In this paper, the energy management strategy between the internal combustion engine and the electric motor in parallel hybrid vehicles is investigated using an improved and innovative online approach. The proposed method is based on fuzzy systems which can provide online strategy, specifically Takagi-Sugeno fuzzy models, which offer several advantages due to their quasi-linear nature. These advantages include lower computational complexity compared to Mamdani fuzzy systems, simplicity in the design phase, lower implementation costs, and high accuracy in simulation results. The key innovation in this study is the reduction of the number of inputs to the fuzzy system. Instead of using both speed and torque as inputs for energy management, only power is utilized as the primary input. This approach ensures that the internal combustion engine operates at maximum efficiency while satisfying constraints such as driving cycle requirements. The results of the simulation provide evidence of improved vehicle performance and reduced fuel consumption, which are presented in detail in the following sections.
In order to investigate the CO2 emission characteristics of hybrid electric vehicles in the tank-to-wheel (TTW) Phase in plateau region, this study conducted real-driving emission tests in Kunming using a portable emissions measurement system (PEMS). The test fleet included one internal combustion engine vehicle (ICEV) and three hybrid electric vehicles with different powertrain architectures: a plug-in hybrid electric vehicle (PHEV), a range-extended electric vehicle (REEV), and a series/parallel hybrid electric vehicle (SPHEV). The results show that the CO2 emissions of hybrid electric vehicles is significantly lower than that of ICEV under various road conditions, especially under complex urban conditions, the CO2 emission factors of ICEV are 2.56-3.27 times higher than those of hybrid electric vehicles. Hybrid electric vehicles are influenced by engine and driving characteristics, with engine characteristics having a more significant impact. The CO2 emission rates exhibit a significant nonlinear relationship with engine speed and exhaust temperature, and its R2 = 0.86-0.94. Additionally, the CO2 emission rates show a significant polynomial function relationship with vehicle velocity and acceleration. It was also found that in the Bin33-Bin40 range, the CO2 emissions of ICEV, PHEV and REEV increased with the increase of vehicle specific power (VSP), while SPHEV showed a certain emissions suppression trend. Research indicates that hybrid electric vehicles possess stronger emission reductions capabilities under multiple operating conditions, providing technical support for achieving urban low-carbon transportation goals.
Hybrid electric vehicle (HEV) powertrain systems exhibit complex dynamic and nonlinear characteristics due to the coupling effects among mechanical, electrical, and thermal subsystems. Traditional multivariate statistical process monitoring (MSPM) methods based on the time lag shift method (TLSM) may suffer from redundant information problems where historical data not used for prediction can contaminate the extracted features and reduce fault detection sensitivity. To address this limitation, this paper proposes a kernel dynamic orthonormal subspace analysis (KDOSA) method for monitoring HEV powertrain faults. The proposed method extends the OSA framework to the kernel feature space using Gaussian kernel functions, aiming to capture nonlinear dependencies while maintaining orthogonal separation between dynamic and static components. By decomposing real-time data into dynamic and static subspaces in the reproducing kernel Hilbert space, KDOSA is designed to mitigate the redundant information problem inherent in TLSM-based kernel methods such as dynamic kernel PCA. A comprehensive monitoring framework is developed with [Formula: see text] indices for both dynamic and static subspaces, providing fault detection capability and diagnostic information about fault origins. The effectiveness of the proposed method is examined through numerical simulations and real-world HEV powertrain experiments. Experimental results demonstrate that KDOSA achieves favorable fault detection performance with performance index (PI) values exceeding 95% across all test scenarios without triggering false alarms in the tested cases, showing improved performance compared with existing OSA-based nonlinear dynamic methods.
Among the key challenges in the energy management systems (EMSs) of hybrid electric motorcycles (HEMCs) are supercapacitor SOC regulation to fully exploit hybridization benefits and appropriate topology design for reducing EMS control complexity. This study proposes two EMSs for HEMCs based on isolated dual-input bidirectional DC-DC converters and a Sugeno fuzzy-logic control strategy. A supercapacitor charge-sustaining approach is employed to improve power support during acceleration and high-power-demand intervals. Furthermore, the proposed converter structures provide inherent asymmetric power sharing between the battery and supercapacitor, reducing EMS control complexity and eliminating the need for additional power-sharing control loops. Two converter configurations with different power-sharing characteristics are investigated for different driving-condition tendencies. A dynamic HEMC model is developed for system simulation and evaluation. Simulation results demonstrate effective power sharing, supercapacitor utilization, and regenerative energy handling. Comparative analyses with existing studies further verify the operational effectiveness of the proposed EMSs.
The growing demand for electrification and the integration of renewable energy sources necessitate advanced power conversion technologies that combine efficiency and versatility. This paper introduces a novel Multi-Port Bidirectional Converter (MBPC) designed for both grid and hybrid electric vehicle (HEV) applications. Featuring two input and two output ports, the MBPC achieves an efficiency exceeding 95% and a power density above 10 W/cm². It effectively manages energy from sources with varying current and voltage profiles, supporting both boost DC-AC and boost DC-DC operations. The innovative design incorporates a streamlined control strategy and a reduced component count, cutting manufacturing costs by up to 30% compared to traditional converters. The proposed MBPC demonstrates significant potential for advancing power electronics in renewable energy systems and electric vehicles, offering a versatile, efficient, and cost-effective solution for energy storage, motor control, and power management applications.
This study investigated the impacts of altitude and initial battery state of charge (SOC) on ammonia and particle number (PN) emissions using two series hybrid electric vehicles (SHEVs) and one series-parallel hybrid electric vehicle (PSHEV) for twenty real driving emission (RDE) tests. Under high-altitude conditions, the average ammonia emissions of cars #A, #B, and #C reached 12.9, 48.1, and 9.9 mg/km, leading to increases of 188.4%, 74.6%, and 191%, compared with those at sea level, with increases amplified by lower SOC. The increasing altitude aggravated incomplete combustion and prolonged the fuel enrichment strategy duration, further elevating ammonia emissions. The average PN emissions at high altitude of cars #A, #B, and #C were 1.48 × 10 ¹ ¹, 2.11 × 10 ¹ ¹, and 8.14 × 10 ¹ ⁰ #/km, corresponding to the increase of 7.94%, 61.76%, and -29.57%, compared with sea-level conditions. The PN emission concentrated on the engine's initial start and restart stages and was also determined by the thermal state of the aftertreatment system for filtration efficiency. At high altitude, low initial SOC induced longer rich-combustion duration and higher ammonia emissions, while high initial SOC would result in high concentration of ammonia peaks with low occurrences. The increasing initial SOC exhibited decreasing and increasing influence on PN emissions for different vehicles, due to the variation of aftertreatment systems and powertrain architectures.
Frequent engine stop-start cycles in hybrid electric vehicles (HEVs) exacerbate noise, vibration, and harshness (NVH), degrading driving comfort-a challenge intensified by growing HEV market share. The initial crank angle (ICA) critically influences restart smoothness, yet traditional control systems relying on crankshaft sensors suffer from low-speed signal unreliability and increased complexity. This study proposes a position-sensor-free stop-position control strategy utilizing only the speed signal. By leveraging the deterministic relationship between speed extrema during the compression stroke and top dead center (TDC), real-time TDC detection and crank angle estimation are achieved. A C¹-continuous quadratic speed trajectory is designed to meet boundary conditions for final speed, acceleration and target position, integrating feedforward torque compensation and gain-scheduled PI feedback control. Vehicle tests demonstrate a positioning accuracy of 1.7[Formula: see text], complete elimination of engine reversal, and recovery of 44.69 J of kinetic energy (6.25% of restart energy demand). By eliminating the need for dedicated crankshaft sensors, this approach simplifies control architecture, offering a cost-effective solution particularly beneficial for low-cylinder-count HEVs with pronounced speed fluctuations.
Regulatory gaps in restart and cold/hot start emissions overlooked by current periodic technical inspection (PTI), and driving behaviors significantly impact plug-in hybrid electric vehicle (PHEV) particle number (PN) emissions under real driving conditions. Using portable emissions measurement systems (PEMS), this study builds cumulative PN emissions by key segments (cold-start, restart) and instantaneous high-emission events across four distinct behaviors. Key findings reveal that calm and normal driving elevate cold-start PN (up to 6.2 ×1011 #/km) due to prolonged engine-off intervals and slow warm-up. Aggressive driving's frequent restarts yield lower per-event emissions owing to thermal advantages. Adaptive cruise control (ACC) minimizes total PN by combining thermally efficient engine operation with extended zero-emission phases (16-17% duration). Crucially, instantaneous high-emission analysis shows > 80% of PN concentrates in < 20% of driving duration, with emission thresholds varying dramatically (82-1366%) across behaviors-primarily due to divergent dominant modes favored by each behavior. To quantify these behavior-specific modes and their parametric signatures, k-means clustering was applied, and found distinct behavioral associations: aggressive driving predominantly linked to high-load/high-rpm operation (> 2800 rpm or > 80% load), while calm/normal driving elevates cold-start and restart contributions. Consequently, real-world emission monitoring necessitates behavior-adaptive dynamic scenarios, tailoring test focus and parametric design informed by clustered thresholds.
Plug-in Hybrid Electric Vehicles (PHEVs) are widely regarded as a pivotal strategy for mitigating transportation emissions; however, a comprehensive understanding of their real-world emission characteristics across various operating modes, particularly the charge-increasing (CI) mode, remains insufficient. This research employed a Portable Emissions Measurement System (PEMS) to investigate the actual emission characteristics of a China VI-b compliant PHEV across urban, suburban, and highway conditions. Furthermore, multiple machine learning models were developed based on On-Board Diagnostics (OBD) data to predict pollutant emissions. Findings reveal that during cold-start, CO and HC emissions peaked in Charge-Sustaining (CS) mode, followed by CI mode, with Charge-Depleting (CD) mode exhibiting the lowest levels. Total NOx emissions, however, remained consistent across all three modes. Vehicle Specific Power (VSP) demonstrated a strong positive correlation with HC, NOx, and CO2 emissions in all propulsion modes. However, CO and Black Carbon (BC) emissions exhibited the highest sensitivity to VSP changes in CI mode, whereas this correlation was significantly weaker in CS and CD modes. Further analysis revealed that emission disparities among the three propulsion modes were most significant under urban conditions. Notably, NOx emissions in CI mode consistently surpassed China VI-b standards across all road types. Among the models evaluated, Random Forest (RF) and LightGBM models exhibited superior performance, with fuel consumption identified as the most critical factor influencing pollutant emissions.
Increasing adoption of zero-emission vehicles (ZEVs) has raised concerns about potential disparities, including increased gentrification in low-income communities. As a first step in unraveling this rarely studied relationship, we examined the association between ZEV adoption and measures of gentrification. We addressed two questions: (1) How do ZEV adoption trends vary by neighborhood-level gentrification and disadvantage in California metropolitan areas? and (2) Among neighborhoods that are not already gentrified, is prior ZEV adoption associated with subsequent changes in housing prices? We obtained census tract-level data on longitudinal counts of battery electric vehicle (BEV) and plug-in hybrid electric vehicle (PHEV) registrations, median home value and monthly rent, disadvantaged communities (DACs) designations, and gentrification status ('none/early,' 'advanced/stable,' or 'exclusive'). Linear mixed models estimated BEV (or PHEV) adoption trajectories (2015-2023) across gentrification status between DAC and non-DAC, allowing nonlinear time trends. Among 'none/early' gentrification tracts, we related prior ZEV adoption (2015-2019) to subsequent (2020-2023) home values and rental prices using linear mixed models. ZEV adoption trends varied by gentrification in non-DAC tracts, while DACs had lower levels of ZEV adoption and less evidence for disparities. Among 'none/early' gentrification tracts, tracts with higher BEV (or PHEV) adoption were associated with larger absolute increases in home value compared to tracts in the lowest adoption quartile. These early findings support community concerns that ZEV adoption may influence housing costs and gentrification. Further research is needed on these complex relations and possible policy interventions.
Dielectric capacitors are indispensable in modern electric systems, including smart grids and hybrid electric vehicles. However, their inherently low energy density limits further device miniaturization and integration. In this study, we present a comprehensive tripartite strategy to overcome this challenge. First, a series of thiourea-based polymers with tailored main-chain architectures are synthesized to optimize free volume and charge distribution in the polymer matrix. Second, nanofillers are incorporated to modulate band structure, achieving an electrical rectification effect that generates high-density interfacial carrier traps in the interface region-effectively suppressing electric field distortion. A rationally designed sandwich structure further homogenizes the electric field distribution, significantly improving dielectric breakdown strength. As a result, the polymer composites display markedly improved energy storage capabilities. The optimized sandwich-structured films achieve a remarkable discharged energy density of 7.33 J/cm3 at 660 MV/m with 90.0 % efficiency at room temperature, and 6.29 J/cm3 at 620 MV/m with 89.1 % efficiency at 150 °C. This work offers an effective strategy for developing high-performance polymer dielectrics with excellent energy storage performance under both ambient and high-temperature conditions.
Nitrous oxide (N2O) is a long-lived greenhouse gas with a global warming potential far exceeding that of carbon dioxide and methane. Despite its environmental significance, N2O emissions from modern light-duty vehicles remain poorly characterized under real-world conditions and across emerging powertrain technologies. In this comprehensive study, a diverse fleet of light-duty gasoline vehicles (LDGVs), hybrid electric vehicles (HEVs), plug-in hybrid electric vehicles (PHEVs), and a light-duty diesel vehicle (LDDV) was systematically investigated using both Worldwide harmonized Light vehicles Test Cycles (WLTC) and Real Driving Emissions (RDE) tests. Detailed analyses were conducted to evaluate operating condition distributions and the coupling relationships among N2O emissions, vehicle driving characteristics, and conventional regulated pollutants. The results demonstrate strong technology-dependent differences. The diesel vehicle exhibited the highest emission factor, primarily driven by non-selective reactions in the aftertreatment system. For gasoline and hybrid vehicles, comparisons between WLTC and RDE revealed that real-world stochastic driving significantly exacerbates transient N2O emissions. Notably, hybrid vehicles exhibited unique N2O profiles highly dependent on their energy management strategies, where frequent engine start-stops and recurring catalyst light-off behaviors dominantly influenced N2O formation. To quantitatively disentangle these complex interactions, a data-driven framework was further implemented. By training models with real-time kinematic parameters (velocity, acceleration, vehicle specific power) and instantaneous exhaust compositions, the feature importance was extracted to precisely identify the primary driving factors for each propulsion system. Ultimately, these multi-dimensional insights provide direct guidance for calibration strategies targeting N2O mitigation in next-generation emission control systems.
Decarbonizing the long-haul transportation sector is a critical global challenge. This paper introduces a hierarchical, learning-augmented framework for co-designing an ammonia-hydrogen hybrid electric powertrain as a viable carbon-free propulsion solution. At the core of the proposed framework is the tight coupling between online control and offline design. In the online supervisory control layer, a deep reinforcement learning (DRL) agent is trained to make real-time decisions on power split and on-board hydrogen production. In the offline propulsion system optimization layer, a DRL-augmented adaptive non-dominated sorting genetic algorithm (RL-ANSGA) is employed to solve the multi-objective component sizing problem, with each candidate design evaluated in high-fidelity co-simulation under a fixed, pre-trained DRL energy management policy. Results demonstrate that the optimized ammonia-hydrogen vehicle outperforms conventional diesel and diesel-hybrid counterparts in energy efficiency and well-to-wheel carbon emissions.
To overcome the efficiency degradation caused by independently designing transmission ratios and evaluating mechanical losses in hybrid electric vehicle drivetrains, this study proposes a unified transmission ratio-efficiency coupled modeling and optimization framework for multi-row planetary gear transmissions. An improved kinematic model based on topological analysis is integrated with a refined multi-source loss model for meshing, bearing, churning, and windage losses. The resulting nonlinear coupled system is solved using a Newton-Raphson method with adaptive step-size regulation. This approach enables the prediction of speed distribution, torque balance, and transmission efficiency under varying operating conditions. An enhanced multi-objective particle swarm optimization (MOPSO) algorithm is then employed to identify high-efficiency zones and to optimize key structural and lubrication parameters. Bench-test verification is conducted through efficiency MAP measurements, thermal endurance tests, and dynamic response evaluations. The results indicate a mean efficiency prediction error of 1.38% and stable thermal and transient behavior. After optimization, the high-efficiency zone coverage increases from 68.5% to 78.6%, and the comprehensive efficiency rises from 92.8% to 95.6%. Overall, the proposed framework provides a computationally efficient and engineering-applicable approach for the systematic design and optimization of planetary gear transmissions.
We examined at-fault injury crashes of four passenger car populations: Hybrid Electric Vehicles (HEVs), Plug-in Hybrid Electric Vehicles (PHEVs), Battery Electric Vehicles (BEVs) and traditional internal combustion engine vehicles (ICEVs). For these populations, crash rates were calculated in relation to both registration years and mileage. Finally, controlled crash rate ratios were calculated to compare the crash risk between electric vehicles (EVs) and ICEVs. Studied car populations were identified and their vehicle information for the period of 2019-2023, including the mileage (76 billion kilometers for all cars during the study period), was drawn from the national Vehicular and Driver Data Register. In addition, cars in the study populations were identified from the motor liability insurance (MLI) database and the crash data for them was retrieved (11,388 motor vehicle occupant injury crashes in total). Crash rates and crash rate ratios were calculated to evaluate the crash risk of EVs. Negative binomial regression was used to model crash involvement rate ratios both per registration year and per mileage for EVs, controlling the age and gender of the vehicle owner and vehicle size. Only battery electric vehicles showed significantly different crash rates than ICEVs per mileage, although the result was weakly significant -15% [-28%; 0%]. There were no significant differences in crash rates per registration years. In addition, there were only a few significant differences in crash circumstances between EVs and ICEVs. On average, the motor vehicle occupant injury crash rate of ICEVs was 151 crashes per billion kilometers and 2.37 crashes per thousand registration years. Our results indicate that, when measured by motor vehicle occupant injury crash rate, passenger cars-regardless of powertrain-have not become safer in Finland compared to the situation ten years ago. However, the current crash rate of BEVs is lower than that of ICEVs. Previous studies suggest that some of the differences in crash rate may be explained by varying usage conditions, which our findings support. Part of the difference may be explained by differences in driver populations, which should be investigated further.
This work presents a comprehensive optimization study for enhancing the performance of a Parallel Hybrid Electric Vehicle (PHEV). The primary objectives were the simultaneous improvement of fuel economy and the reduction of pollutant emissions, namely CO, HC, and NOx, while ensuring all driving performance requirements were met. A multi-objective optimization (MLO) framework was developed and implemented using both the Particle Swarm Optimization (PSO) and DIRECT algorithms, with their performance evaluated against a mono-objective (MNO) approach and a pre-optimized baseline model. Simulations were conducted over standard driving cycles (UDDS and HWFET) using the ADVISOR/Simulink environment. The results demonstrate that the MLO-PSO algorithm is the most effective strategy, successfully converging to superior solutions that significantly outperform both the baseline vehicle and the MNO results. Key improvements included a marked increase in the operational efficiency of the Internal Combustion Engine (ICE) and Electric Motor (EM), smarter battery State of Charge (SOC) management, and a concurrent reduction in fuel consumption and emissions. Under the UDDS cycle, PSO-based optimization achieves fuel consumption reductions of up to 13.74%, compared to 10.33% obtained with the DIRECT method. CO emission reductions with PSO range from 13.26% to 13.41%, while DIRECT achieves higher CO reductions of up to 17.2%. HC emission reductions under UDDS reach 15.66% with MLO-PSO, compared to 14.56% with MNO-DIRECT. Under the HWFET cycle, PSO further improves fuel economy, reaching a maximum reduction of 16.92%, while DIRECT achieves up to 13.33%. MLO-PSO also enables limited emission benefits, including a slight NOx reduction of 0.61% under HWFET, highlighting the trade-off between fuel economy and emission control. The simulation results demonstrate that PSO-based strategies has remarkable superiority in MLO. This study conclusively shows that a properly weighted MLO approach is essential for overcoming the inherent trade-offs in PHEV design, resulting in a vehicle that is more efficient, less polluting, and performs reliably.
The transportation sector's reliance on internal combustion engine (ICE) vehicles contributes significantly to energy consumption and environmental degradation. Plug-in hybrid electric vehicles (PHEVs) present a viable alternative by combining electric propulsion with ICE capabilities to enhance fuel efficiency and reduce emissions. This study evaluates the energy consumption and emission characteristics of PHEVs under diverse real-world driving conditions, focusing on charge-depleting (CD) and charge-sustaining (CS) modes. Using chassis dynamometer (5-cycle, WLTC under varying temperatures) and real driving emissions (RDE) tests, the study reveals that CD mode offers superior efficiency in urban driving due to regenerative braking, while CS mode performs better under high-speed or low-temperature conditions. Notably, energy consumption in CS mode was approximately 2.98 times higher than in CD mode during urban RDE tests. Cold start conditions significantly increased emissions and delayed catalyst activation by 100-150 s. Furthermore, maintaining battery state of charge (SOC) at 60 % in CS mode achieved the highest efficiency across urban and motorway scenarios. These findings suggest that adaptive integration of CD and CS modes, optimal SOC management, and mitigation of cold start effects are essential for improving PHEV efficiency and sustainability.
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This paper introduces a novel hybrid optimization framework for the aging-aware co-design of powertrain component sizing and energy management systems (EMS) in fuel cell hybrid electric vehicles (FCHEVs). The framework integrates the global search capability of the NSGA-II multi-objective evolutionary algorithm with the adaptive fine-tuning strengths of continuous deep reinforcement learning (DRL), employing Deep Deterministic Policy Gradient (DDPG), Twin Delayed DDPG (TD3), and Soft Actor-Critic (SAC). A Type-2 fuzzy logic controller is adopted as the EMS, with its parameters co-optimized alongside the scaling factors of the fuel cell stack, battery pack, and electric motor. The optimization simultaneously minimizes hydrogen consumption and component degradation, while ensuring compliance with real-world performance constraints. Validation is performed across multiple driving cycles, including the urban TEH-CAR cycle, UDDS, and WLTP-Class 3. Results indicate that, in the tested scenarios, the hybrid NSGA-II-DRL approach can improve convergence behavior and Pareto-front diversity compared to standalone NSGA-II. Fuel cell aging remained stable across algorithms, while significant differences emerged in battery degradation and fuel consumption: NSGA-II-DDPG minimized degradation but increased fuel use by 7-14%; NSGA-II-SAC reduced fuel use by 7-14% at the cost of 3-7% higher degradation; and NSGA-II-TD3 achieved fuel savings comparable to SAC with only 2-4% added degradation. Robustness tests under varying road grades further confirmed adaptability. Finally, hardware-in-the-loop validation on an STM32F7 microcontroller demonstrated real-time feasibility, with TD3- and SAC-based strategies showing superior robustness to hardware implementation effects.
This paper presents an adaptive predictive energy management strategy (EMS) for fuel cell hybrid electric vehicles (FCHEVs) that leverages traffic preview information to optimize hydrogen consumption and enhance battery longevity. Utilizing real-time Vehicle-to-Everything (V2X) data, a stochastic traffic prediction model generates probabilistic velocity profiles, enabling anticipatory energy allocation. The proposed two-layer adaptive equivalent consumption minimization strategy (A-ECMS) dynamically adjusts the equivalence factor based on predicted driving conditions, while a lower-level controller performs efficient real-time power distribution between the fuel cell system and battery. The approach concurrently addresses hydrogen economy and battery state-of-charge (SOC) sustainability. Simulations demonstrate 7.2% improvement in hydrogen efficiency over rule-based methods while maintaining SOC within 40-70% and reducing battery stress metrics by 25-40%. The framework is computationally efficient, with an average execution time under 10 ms per control interval, ensuring suitability for real-time embedded implementation. The results offer a viable and intelligent energy management solution for connected and sustainable fuel cell hybrid vehicles.