based plug-in hybrid electric vehicle charging stations for sustainable transportation in India

India's burgeoning population necessitates innovative transportation solutions, with electric vehicles (EVs) emerging as a locally viable and urgent need. However, a significant obstacle lies in the improper deployment of charging stations, which strains the power distribution system. Recognizing the environmental benefits of Renewable Energy Sources (RES), high-priority buses are now equipped with Plug-in Hybrid Electric Vehicle (PHEV) charging stations, bolstering effective utilization. This study leverages meta-heuristic optimization techniques to propose an efficient allocation strategy for PHEV charging stations powered by RES. The core objective is to establish a cost-effective charging infrastructure while upholding the effective integrity of the network in distribution system. To address these challenges, the study employs meta-heuristic optimization algorithms and optimizes forecasting around sustainable energy systems to achieve desired output. Consequently, the research introduces a novel approach, conceptualizing the distribution of optimal charging stations as a multi-objective problem with added parameters. Furthermore, this study tackles the RES PHEV and charging station site challenge by introducing the innovative Walrus Optimization Algorithm (WaOA), which effectively overcomes issues related to slow convergence and local optima. Experimental findings demonstrate the superiority of the proposed model in terms of minimizing the cost function. This research offers a promising pathway toward sustainable transportation in India's evolving landscape.

Multi-objective optimal allocation of renewable distributed generation units in a distribution network under high penetration of plug-in hybrid electric vehicles

Multi-objective optimal allocation of renewable distributed generation units in a distribution network under high penetration of plug-in hybrid electric vehicles

Provincial inequalities in life cycle carbon dioxide emissions and air pollutants from electric vehicles in China

It is still unclear whether emissions reductions from electric vehicles can be achieved across different regions from a lifecycle perspective. Here we use the life cycle assessment model and Quasi Input-Output model to evaluate the carbon dioxide emissions and air pollutants of internal combustion engine vehicles, plug-in hybrid electric vehicles, and battery electric vehicles in different provinces of China, with the provincial electricity consumption data and the sales data of electric vehicles. We find that battery electric vehicles have achieved 11.8% and 1.1% reduction in carbon dioxide and nitrogen oxide emissions, respectively, compared to internal combustion engine vehicles. In contrast, the emissions of sulfur dioxide and particulate matter 2.5 increased by 10% and 20%, respectively. Due to the coal-based power generation structure and the cold weather, the emission intensity of battery electric vehicles in most northern provinces is higher than that in southern provinces. From 2020 to 2030, improving technological progress and optimizing electricity mix will greatly assist in achieving emissions reduction. The results can help policy-makers better understand the emission characteristics and reasonably plan future emission reduction strategies in transportation.

FEA-Based Design Procedure for IPMSM and IM for a Hybrid Electric Vehicle

This paper describes the detailed design procedure of electric machines using finite element analysis (FEA). The proposed method uses the available findings from the literature and FEA results for the design procedure. In addition to electromagnetic analysis, thermal analysis is executed to examine the capability of the designed machines for handling the load in terms of thermal limits. It allows for considering the normal and overload performance of the electric machines during design. The proposed design procedure is used for designing a 100 kW induction machine (IM) and interior permanent magnet synchronous machine (IPMSM) for a parallel hybrid electric vehicle (HEV). The differences between the performance parameters of the studied machines are discussed, and the advantages and disadvantages of each design are highlighted. The designed machines are compared with commercially available electrical machines in terms of performance and power density. The comparison demonstrates that the developed machines can offer comparable performance to other designs.

Modelling and simulation hybrid electric vehicle with hydrogen fuel cells

The article is devoted to the development of simulation model in the MATLAB Simulink software environment of an electric vehicle hybrid traction drive based on fuel cells and lithium battery. The applied topologies of battery and hybrid traction drives are considered. A traction drive model has been synthesized, in which the main energy source is a fuel cell (FC) with a proton exchange membrane (PEM), and the unevenness of the transport load is smoothed out by a high-power buffer storage unit (BSU) based on a lithium-titanium-oxide (LTO) battery.The dependence of the required BSU capacity on the power of the primary source, reduced to a ton of vehicle weight, was obtained. Based on this, the optimal range of hybrid power plant parameters was determined (FC power from 5 to 11 kW/t, LTO battery capacity from 6 to 10 Ah/t) when driving according to the WLTC load cycle. The calculated fuel consumption in this case was 0.56 kg/(km-t), and the time of included state of FC was 94.53%.

An optimal energy consumption calculation method of hybrid electric vehicles with frequency distribution characteristics of driving conditions

For a series of problems in the design and optimisation of series hybrid electric vehicles caused by the driving condition input, such as a variety of user conditions, which may hinder the energy-saving ability of such vehicles. Condition construction will inevitably lead to information loss, and traditional design methods require high computational power and encounter a time disaster during the real-world condition data explosion. This study proposes a condition representation method with driving condition frequency distribution characteristics (DCFDCs) and an optimal energy consumption calculation method, that is, instantaneous battery energy balance solution and global battery energy balance correction (IBGB–GBEB) with DCFDCs. The energy consumption calculation results show that the proposed optimal energy consumption method can guarantee the accuracy of the energy consumption calculation and reduce the calculation time cost. The difference in fuel consumption between the optimal energy consumption method and the dynamic planning (DP) algorithm is only 2.50%, but the difference in the calculation time between the two methods is 87.00%. Furthermore, the practical example of hybrid power system (HPS) design parameter optimisation shows that the energy consumption calculation results obtained by the TDCR–DP and DCFDCs–MIGA methods are consistent, and the relative error is only 1.81%. However, the calculation time of the TDCR–DP method is 472 h, whereas that of the DCFDC–MIGA method is only 2.17 h, which is a calculation time reduction of more than 99.54%. The results highlight the effectiveness and time advantage of the optimal energy consumption calculation method with DCFDCs. This study can provide theoretical guidance and technical support for solving system optimisation problems caused by the explosion of driving condition data.

State-of-Health Estimation for Lithium-Ion Batteries in Hybrid Electric Vehicles—A Review

This paper presents a comprehensive review of state-of-health (SoH) estimation methods for lithium-ion batteries, with a particular focus on the specific challenges encountered in hybrid electric vehicle (HEV) applications. As the demand for electric transportation grows, accurately assessing battery health has become crucial to ensuring vehicle range, safety, and battery lifespan, underscoring the relevance of high-precision SoH estimation methods in HEV applications. The paper begins with outlining current SoH estimation methods, including capacity-based, impedance-based, voltage and temperature-based, and model-based approaches, analyzing their advantages, limitations, and applicability. The paper then examines the impact of unique operating conditions in HEVs, such as frequent charge–discharge cycles and fluctuating power demands, which necessitate tailored SoH estimation techniques. Moreover, this review summarizes the latest research advances, identifies gaps in existing methods, and proposes scientifically innovative improvements, such as refining estimation models, developing techniques specific to HEV operational profiles, and integrating multiple parameters (e.g., voltage, temperature, and impedance) to enhance estimation accuracy. These approaches offer new pathways to achieve higher predictive accuracy, better meeting practical application needs. The paper also underscores the importance of validating these estimation methods in real-world scenarios to ensure their practical feasibility. Through systematic evaluation and innovative recommendations, this review contributes to a deeper understanding of SoH estimation for lithium-ion batteries, especially in HEV contexts, and provides a theoretical basis to advance battery management system optimization technologies.

Advancing combustion technologies and alternative fuels in hybrid electric vehicles: a pathway to high-efficiency, low-emission propulsion systems

Advancing combustion technologies and alternative fuels in hybrid electric vehicles: a pathway to high-efficiency, low-emission propulsion systems

Carbon Footprint of Electric Vehicles—Review of Methodologies and Determinants

The carbon footprint of a product and organization is one of the most important environmental indicators in many sectors, including transport. Consequently, electric vehicles (EV) are being introduced as an alternative to achieve decarbonization targets. This article presents an overview of methodologies for assessing the carbon footprint of electric vehicles, including a review of concepts, methods, standards, and calculation models based on the life cycle of the carbon footprint. The article also includes a systematic review of the results of EV carbon footprint analyses. The analysis of current knowledge on the carbon footprint focuses on road transport vehicles: Battery Electric Vehicles (BEV), Fuel Cell Electric Vehicles (FCEV), Hybrid Electric Vehicles (HEV), and Plug-in Hybrid Electric Vehicles (PHEV). Additionally, a review of factors determining the carbon footprint assessment of electric vehicles, considering their entire life cycle, has been conducted.

The effect of different mission profiles and ambient temperature on the lifetime of boost converter IGBT with a robust controller in a hybrid electric vehicle

AbstractThe reliability of power electronic converters is one of the essential issues in designing of electric vehicles. This paper estimates the lifetime of the boost converter switch by Semikron and Coffin‐Manson models for two common failure mechanisms of Bond wire and Base plate solder, respectively. Four mission profiles based on the Artemis standard are applied to hybrid electrical vehicle model to determine unidirectional output power. Kharitonov's theory is used to design a robust controller to handle the uncertainty raised by different power cycles of the electric vehicle and the parameters of the converter during simulation. Stability of converter is achieved during simulation by identifying simple proportional integral controller coefficients with Kharitonov's theorem. A prototype 230 W boost converter is designed and utilized to validate average model and switch loss calculation relationships. The lifetime results indicate that the number of cycles, and the average and the maximum junction temperature have a more impact than the duration of the drive cycle on the lifetime of the converter. A mixed mission profile is considered to investigate the effect of sudden change in driving modes and speed on total consumed life and lifetime to enhance the study's applicability. Lifetime of switch is decreased significantly in mixed mode in comparison with other mission profiles in the same driving time. Furthermore, the motorway mission profile has 53%, 39.6%, and 160% less total consumed life in comparison with the urban, rural and mixed mission profiles, respectively. In addition, the effect of ambient temperature changes on IGBT lifetime has been investigated for four mission profiles. While motorway had the least total consumed life in 25°C, the urban had better performance in comparison with other mission profiles from 25 to 55°C.

Effectiveness and Heterogeneous Effects of Purchase Grants for Electric Vehicles

AbstractWe evaluate German purchase subsidies for battery electric vehicles (BEVs) and plug-in hybrid electric vehicles (PHEVs) using data on new vehicle registrations in Germany during 2015-2022. We account for confounding time trends and interacting EU-level $$\text {CO}_{2}$$ standards using neighboring countries as a control group. We find that 40% of BEV and 25% of PHEV registrations were subsidy-induced. The program had strong distributional effects, with greater uptake in wealthier and greener counties. We estimate implied abatement costs of 870 euro per ton of $$\text {CO}_{2}$$ for BEVs and 2,470 euro for PHEVs, suggesting that policy makers should re-balance support schemes away from PHEVs.