Worldwide Harmonized Light Vehicles Test Research Articles

Adaptive extended kalman filter for PEMFC membrane water content estimation

Proton Exchange Membrane Fuel Cells are a favorite technology for decarbonizing the transportation sector. However, their large-scale democratization is hampered by their high cost compounded by their unsatisfactory lifespan. To anticipate potential degradation while keeping improving performance, it is essential to maintain an acceptable humidity range inside the cells, especially at the membrane level. However, membrane humidity level is not directly measurable, alternative techniques must be considered to recover this key variable. Here, we develop a real-time software sensor of the membrane water content at the fuel cell’s heart. We build a model describing the membrane water balance, electrochemical behavior, and species mass balance. We then reduce the model and perform an Adaptive Extended Kalman Filter. We perform sensitivity analyses in both steady-state and transient conditions. We validate the filter on a “Worldwide Harmonized Light Vehicles Test Cycles” test procedure. Finally, we obtain a fast and accurate model-based software sensor.

Electric vehicles as an emission mitigation option: expectations and reality

AbstractGlobally, the reliance on thermal power, constituting 67% of electricity generation, prompts questions for many countries regarding the viability of electric vehicles (EVs) for emission reduction in the transportation sector. This study comprehensively analyzes CO2 emissions in the small family car segment, considering engines powered by different energy sources. The analysis is coupled with Worldwide Harmonized Light Vehicle Test Procedure driving profiles, accounting for specific ambient temperature structures representative of densely populated areas in Türkiye. The constructed model incorporates the hourly temperature of the relevant cities and the hourly electricity production mix by considering the indirect emissions and comparing the ICEVs (Internal Combustion Engine Vehicles) and EVs in different time frames from 2015 to 2020. The resulting emissions from each vehicle type in each city and driving profile, along with the ratio of instances in which EVs exhibit higher emissions than ICEVs, are presented.The findings reveal the intricate interplay between vehicle type, climate conditions, and driving profiles. While the urban driving profile emerges as superior for EVs regarding CO2 reduction, offering a comfortable driving experience without range anxiety and mitigating local pollutants, it exhibits less significant carbon emission reductions at lower and higher speed profiles, particularly in cold climate conditions. EVs are not a silver bullet but a valuable technology for emissions reduction under specific conditions. Policymakers are urged to consider factors favoring EVs over ICEVs regarding CO2 emissions, such as driving profiles, time and climatic conditions when formulating investment policies and subsidies. Graphical abstract

Large-scale research on durability test cycle of fuel cell system based on CATC

Durability is one of the technical bottlenecks restricting fuel cell electric vehicle development. As a result, significant time and resources have been invested in research related to this area worldwide. Current durability research mainly focuses on the single cell and stack levels, which is quite different from the usage scenarios of actual vehicles. There is almost no research on developing durability test cycles on the fuel cell system level. This paper proposes a universal model for developing a durability test cycle for fuel cell system based on the China automotive test cycle. Large-scale comparison tests of the fuel cell systems are conducted. After 1000 h test, the output performance degradation of three mass-produced fuel cell system is 14.49%, 9.59%, and 4.21%, respectively. The test results show that the durability test cycle proposed in this paper can effectively accelerate the durability test of the fuel cell system and evaluate the durability performance of the fuel cell system. Moreover, the methodology proposed in this paper could be used in any other test cycles such as NEDC (New European Driving Cycle), WLTC (Worldwide Harmonized Light Vehicles Test Procedure), etc. And it has comprehensive application value and are significant for reducing the cost of durability testing of fuel cell systems and promoting the industrialization of fuel cell electric vehicles.

Development of dual polarization battery model with high accuracy for a lithium-ion battery cell under dynamic driving cycle conditions

Lithium-ion batteries are a key technology for electric vehicles. They are suitable for use in electric vehicles as they provide long range and long life. However, Lithium-ion batteries need to be controlled by a Battery Management System (BMS) to operate safely and efficiently. The BMS continuously controls parameters, such as current, voltage, temperature, state of charge (SoC), and state of health (SoH), and protects the battery against overcharging and discharging, imbalances between cells, and thermal runaways. The battery models and several prediction algorithms that the BMS uses to carry out these checks are essential to the system's performance. This research assesses the Dual Polarization (DP) model's ability to mimic actual battery performance in different dynamic driving conditions. In the study, a battery model for a Lithium–Nickel–Manganese–Cobalt-Oxide (Li-NMC) cell with a nominal capacity of 2 Ah is developed. A DP model was used in the study. Modeling and parameter estimation were performed in MATLAB Simulink/Simscape. Firstly, the model parameters are estimated depending on the SoC using the current and voltage data obtained from the Hybrid Pulse Power Characterization (HPPC) test. A further validation study of the model for low dynamic and high dynamic driving cycles is then presented. Dynamic Stress Test (DST), the US06 Supplemental Federal Test Procedure (SFTP) and Worldwide harmonized Light vehicles Test Procedure (WLTP) cycles were used for model validation. As a result of the study, the model's Root Mean Square (RMS) error values were obtained as 0.0053 V for DST, 0.0059 V for US06, and 0.008 V for WLTP. The obtained model is particularly successful for simulating a battery under dynamic current conditions and for use in control and prediction algorithms.

The comparison of gasoline powered vehicle and serial hybrid vehicle on emissions

In this study, a vehicle model was developed to examine the performance of a gasoline-powered vehicle and a vehicle with a serial-hybrid-drive system. An extra-downsized gasoline hybrid engine was used as a range extender in the vehicle model. Utilizing OBD II output to collect data, engine data for a sample vehicle was established with a neural network. Vehicle models were subsequently built using Matlab Simulink to complete the study. A comparison was made between the vehicle equipped with a serial-hybrid-drive system and the one with a gasoline engine-powered system regarding their fuel consumption and CO2 emissions for NEDC (New European Drive Cycle) and WLTC (Worldwide Harmonized Light Vehicles Test Cycle) Class 2 cruise cycles. Based on two driving cycles with different speed-time profiles, the results demonstrate the significant impact that powertrains can have on a vehicle's efficiency and performance, particularly during the transition from NEDC to WLTC which takes into account higher speeds, dynamic driving cycles, and factors such as air conditioning usage. Based on the findings, there was a 3.3% rise in CO2 emissions during an NEDC driving cycle with an additional downsized serial-hybrid-drive system. However, the opposite occurred during the WLTC driving cycle, resulting in a 1.7% decline in the serial-hybrid vehicle's fuel consumption and emissions performance.

Real‐time monitoring and ageing detection algorithm design with application on SiC‐based automotive power drive system

AbstractThe article describes an innovative methodology for the design and experimental validation of monitoring and anomaly detection algorithms, with a particular focus on the aging phenomenon, linked to the anomalous modification of the , in devices switching in power electronic systems integrated into modern high‐performance electrified vehicles. The case study concerns an electric drive for fully electrified vehicles, in which a three‐phase axial flux synchronous motor integrated into a wheel motor (Elaphe) is used and in which a high‐efficiency three‐phase inverter, designed with SiC technology (silicon carbide). The article proposes the design and validation of the innovative aging monitoring and detection system, in four consecutive phases. The first phase involves the creation of a real‐time model of electric drive, validated through experimental data extrapolated directly during a WLTP (Worldwide Harmonized Light Vehicle Test Procedure) test. The second phase consists of the creation of a virtual dataset representative of the aging phenomenon, via an anomaly injection procedure, emulating this phenomenon with a scaling factor (depending on the value of the ) on the current phase of the motor, relating to the inverter branch whose SiC device is affected. The third phase concerns the design of an estimator of the , based on an ANN (Artificial Neural Network) regression model, and involves a data manipulation phase with features extraction and reduction techniques. The fourth and final phase, involves the experimental validation of the method, through PIL (Processor‐In‐the‐Loop) tests, integrating the monitoring algorithm (consisting of a real‐time model and AI‐based regression model) on the NXPs32k144 embedded platform (based on Cortex‐M4), making the algorithm interact with the electric drive model on which anomaly injection is applied.

Thermal management improvement in electric vehicles through design optimization of coolant distributor valves

On battery electric vehicles a precise control of heat flows is required for passenger comfort and for both performance and safety of battery packs. This is typically achieved with a thermal management system which employs some type of liquid coolant to transfer heat. Coolant distributor valves are used to regulate coolant flows through the exchangers, for example to recover heat from hot sources, thus to increase vehicle energy efficiency. The development of this type of component is high-priced and the design is generally complex since several operating functions and manufacturing constraints should be satisfied. Despite this complexity and the positive impact such valves have on the thermal management of electric vehicles, a few works are currently available in the literature on design procedures for this component, where only a partial overview of the topic is provided and a comprehensive design methodology considering the effective operating conditions is still missing. In this work we construct a physical model of the valve for torque, internal leakage and pressure drop, and we build an optimization procedure to improve the global performance of the valve. We compare the obtained designs with a simpler approach that optimizes performance parameters independently and we show that designs which severely under-perform with respect to the uncontrolled performance parameters can be produced if an over-simplified design approach is followed. We then show with a global vehicle thermal model that the impact of valve design on global vehicle performance crucially depends on the thermal exchanges at vehicle level and, thus, on its operating conditions. In particular, the impact is considerable when mixing of cold and hot flows through the valve occurs. For a specific example vehicle evaluated in a type-3a cycle of the Worldwide Harmonized Light Vehicle Test Procedure, a valve determined with the proposed optimization procedure allows us to obtain in this condition an increase of about 1 km in the vehicle driving range with respect to a valve defined with the simpler approach.

Modeling and Control of a PEM Fuel Cell Hybrid Energy System Used in a Vehicle with Fuzzy Logic Method

PEM (Proton Exchange Membrane) fuel cells, which are commonly used in vehicles, are critical for sustainable transportation in the future. In this study, it is aimed to en-hance the system efficiency of the PEM fuel cell and provide fuel economy. To achieve this goal, the hybrid energy system with a PEM fuel cell and battery pack is controlled with two different strategies. The first control strategy is designed using Fuzzy Logic (FL), while the other control strategy is designed with the classical on-off method with the 'Relay' block. Power output of the fuel cell is determined depending on the change in the charging state of the battery pack and the power consumed by the electric vehicle in this study. The aim is to provide that the fuel cell operates in a high-efficiency range and can generate enough power when needed. Vehicle and fuel cell modeling were per-formed in Matlab/Simulink environment. NEDC (New European Driving Cycle) and WLTP (Worldwide Harmonized Light Vehicles Test Procedure) driving cycles were considered and fuel cell efficiency and hydrogen consumption were compared at dif-ferent state of charge values of the battery. The analyses were carried out over long dis-tances by repeating the driving cycles. It was observed that fuzzy logic control provid-ed 11.6% less fuel consumption than classic on-off control in NEDC and WLTP driving cycles repeated five times. The values obtained as a result of the study showed that fuzzy logic control is more advantageous to increase the energy efficiency of fuel cells.

LSTM-Based Virtual Load Sensor for Heavy-Duty Vehicles.

In this paper, a special recurrent neural network (RNN) called Long Short-Term Memory (LSTM) is used to design a virtual load sensor that estimates the mass of heavy vehicles. The estimation algorithm consists of a two-layer LSTM network. The network estimates vehicle mass based on vehicle speed, longitudinal acceleration, engine speed, engine torque, and accelerator pedal position. The network is trained and tested with a data set collected in a high-fidelity simulation environment called Truckmaker. The training data are generated in acceleration maneuvers across a range of speeds, while the test data are obtained by simulating the vehicle in the Worldwide harmonized Light vehicles Test Cycle (WLTC). Preliminary results show that, with the proposed approach, heavy-vehicle mass can be estimated as accurately as commercial load sensors across a range of load mass as wide as four tons.

OPTIMISATION OF HYBRID ELECTRIC VEHICLE ENERGY MANAGEMENT USING MACHINE LEARNING

The focus of the research is on the optimization of the hybrid electric vehicle energy management using machine learning. The objective of this research is to develop the algorithm by using Support Vector Machine (SVM) and to identify the optimal operation mode for SHEV based on the power demand use by using the algorithm. First of all, this research focus on the series hybrid electric vehicle and the vehicle used is an all-terrain vehicle (ATV). Suitable formula will be used to complete the modeling of the vehicle in Energetic Macroscopic Representation (EMR) form and the model constructed by using Matlab Simulink. Then, Support Vector Machine is used to optimize the energy management in the vehicle. The training data from New European Driving Cycle (NEDC) and Worldwide harmonized Light vehicles Test Cycles (WLTC) with 3 classes will put be inside the SVM to undergo training and then the optimal operation modes for each driving cycle will be obtained by using Linear SVM. Then, the obtained results which are the predicted operation mode using the driving cycles and are plotted in the graph. The pattern of the graph is analyzed and then the best predicted operation mode with the highest accuracy among all the driving cycles is chosen. The trained model of the driving cycle with the highest accuracy is used to predict the optimal operation mode for ATV so that to have higher efficiency in energy management by using the Classification Learner inside the Matlab.

Using a Reference Electrode inside Li-Ion Cell As an Operando Sensor to Detect Aging Mechanisms

Lithium-ion batteries (LIBs) are a key technology in the massive deployment of EVs. Besides enhancing the capabilities and performances of the batteries, we want to accelerate their charging rate to increase their driving range. However, special attention must be paid to safety and thus to understanding the aging mechanisms inside the cells. Evaluating the origin of capacity decay that is linked to loss of cyclable lithium (LLI: Loss of Lithium Inventory) or active materials (LAM: Loss of Active Material) can be obtained by two different methodologies but both have certain limitations: Non-invasive [1], of which one of the most popular is Incremental Capacity Analysis (ICA). ICA operates by slipping or contracting the electrochemical profiles of the electrodes to fit the experimental curve of the cell obtained at low current. This method can provide contradictory information if not well used [2].Post Mortem Analysis (PMA) [3], LLI and LAM are evaluated after sampling of disks of electrodes retrieved by dismantling the cell at discharged state and re-assembled in half coin-cells versus lithium. However, coin cells are assembled by introducing fresh electrolyte and separator that can be not representative of the real state of impregnation of the electrodes and separator in the cell after cycling. Moreover, sampling must be performed on different areas where heterogeneities are observed (gas bubble, lithium deposition) to be representative of the global behavior of the electrode considering their respective surface [4,5]. Embedded sensors inside the batteries are a major asset to monitor the internal parameters and prevent failures. The implementation of a reference electrode [6], as an electrochemical sensor, provides interesting results in terms of operando degradation detection since it allows the potential of the positive and negative electrodes to be monitored individually. The objective of our study was to assess the efficiency of the reference electrode, as a sensor, to quantify the aging mechanisms occurring inside the cell and detect lithium plating onset. The reference electrode could be used to modulate the current and reduce the overpotential of the negative electrode avoiding lithium metal being plated. Such sensor implementation is in the scope of several European projects including the INSTABAT project [7]. We will try to demonstrate in the presentation that the reference electrode can be an additional tool for lithium-ion cell diagnosis and cell management. For that, we will base our demonstration on cycling tests performed on instrumented NMC/Graphite pouch cells. The cycling protocol is either formulated as a worldwide harmonized light vehicle test cycle (WLTC) for discharging and 1C for charging and or at CC-CV/CC in charge/discharge and at different temperatures. A correlation with post-mortem analyses will give answer about the consistency of the response in potential given by the reference electrode and for example if it is able to detect the formation of localized metallic lithium.Figure 1 : (left) Electrode balancing during a cell checkup allowing precise monitoring of the cell degradation using individual electrode potentials. (right) Understanding the conditions of the appearance of lithium plating when increasing the charging rate.

Energy Management Strategy for P1 + P3 Plug-In Hybrid Electric Vehicles

In order to simultaneously improve the fuel economy and overall performance of plug-in hybrid electric vehicles (PHEVs), this study selected the P1 + P3 configuration as its research object. Through a configuration analysis of hybrid vehicles, it confirmed the feasibility of P1 + P3 configuration-PHEV operating modes. Based on this, a rule-based control strategy was developed, and simulation models for the entire vehicle and control strategy were constructed in both Cruise and MATLAB/Simulink software. The study conducted simulation analysis by combining three sets of Worldwide Harmonized Light vehicles Test Cycle (WLTC) driving cycles to assess the fuel-saving potential of the dual-motor P1 + P3 configuration. The simulation results showed that the vehicle model was reasonably constructed and the proposed control strategy had good control effects on the entire vehicle. Compared to conventional gasoline vehicles, the P1 + P3 configuration PHEV achieved a 67.4% fuel economy improvement, demonstrating a significant enhancement in fuel efficiency with the introduction of electric motors.

Exhaust Emissions from Euro 6 Vehicles in WLTC and RDE—Part 2: Verification by Experimental Measurement

The subject of assessing exhaust emissions in real driving conditions has been relevant for a long time. Its introduction into approval tests focused attention on the comparative possibilities of tests performed on a chassis dynamometer and in road conditions. The article is a continuation of research on the possibilities of estimating emissions in the Real Driving Emission test based on emission data from Worldwide harmonized Light Vehicles Test Cycles. The first part discussed the possibility of comparing dynamic parameters in these tests, and the second part discussed the possibility of estimating road exhaust emissions. The work was done in two stages: the first stage involved the use of distance-specific emissions in individual parts of the WLTC test, and the second stage involved the use of exhaust emission rates as datasets divided into intervals defined by vehicle speed and acceleration. Comparative tests were performed for conventional vehicles (gasoline, diesel) and hybrid vehicles. A chassis dynamometer was used to carry out WLTC tests and PEMS equipment was used for the RDE tests. The exhaust gas components that had to be measured in road tests, namely: carbon monoxide, carbon dioxide, nitrogen oxides, and the number of particulate matter, were analyzed. Based on the data collected, parameters such as road emissions and the exhaust emissions rate were determined for each phase of the dynamometer test as well as the road test. Because of this, it was possible to compare the distance-specific exhaust emissions of each vehicle in the two emission tests. The comparison resulted in establishing that it is possible to estimate distance-specific exhaust emissions of conventional and hybrid vehicles in road test conditions, using only the results obtained in the approval test (for selected test phases). The research concluded that it is possible to estimate selected RDE test parameters based on the results obtained in the WLTC test for the tested vehicles.

Exhaust Emissions from Euro 6 Vehicles in WLTC and RDE—Part 1: Methodology and Similarity Conditions Studies

The article is an attempt to perform an ecological assessment of passenger cars with various types of engines in road emission tests. The main research problem and, at the same time, the goal was to develop a method for determining the exhaust emissions from motor vehicles in real traffic conditions based on results obtained in homologation tests. The tests were carried out on vehicles equipped with gasoline, diesel, and hybrid engines, and the obtained results were analyzed. All of the selected vehicles were of the same class—passenger cars, with a similar curb weight, similar maximum engine power, and in the same emission class (Euro 6d). The authors compared the dynamic parameters of vehicle motion in established emission tests: Worldwide harmonized Light vehicles Test Cycles and Real Driving Emissions. Four procedures were used to analyze and compare the operating conditions of the vehicles in the WLTC and RDE tests, differing in how the phases in the tests were divided as well as having a different methodology for determining the road emissions in the tests. The procedures were as follows: WLTC (where the test was divided and the determination of the road emission of exhaust gases was carried out according to the standard WLTP procedure), RDE (the road test was divided into sections and the exhaust emission was determined according to the standard RDE procedure), WLTC1+2 (the test was divided into phases: 1 + 2, 3, and 4; a combination of phases 1 and 2 corresponding to the urban section of the RDE test), WLTCRDE (where drive phases were divided and emissions determined in the same way as in the RDE procedure, which assumes the division of the test into sections based on vehicle speed). The implementation of the research task in the form of an algorithm procedure when comparing the dynamic parameters of the movement in the WLTC and RDE tests is the leading goal presented in this article. The division of the WLTC test into sections (urban, rural, and motorway) according to the RDE procedure and also the calculation of the total emissions in the test according to this procedure resulted in obtaining similar road emission values in the test.

A Novel Online Prediction Method for Vehicle Velocity and Road Gradient Based on a Flexible-Structure Auto-Regressive Integrated Moving Average Model

The auto-regressive integrated moving average (ARIMA) model has shown promise in predicting vehicle velocity and road gradient (V–G) for the purpose of constructing power demands in predictive energy management strategies (PEMS) for electric vehicles (EVs). It offers flexibility, accuracy, and computational efficiency. However, the performance of a conventional ARIMA model with fixed structure parameters can be disappointing when the data fluctuate. To overcome this limitation, a novel and flexible-structure-based ARIMA (FS–ARIMA) is proposed in this paper to improve online prediction performance. First, the sliding window method was developed to produce fitting data in real time based on real local historical data, reducing the online computation time. Secondly, the influence of the sliding window sample size, differencing order, and lag in the model on the prediction accuracy was investigated. Based on this, an FS–ARIMA was proposed to improve the prediction accuracy, where an augmented Dickey–Fuller (ADF) test was developed to select the differencing order in real time and the Bayesian information criterion (BIC) was applied to update the model and determine its lag under an optimal sample size. Lastly, to validate the proposed FS–ARIMA, simulations were conducted using two typical driving cycles collected via experiments, as well as the following three typical driving cycles: the New European Driving Cycle (NEDC), the Urban Dynamometer Driving Schedule (UDDS), and the Worldwide Harmonized Light Vehicles Test Cycle (WLTC). The results demonstrated that FS–ARIMA improved prediction accuracy by approximately 41.63% and 42.19% for the velocity and gradient, respectively. The proposed FS–ARIMA prediction model has potential applications in predictive energy management strategies for EVs.

Virtual battery electric vehicle development via 1D tools

Actual testing and prototyping costs make up a significant portion of engineering budgets. Virtual demonstration mainly relies on fast and accurate models with robust performance prediction capability as a cost-effective solution. In this manuscript, GT-Suite, a one-dimensional simulation tool is preferred for developing an isothermal battery model. The developed battery model is implemented to a Light Commercial Vehicle model to run Worldwide Harmonized Light Vehicles Test Cycle. The critical outputs such as state of charge, energy depletion, heat rejection, and generated power are reported. Histograms such as pack current, charge, and discharge current with respect to its nominal capacity (C-rate) are also created to examine the operation capability of battery. In the results, it is seen that the state of charge diminishes from 90% to 82%, as an expected behavior. It is also found that the Parallel Sparse Direct and Multi-Recursive Iterative Linear Solvers methodology reduces the simulation duration by approximately 100 times, in comparison with Singular Value Decomposition Electrical Inversion Scheme. The runtime of the battery pack modeled with the cellular approach combined with the SVD method is more than 90 h. However, the runtime drops to 0.75 h when the PARDISO technique is applied. The method developed within this study can be used for rapid and accurate development of batteries. The verification can be completed in virtual environment and a vital reduction in engineering/prototype/test costs can be guaranteed. The innovation the developed methodology propose is ensuring a fast and reliable performance assessment via taking the holistic effect of integrated models into consideration. Hence it is possible for original equipment manufacturer to completely or gradually eliminate the actual tests and hence prototype costs, test system, and engineer allocations.

Data-driven analysis of battery electric vehicle energy consumption under real-world temperature conditions

Demand for battery electric vehicles has grown significantly in recent years as global oil prices have climbed. The current development of battery electric vehicles is still in its infancy, and its energy consumption also requires great attention. Five same model battery electric vehicles' driving condition data of a certain brand in Tianjin area are collected for a whole year, and their energy consumption is analyzed. The results show that the energy consumption of the actual driving conditions in the four seasons is generally higher than the test results under New European Driving Cycle, and only some vehicles' energy consumption in spring and autumn is consistent with the test results under Worldwide Harmonized Light Vehicles Test Cycle. Energy consumption tends to increase at both ends with the change of temperature. The impact of low temperature is greater, because the air conditioner usage rate and power in Winter are higher than those in Summer. Through principal component contribution rate analysis and K-means clustering calculation of micro-trips, the results show that the average energy consumption in urban, suburban and high-speed driving conditions decreases gradually. Driving habits such as rapid acceleration ratio and rapid deceleration ratio also have an impact on energy consumption. The decrease of energy braking recovery indirectly caused by rapid deceleration ratio increase makes the energy consumption per 100 km increase. And the increase of rapid acceleration ratio leads to an increase in energy consumption per 100 km. Finally, the Spearman rank correlation analysis shows that energy consumption has the greatest correlation with the average vehicle speed and air conditioner usage status, followed by the constant speed proportion, idle speed proportion and temperature, and finally the recovery of braking energy per 100 km, acceleration proportion, deceleration proportion and rapid acceleration proportion.

Assessment of Energy Demand for PHEVs in Year-Round Operating Conditions

In this paper, particular attention is paid to an advanced variant of the plug-in hybrid electric vehicle, known as PHEV, which combines two functionalities: the vehicle’s internal combustion engine (ICE) and the electric motor. The study described herein also presents the influence of factors such as the ambient temperature, vehicle speed and traffic distance on the PHEV’s energy consumption. It has been shown that the vehicle’s range estimated based on its electronic control module (ECU) is about 20% shorter per annum on average for its year-round operation in everyday driving conditions. When analyzing the energy consumption based on the vehicle’s unitary energy consumption model, attention was paid to values that are strongly correlated with traffic and weather conditions. In addition, the authors emphasized that the estimated total energy consumption of a battery electric vehicle (BEV) or hybrid vehicle (PHEV), relative to the normative values arising from the type approval test cycle, deviate from the actual values arising from real driving conditions and often vary substantially. As shown in this paper, the energy consumption intensity of a vehicle is significantly influenced not only by its speed but also by weather conditions, including ambient temperature. In extreme cases, energy consumption intensity can increase by up to 68% relative to a WLTP (Worldwide Harmonized Light Vehicle Test Procedure) cycle.

State machine-based architecture to control system processes in a hybrid fuel cell electric vehicle

This paper presents the development and implementation of a system supervisory controller in a hydrogen-based fuel cell electric vehicle. The controller's primary function is to ensure the safe control of the fuel cell system processes while facilitating coordination among various subsystems, including the balance of plant subsystems, vehicle control unit, diagnosis unit, and powertrain. The supervisory controller comprises of three primary parts: a State Machine, an Optimal Setpoint Generator, and a Power Limit Calculator. The State Machine, which serves as the central part of the supervisory controller, coordinates the fuel cell system's different operational states, including the complex processes of start-up and shutdown. To maximize the fuel cell system's efficiency and minimize the stack's degradation, the Optimal Setpoint Generator produces the subsystem's setpoints by solving an optimization problem and considering the manufacturer's requirements. The Power Limit Calculator assesses the stack's power output capability and calculates the current setpoint for the DC/DC converter. It then provides this data to the Energy Management System (EMS), which oversees the distribution of power between the fuel cell system and the batteries. The proposed fuel cell system supervisory controller is verified using the Worldwide Harmonized Light Vehicles Test Cycles (WLTC) in a real-world car. The designed control structure is implemented in a prototype hydrogen-based electric car at both PowerCell and CEVT facilities under the framework of the INN-BALANCE Horizon 2020 European project.

Particulate emissions from gasoline vehicles using three different fuel injection technologies

Gasoline vehicle emission is a major source of urban particulate matter (PM), particularly ultra-fine PM (≤100 nm), exerting adverse health effects. While the PM emissions from port fuel injection (PFI) and gasoline direct injection (GDI) vehicles have been well addressed in previous studies, few studies have reported the PM emission from the new mixed injection (MI) vehicles. Here, we employed a chassis dynamometer to investigate particle emissions under the Worldwide Harmonized Light vehicles Test Cycles (WLTC) from vehicles with three different fuel injection technologies. The PM and particle number (PN) emission factors (EFs) for MI vehicles are 129.53 μg/km and 1.0 × 1013 #/km, respectively. A trend of PFI > MI > GDI is observed for both PM and PN EFs for vehicles with same emission standard. Notably, the PM emissions of MI vehicles are greatly affected by both fuel injection methods at low and medium speeds, but are more significantly affected by PFI at high speeds. MI vehicle shows much lower PM and PN emissions than other vehicles at low-, medium-, and high-speed phases, but substantial hiher ultra-fine PM emission at extra-high speed phase. This is because the insufficient air supplied by naturally-aspirated engine of MI vehicles in the high-load region leads to incomplete combustion and generate large formation of ultra-fine particles. Besides, the gasoline particulate filter reduces 94.5% of PM emission but produces abundant nucleation-mode particles during regeneration at the extra-high speed phase for MI vehicles. Our study gives an insight to the state-of-the-art engine technology, i.e., MI vehicles, and reveals that this new technology is a feasible alternative for simultaneously reducing pollutant emissions from gasoline vehicles.