Worldwide Harmonized Light Vehicles Test Cycle Research Articles

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.

A metaheuristic algorithm for model predictive control of the oil-cooled motor in hybrid electric vehicles

The current energy management methods for hybrid vehicles are primarily focused on matching the power flow between the engine and the motor. In order to further reduce overall energy consumption and extend the vehicle's lifespan, this paper utilizes model predictive control (MPC) in the energy management of hybrid vehicles to control the energy consumption and temperature of the oil-cooled motor, with a focus on studying the algorithm for solving the optimization problem in MPC. The main work includes: Establishing a simplified model for the oil-cooled motor and training the working condition prediction model based on the worldwide harmonized light vehicles test cycle (WLTC) conditions utilizing long short-term memory (LSTM) recurrent neural network technology. Furthermore, proposing a novel metaheuristic algorithm based on the temporal nature of the problem, multiple hierarchical clustering algorithm (MHCA). In comparison with typical metaheuristic algorithms, the MPC optimized using MHCA exhibits significantly reduced computation time, with an average time of 0.1967s, indicating that using MHCA can theoretically achieve real-time control and eliminate lag in actual control processes. Furthermore, compared to other optimization algorithms, the MHCA algorithm demonstrates the lowest correlation between computation time and operational conditions, highlighting its high robustness in ensuring the safety of practical control.

Numerical study on the performance, combustion characteristics and energy flow distribution of gasoline-powered vehicle under synthetic actual driving test cycle

In order to meet the requirements of vehicle performance prediction and optimization under actual urban road conditions, a synthetic actual driving test cycle (ADTC) is proposed to accurately predict the performance and energy distribution characteristics of vehicles. Meanwhile, a multi-physical field vehicle energy management simulation model is built and verified carefully. Thus, numerical analysis was conducted on the performance of vehicles under ADTC and the characteristics of energy distribution were revealed. The results show that, the brake thermal efficiency (BTE) of the engine under ADTC varies in the range of 14.88 %–30.54 %, that is lower than worldwide harmonized light vehicles test cycle (WLTC) and new European driving cycle (NEDC). Besides, the proportion of combustion loss is WLTC, ATDC and NEDC from the largest to the smallest, which is mainly related to the complexity of speed variation. Based on this, unlike traditional testing cycles, ADTC synthesized from local actual road test data can more accurately evaluate the actual performance of vehicles under local characteristic road conditions and provide guidance for deep optimization, which is beneficial for energy conservation and emission reduction.

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.

Optimization of Realistic Accelerated Stress Tests for PEM Fuel Cells Using Standardized Automotive Driving Cycles

Proton exchange membrane (PEM) fuel cells have so far not achieved the targeted lifetime necessary to be an economical alternative for automotive applications. This is mainly caused by their high degradation rates. To better understand the degradation mechanisms, the use of accelerated stress tests (ASTs) at the component level is widespread. However, it is still a challenge to transfer their results to real-world applications.To address this problem, the approach of this work is to derive load profiles from standardized driving cycles for passenger cars. In combination with a fuel cell system model that is used to define the operating conditions of the media, such as temperature or pressure, a test procedure is obtained that results in a realistic degradation of the fuel cell. The proposed test procedure exposes the PEM fuel cell (PEMFC) to the most important degradation conditions in mobile applications. Dynamic operation is responsible for deterioration processes within the PEMFC and is the underlying cause of typical hygrothermal and voltage cycling events. Such cycling also occurs during start-stop operations, causing severe damage to the PEMFC catalysts. In addition, idling conditions may prompt open-circuit voltage (OCV) conditions in the PEMFC, resulting in dissolution of the Pt catalyst and subsequent chemical degradation of the PEM. Additionally, high load operation may also lead to catalyst degradation. Finally, the inertia of the auxiliary components, such as the humidifier, during load changes causes not ideal conditions, such as relative humidity changes, resulting in further mechanical stress on the membrane.Due to the targeted PEMFC lifetime of up to 8,000 hours for automotive applications [1], realistic degradation test procedures have a very long test duration, making them particularly challenging and costly to conduct. To overcome this hurdle, the load profile of the proposed test procedure is shortened by using an in-house developed optimization algorithm. For this purpose, four operation modes are defined as degradation-accelerating: voltage cycling, relative humidity (RH) cycling, OCV/idling, and high load operation. Based on state-of-the-art AST procedures, a sub-algorithm is developed for each operating mode. Such sub-algorithms are capable of identifying all occurrences within a given test procedure that may lead to accelerated fuel cell degradation. For each sub-algorithm, the degradation parameters (namely upper and lower potential limits for voltage cycling, membrane tensile stress for RH cycling, fluoride emission rate for OCV/idling, and voltage degradation rate for high load) are specified by the user. This allows the comparison of different fuel cells in terms of their resistance to conventional degradation mechanisms.The developed algorithm can be used to optimize various driving cycles such as the worldwide harmonized light vehicles test cycle (WLTC) for various mobile applications. All cycle events identified as accelerating degradation are connected with a point connector function based on the fuel cell polarization curve. This ensures moderate voltage steps between the identified phases and thus prevents the algorithm from inducing further degradation of the fuel cell.The optimization algorithm obtained can reduce the test duration by up to 40%. The method can be repeated frequently during AST operation by repeatedly measuring the polarization curve to consider the continuous degradation in the test procedure. Decreasing fuel cell performance leads to more severe voltage drops during load changes and thus an increase of degradation-accelerating phases over the test period. Furthermore, start-stop ASTs developed by Bisello et al. [2] are also included due to their strong contribution to fuel cell degradation in automotive applications.Finally, the optimized cycles are analyzed using two simulation tools to evaluate the degradation. The first tool by Ao et al. [3] uses the electrochemical surface area (ECSA) to quantify the proceeding degradation during cycle operation. A characteristic voltage and the voltage change rate are extracted from the driving cycle. For both values, the resulting decrease in ECSA is modeled and aging is defined as the ECSA loss rate. The second tool by Pei et al. [4] examines the driving cycle according to the four degradation-inducing conditions and combines them with experimentally determined degradation rates. Both tools allow an analysis of the degradation derivative between the initial and optimized test procedures. By adjusting the degradation rate for the different degradation mechanisms, the AST procedure is improved in terms of its representativeness for real-world applications.

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.

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.

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.

Exhaust aftertreatment device-derived ammonia emissions from conventional and hybrid light-duty gasoline vehicles over different driving cycles

Ammonia emissions from motor vehicles have great effect on air pollution and human health in urban areas. Recently, many countries have focus on ammonia emission measurement and control technologies for light-duty gasoline vehicles (LDGVs). To analyze ammonia emission characteristics, three conventional LDGVs and one hybrid electric light-duty vehicle (HEV) were evaluated over different driving cycles. The average ammonia emission factor at 23℃ was 4.5 ± 1.6 mg/km over Worldwide harmonized light vehicles test cycle (WLTC). Most ammonia emissions mainly concentrated in low and medium speed sections at cold-start stage, which were related to rich burn conditions. The increasing ambient temperatures led to the decrease of ammonia emissions, but high load caused by extremely elevated ambient temperature led to obvious ammonia emissions. The ammonia formation is also related to three-way catalytic converter (TWC) temperatures, and underfloor TWC catalyst could eliminate ammonia partly. The ammonia emission from HEV, which are significant less than LDGV, corresponded to the engine working state. The large temperature difference in the catalysts caused by power source shifts were the main reason. Exploring the effects of various factors on the ammonia emission is beneficial for revealing the instinct formation conditions, providing theoretical support for the future regulations.

Performance Comparison of Si IGBT and SiC MOSFET Power Module Driving IPMSM or IM under WLTC

The cumulative inverter losses and power consumption of a silicon insulated gate bipolar transistor (Si IGBT) and three types of silicon carbide metal-oxide-semiconductor field-effect transistors (SiC MOSFETs) were evaluated on an electric motor test bench under a worldwide harmonized light vehicles test cycle (WLTC). SiC MOSFETs showed higher performance than Si IGBT regardless of the motor type and test vehicles. In the case of driving an interior permanent magnet synchronous motor (IPMSM), the latest 4th generation SiC MOSFET (SiC-4G) in ROHM has the lowest inverter loss and energy consumption compared with the other generations. In the case of driving an induction motor (IM), on the other hand, the 2nd generation SiC MOSFET (SiC-2G) in ROHM has the best energy consumption despite the fact that the inverter losses of SiC-2G are slightly larger than the loss of SiC-4G. The latest or later generation power device does not necessarily contribute to better performance in a total system by simply replacing early power devices.

Energy-Saving Optimization for Electric Vehicles in Car-Following Scenarios Based on Model Predictive Control

In this paper, an economy-oriented car-following control (EOCFC) strategy is proposed for electric vehicles in car-following scenarios. Specifically, a controller based on model predictive control (MPC) is developed to optimize the host vehicle’s speed for better energy economy while ensuring good car-following performance and ride comfort. The vehicle’s energy consumption is accurately quantified in the form of demand power, which is incorporated in the cost function for energy optimization. The proposed EOCFC strategy is evaluated using three standard test cycles, i.e., New European Driving Cycle (NEDC), Urban Dynamometer Driving Schedule (UDDS) and Worldwide Harmonized Light Vehicles Test Cycle (WLTC), in comparison with a typical multi-objective adaptive cruise control strategy. The evaluation results demonstrate that the proposed EOCFC improves the energy economy of the host vehicle by 0.53%, 3.33% and 1.51%, under the NEDC, UDDS and WLTC test cycles respectively.

WLTC measuring driving cycle (power reserve measurement procedure for hybrids and electric vehicles)

Problem. The most effective energy management strategies for hybrid vehicles and electric vehicles are optimization-based strategies. These strategies require prior knowledge of the driving cycle, which is not easy to predict. Goal. The goal is to combine the Worldwide harmonized light vehicles test cycle (WLTC) with short trips on small sections with real traffic levels to predict the energy and fuel consumption of hybrid vehicles and electric vehicles. Methodology. Research methods are experimental and mathematical. First of all, eight characteristic parameters are extracted from real speed profiles used on urban road sections in the city of Kharkiv under various road conditions, as well as on short WLTC trips. The minimum distance algorithm is used to compare parameters and determine three traffic levels (heavy, medium, and low traffic) for short WLTC trips. Thus, for each route determined using Google Maps, the energy and fuel consumption of hybrid vehicles and electric vehicles are estimated using short trips by the WLTC, adjusted for distances and traffic levels. In addition, a numerical model of the vehicle was implemented. It was used to test the accuracy of predicting fuel and energy consumption in accordance with the proposed methodology. Originality. For the methodology using only GM information is required as input data; no other device or software is required. This aspect makes the methodology extremely economical. Then, the algorithm regulating traffic levels shown by GM is unique and valid in all urban centers. This aspect makes the methodology universal. WLTC takes into account the driving styles of drivers around the world, so the methodology can be applied to any car driver. Prediction accuracy can be increased by taking into account other input information, such as the distribution of traffic light signals or the driver's typical gear shifting style. Results. The results are promising, as the average absolute percentage error between experimental driving cycles and projected ones is 3.89 % for fuel consumption, increasing to 6.80 % for energy consumption. Practical value. The possibility of energy forecasting and fuel consumption for a hybrid vehicle and an electric vehicle makes it possible to develop energy consumption management systems for HEVs that can manage the energy reserve to ensure full travel by electric traction in limited traffic zone (LTZ) or minimize local air pollution; increase the service life of energy reserves (usually batteries) by maintenance costs and disposal problems reducing; optimize the transmission-use efficiency due to fuel consumption and pollutants emissions reduction.

Influence of Driving Cycles on Emission Parameters of Vehicles

The vehicle emissions tests, which are performed using the Portable Emissions Measurement Systems (PEMS), indicate that the diesel cars produce several times more NOx emissions on the road than during certification realised according to the methodology New European Driving Cycle (NEDC). This fact initiated the infamous "dieselgate" affair. It is a well-known reality that the current emission limits are very strict and therefore a serious problem arises for the motor car producers to meet the demanding emission requirements. The main task of the presented research work was testing of 10 passenger cars with regard to true values of their exhaust gas emissions. These cars were equipped with the diesel engines and gasoline engines that officially meet the Euro 4-6 standards. Based on the measured results it was found that the real NOx emissions of the diesel engines significantly exceed the values measured by NEDC. The results obtained using the NEDC cycle also contradict the findings from two alternative driving cycles: CADC (Common Artemis Driving Cycle; Artemis - Assessment and Reliability of Transport Emission Models and Inventory Systems) and WLTC (Worldwide harmonized Light vehicles Test Cycle).

Ammonia Slip Estimation Based on Ammonia Slip Catalyst Control-Oriented Modeling and On-Board Diagnostics NOX Sensor Cross-Sensitivity Analysis

Abstract The incoming emission regulations for internal combustion engines are gradually introducing new pollutant species, which require greater complexity of the aftertreatment systems concerning layout, control, and diagnostics. This is the case of ammonia, which is injected into the exhaust gas through urea injections for NOx abatement in selective catalytic reduction (SCR) systems and can also be generated in three-way catalysts. However, ammonia slip requires its oxidation on a dedicated ammonia slip catalyst (ASC). The set composed of the urea injection system, SCR, and ASC requires control tools to ensure high NOx conversion efficiency and compliant ammonia slip under real driving conditions. These tasks are based on the use of NOx sensors ZrO2 pumping cell-based, which has the disadvantage of high cross-sensitivity to ammonia that can affect the measurement of NOx and compromise the SCR-ASC control strategies. The present work proposes a methodology to predict ammonia and NOx tailpipe emissions. For this purpose, a control-oriented ASC model was developed to use its ammonia slip prediction to determine the cross-sensitivity correction of the tailpipe NOx sensor. The model is based on a simplified solution of the transport equations of the species involved in the ASC reaction mechanism. The model was calibrated using steady- and quasi-steady-state tests performed in a Euro 6c engine. Finally, the performance of the proposed methodology to predict NOx and ammonia emissions was evaluated against experimental data corresponding to worldwide harmonized light vehicles test cycles (WLTC) applying different urea dosing strategies.

Heating performance of heat pump air conditioning system for battery electric vehicle with different refrigerants

To improve the heating performance of Heat Pump Air Conditioning System (HPACS) of Battery Electric Vehicle (BEV) and reduce the greenhouse effect of refrigerants, the performances of multiple refrigerants in different modes and conditions are analyzed in this paper. Based on Worldwide Harmonized Light Vehicles Test Cycle (WLTC) dynamic conditions, the impact of heating on battery State of Charge (SOC) is analyzed in HPACS and Positive Temperature Coefficient (PTC) combined heating mode. To better evaluate the contribution of refrigerants to the greenhouse effect, Total Equivalent Warming Impact (TEWI) is used to evaluate multiple refrigerants. The results show that heating capacity increases with the increment of compressor power and air velocity. As compressor power decreases and air velocity increases, COP increases. R32 and R744 are the optimal refrigerants. When the ambient temperature is between −10°C and −15°C under WLTC conditions, the SOC reduction of R32 system is 20.8%–21.1% less than that of R134a system. When the ambient temperature is between −20 and −25°C, the SOC reduction of R744 system is 21.4%–21.6% less than that of R134a system. And the carbon dioxide emissions of R32 and R744 systems are 35.1%, 66.9%, 39.2%, and 55.5% less than that of R134a system, respectively.

Model Development for State-of-Power Estimation of Large-Capacity Nickel-Manganese-Cobalt Oxide-Based Lithium-Ion Cell Validated Using a Real-Life Profile

This paper investigates the model development of the state-of-power (SoP) estimation for a 43 Ah large-capacity prismatic nickel-manganese-cobalt oxide (NMC) based lithium-ion cell with a thorough aging investigation of the cells’ internal resistance increase. For a safe operation of the vehicle system, a battery management system (BMS) integrated with SoP estimation functions is crucial. In this study, the developed SoP model used for the estimation of power throughout the lifetime of the cell is coupled with a dual-polarization equivalent-circuit model (DP_ECM) for achieving the precise estimation of desired parameters. The SoP model is developed based on the pulse-trained internal resistance evolution approach, and hence the power is estimated by determining the rate of internal resistance increase. Hybrid pulse power characterization (HPPC) test results are used for extraction of the impedance parameters. In the DP_ECM, Coulomb counting and extended Kalman filter (EKF) state estimation methods are developed for the accurate estimation of the state of charge (SoC) of the cell. The SoP model validation is performed by using both dynamic Worldwide harmonized Light vehicles Test Cycles (WLTC) and static current profiles, achieving promising results with root-mean-square errors (RMSE) of 2% and 1%, respectively.

Experimental study on combustion and emissions of a compression ignition engine fueled with gasoline

Gasoline compression ignition (GCI) is an effective way to achieve both high thermal efficiency and low emission. The combustion and emission performances of GCI and DCI (diesel compression ignition) were compared on a 2.0 L diesel engine equipped with Three-way catalyst-Lean NOx trap/Passive selective catalytic reduction (TWC-LNT/PSCR) aftertreatment system. In order to further clarify the advantages and disadvantages of GCI, this paper first studies the combustion and emission at 1500 rpm and braking average effective pressure (BMEP) of 4–9 bar. Secondly, six small map points of worldwide harmonized light vehicles test cycle (WLTC) are studied. The results show that the braking thermal efficiency (BTE) of GCI is lower than that of DCI at low load. When BMEP is greater than 5 bar, the BTE of GCI is significantly improved. GCI achieves a maximum BTE of 43%, which is 3% higher than DCI. Compared with DCI, the NOx emission of GCI is slightly lower, the smoke emission of filter smoke number (FSN) is significantly improved, and the CO and HC emissions are significantly increased. GCI engine equipped with TWC-LNT/PSCR system with high aftertreatment efficiency has the potential to meet China’s VI B emission regulations.

Emissions of Euro 6 Mono- and Bi-Fuel Gas Vehicles

Compressed natural gas (CNG) and liquefied petroleum gas (LPG) are included in the group of promoted transport fuel alternatives in Europe. Most studies on emissions factors are based on old technology CNG and LPG fueled vehicles. Furthermore, there are not many data at low ambient temperatures, on-road driving, or unregulated pollutants, such as ammonia (NH3). In this study we measured the emissions of one Euro 6b CNG light commercial vehicle, one Euro 6b and one Euro 6d-Temp bi-fuel LPG passenger car, one Euro 6d-Temp bi-fuel CNG passenger car, and four Euro 6d-Temp CNG passenger cars. Tests included on-road testing and worldwide harmonized light vehicles test cycles (WLTC) in the laboratory with cold and hot engine, at 23 °C and −7 °C. The results showed 10–23% CO2 savings in gas modality compared to gasoline, lower CO and particle number emissions, and relatively similar total and non-methane hydrocarbons and NOx emissions. The ammonia emissions were high for all vehicles and fuels; higher than gasoline and diesel vehicles. The results also showed that, following the introduction of the real-driving emissions regulation, even though not applicable to the examined vehicles, Euro 6d-Temp vehicles had lower emissions compared to the Euro 6b vehicles.