Driving Cycle Test Research Articles

Intelligent Robust Controllers Applied to an Auxiliary Energy System for Electric Vehicles

This paper presents two intelligent robust control strategies applied to manage the dynamics of a DC-DC bidirectional buck–boost converter, which is used in conjunction with a supercapacitor as an auxiliary energy system (AES) for regenerative braking in electric vehicles. The Neural Inverse Optimal Controller (NIOC) and the Neural Sliding Mode Controller (NSMC) utilize identifiers based on Recurrent High-Order Neural Networks (RHONNs) trained with the Extended Kalman Filter (EKF) to track voltage and current references from the converter circuit. Additionally, a driving cycle test tailored specifically for typical urban driving in electric vehicles (EVs) is implemented to validate the efficacy of the proposed controller and energy improvement strategy. The proposed NSMC and NIOC are compared with a PI controller; furthermore, an induction motor and its corresponding three-phase inverter are incorporated into the EV control scheme which is implemented in Matlab/Simulink using the “Simscape Electrical” toolbox. The Mean Squared Error (MSE) is computed to validate the performance of the neural controllers. Additionally, the improvement in the State of Charge (SOC) for an electric vehicle battery through the control of buck–boost converter dynamics is addressed. Finally, several robustness tests against parameter changes in the converter are conducted, along with their corresponding performance indices.

Energy management of fuel cell hybrid electric bus in mountainous regions: A deep reinforcement learning approach considering terrain characteristics

Environmental characteristics, particularly in mountainous regions with significant slopes, substantially impact vehicle power demand and fuel consumption, thereby influencing both fuel economy and vehicle lifespan. However, existing research lacks energy management strategies specifically designed for these challenging terrains. This study presents an innovative energy management strategy (EMS) for fuel cell hybrid electric buses (FCHEB) utilizing a deep reinforcement learning algorithm considering terrain characteristics. A comprehensive model that incorporates road fluctuations and steep slopes was developed to accurately represent the terrain features of mountainous regions. Driving cycle and road condition data specific to hydrogen fuel cell buses in these areas were collected and processed as for the training sets of EMSs. Subsequently, an EMS based on the Proximal Policy Optimization (PPO) algorithm was devised to address the unique characteristics of these mountainous regions. Simulation results indicate a 26.16 % improvement in fuel economy and a 44.98 % enhancement in convergence efficiency compared to traditional methods that do not consider road slopes. Further validation through new bus route trials and standard driving cycle tests confirms the strategy's robustness and adaptability.

Degradation mechanism analysis of a fuel cell stack based on perfluoro sulfonic acid membrane in near-water boiling temperature environment

Perfluoro sulfonic acid (PFSA)-membrane fuel cells for heavy-duty vehicle applications require elevated operating temperature, which may negatively impact the durability of proton exchange membrane fuel cells (PEMFCs). This study evaluated a 10 kW stack by conducting a 100 h driving cycle test in near-water boiling temperature. Performance degradation varied in different regions of the membrane electrode assembly (MEA), with severe degradation observed near the coolant outlet (near-water boiling temperature) and the hydrogen inlet. The rapid degradation of the MEA was examined through an analysis of its electrochemical properties, morphology, pore size, mechanical properties, degree of graphitization, and elemental composition in different regions. It was found that elevated temperatures accelerated the degradation of the PEM, leading to polymer melting, corrosion of carbon support, and the aggregation and growth of Pt nanoparticles within the MEA, ultimately the MEA structural breakdown. the MEA supporting structural breakdown. The combination of high temperatures and the loss of fluoride ions from the MEA resulted in damage to the bipolar plate coatings. Under the collaborative influence of these factors, the average voltage degradation rate of the PEMFC stack was 1.44 %@1.0 A cm−2, and the single cell with the worst degradation was up to 8.6 %@1.0 A cm−2.

A Novel Multi-Stage Stochastic Estimation Algorithm for Estimating the Parameters of the Extended Single Particle Model of a Lithium-Ion Battery

Lithium-ion batteries (LiBs) are commonly used as energy storage for many applications due to LiBs’ high energy density, long cycle life, and reliable manufacturing processes. Hence, effective performance management of LiBs is important and is implemented via Battery Management Systems (BMS)[1]. A BMS also maximises the safety and lifetime of battery packs; and therefore, needs to estimate the internal battery states including State of Charge (SoC) and State of Safety (SoS). Accurate estimation of battery SoX can be realized with the use of an accurate battery model [2].From the different types of battery models, electrochemical models (EMs) are considered the most accurate type of cell-level model [2]. EMs are sets of coupled-partial differential equation systems, where each equation is derived from the physics occurring in the cell. Of the different EMs, the partial two-dimensional model (P2D) is considered the most accurate cell-level model. However, the P2D model is complex and has no analytical solution, thus requiring computationally expensive iterative solvers for implementation. This makes practical implementation of the P2D model on BMS platforms challenging. Hence, most approaches seen in the literature prefer the use of reduced order versions (ROM) of the P2D model, that require far less computational power for implementation [1].One such ROM is the extended Single Particle Model (eSPM) which differs from the P2D model in considering each electrode to be modelled by a single spherical particle and charge conservation in the electrolyte to follow a simplified linear relationship. These assumptions help the model be accurate up to a 2C operational current, and enable an analytical solution to be derived using direct solving of the eSPM equations [2]. Due to fast-forward computation and reasonable accuracy, we’ve chosen the eSPM model for parameter estimation (PE). For accurate measurements of the model parameters, expensive and invasive experimental techniques are typically required. Non-destructive PE methods have a growing interest in the literature as they are not only non-invasive but also enable real-time dynamic PE to be done on a BMS [3].PE of EMs can be categorized under two approaches: deterministic methods and stochastic methods. Deterministic methods require a form of gradient descent to be used, wherein partial derivatives of the parameters of the model need to be calculated. This approach tends to be computationally expensive and tends to converge to local minima instead of searching the parameter space thoroughly. Hence, stochastic approaches incorporate randomness, thus searching the entire parameter space for the global optima [3]. Multiple different stochastic approaches have been employed for PE of EMs [4]. In this work, we compare and contrast some of the commonly used approaches and propose a novel multi-stage approach based on a combination of different stochastic PE algorithms.The proposed algorithm (see Figure (1)) requires three sets of data as inputs: the cell chemistry, constant current discharge data and the parameter boundaries of the eSPM model. Using these definitions, the first stage of the algorithm estimates a parameter set that has a reasonable level of accuracy i.e. less than 4% root mean square error (RMSE). The second stage of the algorithm does region-by-region optimisation, where parameters sensitive to different regions of the discharge curve are varied until the overall error is reduced to less than 1% RMSE. The proposed approach significantly reduces the estimation processing time compared to the ‘brute-force’ approach and is flexible in selecting different algorithms for each stage.Thus, contributions of this paper are briefly explained as follows: A performance comparison of the proposed algorithm with different stochastic estimation algorithms including the genetic algorithm, the particle swarm optimisation algorithm and the simulated annealing algorithm.A fast and adaptable multi-stage PE algorithm for the LiB eSPM model, with average estimation speeds of less than fifteen minutes for a typical BMS processor, with accuracy of less than 1% RMSE error.Validation of the algorithm with dynamic drive-cycle test data. [1] L. Lu et al, “A review on the key issues for lithium-ion battery management in electric vehicles,” Journal of Power Sources, vol. 226. pp. 272–288, Mar. 15, 2013. doi: 10.1016/j.jpowsour.2012.10.060.[2] S. Abada et al, “Safety focused modeling of lithium-ion batteries: A review,” J Power Sources, vol. 306, pp. 178–192, Feb. 2016, doi: 10.1016/J.JPOWSOUR.2015.11.100.[3] E. Miguel et al, “Review of computational parameter estimation methods for electrochemical models,” Journal of Energy Storage, vol. 44. Elsevier Ltd, Dec. 15, 2021. doi: 10.1016/j.est.2021.103388.[4] A. Jokar et al, “An Inverse Method for Estimating the Electrochemical Parameters of Lithium-Ion Batteries,” J Electrochem Soc, vol. 163, no. 14, p. A2876, Oct. 2016, doi: 10.1149/2.0191614JES. Figure 1

Design and development of model for proton exchange membrane fuel cell

Fuel prices are rising, and fossil fuel resources are depleting. This fuel is consumed by conventional fuel vehicles, which contributes to increased greenhouse gas emissions and air pollution. Various vehicle technologies have been introduced in modern times. Among these innovations, fuel cell vehicle technology stands out due to its ease of use, robustness, and simplicity. The primary energy source for this system is hydrogen. Fuel cells use hydrogen to generate electricity through a chemical process that does not involve combustion. The purpose of this paper is to describe the design and development of a simulation model for a fuel cell (proton exchange membrane type) vehicle using mathematical equations and the MATLAB/Simulink R2020a software, taking into account all voltage losses and temperature variations of the proton exchange membrane (PEM) type fuel cell. The designed model is validated in two ways: first by generating V-I characteristics for the designed PEM-type fuel cell, and then by performing a performance test in Simulink using the driving cycle on one of the fuel cell vehicles. The obtained simulated results are nearly identical to the expected results, and the designed PEM-type fuel cell performed well in all real-time driving cycle test conditions.

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.

A test bench for the assessment of flow meters accuracy for fuel consumption measurement in highly dynamic drive cycle tests

The transport sector is one of the most significant contributors in terms of NOx, particulate matter and net CO2 emission. In order to mitigate this environmental impact, severe homologation limits have been introduced by the different regional Authorities. Both pollutant emissions and CO2 production of vehicles are measured during standardized laboratory test cycles and road tests, both characterized by relatively abrupt powertrain load and fuel rate transients. This dynamic approach results in severe issues in terms of fuel flow measurement capabilities as flow meters are typically calibrated in steady fuel flow conditions. In the present activity, an experimental test bench was developed with the aim of offering a flexible and affordable test system to evaluate the performances of different flow meters in dynamic conditions. The test bench consists of a complete fuel injection system managed to reproduce an assigned cycle; by this approach the highly-dynamic engine-like fuel flow rate is replicated, offering the opportunity to compare the accuracy of the flow meter under test with both a master Coriolis-type flow meter and the nominal fuel flow time-profile. This bench allows the execution of flow meter accuracy tests in controlled operating conditions with reduced efforts with respect to conventional vehicle test benches and with a high reproducibility level.

Thorough Analysis of the Reliability of Measurements on Chassis Roller Dynamometer and Accurate Energy Consumption Estimation for Electric Vehicles

The energy consumption of the electric test vehicle on the roller dynamometer test bench is thoroughly investigated in this work. Initially, we extensively examined the relevant testing approaches on chassis dynamometer test benches. Then, we presented the main issues and limitations of this type of testing facility. After that, an experimental analysis was performed by recording the NEDC and the WLTP-class 2 standard driving cycle test data. It was found that the power consumption associated with specific test maneuvers could be accurately simulated via the roller dynamometer system. However, the experimental test results showed some disparities compared to the expected results from the simulation models. These findings supported identifying the dynamic behavior resulting from the tire and roller contact. The LuGre friction model provided the solution, incorporating friction-related physical models such as stick-slip and the Stribeck effect. In addition, adjustments were made to the LuGre model to consider the dynamic influences of the roller on the tire. Finally, we created a systematic estimation model for energy consumption by integrating the boundary conditions of the test bench, a generic model for energy estimation of electric vehicles, and the proposed tire–roller contact model. The overall model requires only the measured traction speed of the test vehicle and the auxiliary energy losses as physical inputs. The results proved that the proposed model enabled the estimation of the total power consumption with high accuracy.

Series Motor Four Quadrants DC Chopper For DC Drive Electric Vehicle Driving Cycle Test

The most common need for electric car is to be able to carry a maximum load of 850-1300 kg. The expected maximum speed of the electric vehicle (EC) is about at 110-120 km/h and the acceleration is for 60Km/h in less than 10 seconds. To determine whether the DC Drive EV performance is up to a standard requirement for the end user’s such as maximum speed, cruising speed, acceleration, deceleration, stability, safety, etc., an EV’s full design must go through some sort of testing. This study examined the use of a series motor and a four-quadrant DC chopper (FQDC) for typical driving cycle tests like the New European Driving Cycle to test these special requirements of a DC drive electric automobile (NEDC). Using Matlab/Simulink software and vehicle dynamics equations, a simulation model of the DC Drive EV is created in order to examine and investigate the performance under the NEDC test applied to the FQDC in order to make sure that it complies with the NEDC requirements. From the test findings, it can be inferred that the FQDC is capable of meeting all NEDC standards.

Characterisation of a Coriolis flow meter for fuel consumption measurements in realistic drive cycle tests

When testing light-duty and heavy-duty vehicles on chassis dynamometers, as in the WLTP, or engines on engine test benches, as in the WHDC, it is required to measure the fuel consumption. In the preferable case, the measurement of the fuel consumption is carried out with suitable flow meters. These require high measurement accuracy in a wide flow range, independent of the fuel type, as the flow rate range is often very large and depends on the power range of the vehicle engines. Moreover, the fuel flow rate in the test cycles is very dynamically related to the loads. In the scope of the ongoing EMPIR Joint Research Project 20IND13 SAFEST the dynamic flow behaviour as well as the measurement accuracy of flow meters for different types of fuels are investigated. This paper presents first results from the realisation of dynamic flow profiles, and flow measurements with a Coriolis Flow Meter with different representative fuels in a wide density and viscosity range and a wide flow rate range at different fuel temperatures.

Double-Layer Energy Management for Multi-Motor Electric Vehicles

This paper proposes a double-layer energy management system (EMS) for electric vehicles driven by multiple permanent magnet synchronous motors. The system minimizes energy consumption and ensures safe longitudinal motion. The inner-layer distributes torques and flux currents by minimizing the motor input power. The outer-layer generates the total torque command by controlling the aggregated motor speed via a disturbance observer-based controller. A design condition that sufficiently guarantees the system's L <sub xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sub> stability is presented. The condition is independent of the torque distribution ratios and can be checked conveniently via passivity notation without linearizing the total system. Various validation tests were performed using a three-wheel recreational electric vehicle (EV) platform. The advantage of the double-layer EMS has been compared with several EMSs proposed recently in the literature. Critical testing scenarios were employed, including sharp change in road friction during high acceleration. Test results reveal that, regardless of such condition, the double-layer EMS can prevent the wheel slip, thereby significantly reducing energy consumption. The New European Driving Cycle test was also conducted to demonstrate the merit of simultaneously optimizing torque distribution ratios and motor flux-currents.

High-Precision and Robust SOC Estimation of LiFePO4 Blade Batteries Based on the BPNN-EKF Algorithm

The lithium iron phosphate (LiFePO4) blade battery is a long, rectangular-shaped cell that can be directly integrated into battery pack systems. It enhances volumetric power density, significantly reduces costs, and is widely utilized in electric vehicles. However, the flat open circuit voltage and significant polarization differences under wide operational temperatures are challenging for accurate voltage modeling of battery management systems (BMSs). In particular, inaccurate state of charge (SOC) estimation may cause overcharging and over-discharging risks. To accurately perceive the SOC of LiFePO4 blade batteries, a SOC estimation method based on the backpropagation neural network-extended Kalman filter (BPNN-EKF) algorithm is proposed. BPNN is a neural network model that utilizes the backpropagation algorithm to update model parameters, while EKF is an optimal estimation algorithm. Firstly, dynamic working condition tests, including the New European Driving Cycle (NEDC) and high-speed working (HSW) condition tests, are conducted under a wide temperature range (−25–43 °C). HSW conditions refer to a simulated operating condition that mimics the driving of an electric vehicle on a highway. The minimum voltage of the battery system is used as the output for training the BPNN model. We derive the Kalman gain by combining the BPNN output voltage. Additionally, the EKF algorithm is employed to correct the SOC value using voltage error information. Concerning long SOC calculation intervals, capacity errors, initial SOC errors, and current and voltage sampling errors, the maximum SOC estimation RMSE is 3.98% at −20 °C NEDC, 3.62% at 10 °C NEDC, and 1.68% at 35 °C HSW. The proposed algorithm can be applied to different temperatures and operations, demonstrating high robustness. This BPNN-EKF algorithm has the potential to be embedded in electric vehicle BMS systems for practical applications.

Recursive ARMAX-Based Global Battery SOC Estimation Model Design using Kalman Filter with Optimized Parameters by Radial Movement Optimization Method

Precise estimating battery state-of-charge (SOC) is an important factor in determining vehicle range in an electric or hybrid vehicle. However, the parameters of battery are highly dependent on environmental conditions such as temperature. One of the main drawbacks in battery SOC estimation methods by using existing approaches is inability to adapt to the variable environmental operating conditions. In this study, a hybrid technique which combines the Kalman filter and recursive autoregressive exogeneous moving average model is proposed for the online battery parameters estimation. To design robust SOC estimation to varying battery parameters, the parameters of Kalman filter are optimized with the radial movement optimization metaheuristic algorithm. The proposed approach is implemented considering both different temperatures (0 °C, 25 °C, and 40 °C) and driving test cycles, UDDS, LA-92, and US06. Second-order RC battery equivalent model-based approach are compared with the proposed method, and the result shows that the proposed method is better than the conventional method in the aspects of average statistical parameters for all the driving test cycles.

Quantitative diagnosis of PEMFC membrane humidity with a vector-distance based characteristic mapping approach

Membrane dehydration or flooding fault is one of the main causes of performance degradation for Proton Exchange Membrane Fuel Cell (PEMFC). Effective and accurate diagnosis of membrane humidity faults is necessary to ensure the optimal operation of PEMFC. However, the research gap lies in the quantitative diagnosis of membrane humidity values under complex working conditions of vehicle application. In this paper, characteristic spaces describing membrane humidity are established as multi-matrices consisting of the amplitude spectrum extracted from output voltage and pressure drop in PEMFC under different vehicle working conditions. In the multi-matrix, the relationship between the membrane humidity fault levels and the amplitude spectrum can be described as characteristic vectors. Then, a vector-distance based mapping approach under multi-information characteristic vectors is developed to describe the relationship between characteristic vectors and humidity values. Consequently, the membrane humidity of the PEMFC can be quantitatively evaluated with the projection length between characteristic vector and humidity-change vector, with which the humidity value of PEMFC membrane can be obtained under different working conditions in real-time. The performance of the proposed a vector-distance based characteristic mapping approach is verified by New European Driving Cycle (NEDC) and World Light Vehicle Test Procedure (WLTP) working conditions. The diagnostic results show that the quantitative humidity diagnosis strategy can achieve diagnostic accuracy of 98.16% and 92.24% under NEDC and WLTP working conditions, compared with the single information of output voltage-based and pressure drop-based diagnosis strategy, the accuracy is improved by 4.37% and 4.08% respectively.

Design and Performance Investigation of a Vehicle Drive System with a 12/10 Flux-Switching Permanent Magnet Motor

The performance of a drive system with a flux-switching permanent magnet (FSPM) motor was studied through tests on a commercial electric vehicle (CEV). A practical design and an optimization method for the FSPM motor were proposed for a light-duty CEV. The initial dimensions of the motor were calculated by theoretical equations referring to a permanent magnet synchronous motor. Then, optimization was conducted through a response surface methodology (RSM) and a genetic algorithm (GA) based on three-dimensional finite element analysis (3D-FEA). With the optimized parameters, a prototype of the FSPM drive system was manufactured and assembled into an actual CEV. The performance of the CEV was investigated on an automobile test platform. The experimental results show that the FSPM drive system could drive the CEV properly. The high-efficiency running time of the FSPM motor accounted for 84% of the total time tested, which shows great potential for practical application in CEVs. However, the experimental results also show that the FSPM motor faced problems of large speed deviation and high-temperature rise during the driving cycle test, which should be fully addressed for practical applications.

Research on the hydrogen consumption of fuel cell electric vehicles based on the flowmeter and short-cut method

Energy consumption is essential for evaluating the competitiveness of fuel cell electric vehicles. A critical step in energy consumption measurement is measuring hydrogen consumption, including the mass method, the P/T method, and the flowmeter method. The flowmeter method has always been a research focus because of its simple operation, low cost, and solid real-time performance. Current research has shown the accuracy of the flowmeter method under specific conditions. However, many factors in the real scenario will influence the test result, such as unintended vibration, environment temperature, and onboard hydrogen capacity calibration. On the other hand, the short-cut method is also researched to replace the run-out method to improve test efficiency. To evaluate whether the flowmeter method basing on the short-cut method can genuinely reflect the hydrogen consumption of an actual vehicle, we research and test for New European Driving Cycle (NEDC) and China Light-Duty Vehicle Test Cycle (CLTC) using the same vehicle. The results show that the short-cut method can save at least 50% of the test time compared with the run-out method. The error of the short-cut method based on the flowmeter for the NEDC working condition is less than 0.1%, and for the CLTC working conditions is 8.12%. After adding a throttle valve and a 4L buffer tank, the error is reduced to 4.76% from 8.12%. The test results show that hydrogen consumption measurement based on the flowmeter and short-cut method should adopt corresponding solutions according to the scenarios.

Torque allocation strategy based on economy and stability for electric vehicle considering controllability after motors failure

To reduce the energy consumption and improve the stability of distributed drive electric vehicles, a torque allocation strategy based on an economy and stability optimisation function (ESOF) and a fuzzy proportional-integral-derivative rule control (FPRC) strategy are proposed while considering motor efficiency, braking energy recovery and motor failure. First, the vehicle dynamics and motor equivalent models are established. Subsequently, a torque prediction model and fuzzy controller for the vehicle are designed to calculate the total desired torque and yaw moment, respectively. A torque optimisation function is established to minimise power losses in the electric motor and maximise braking energy recovery, and it is solved using an improved genetic algorithm. While satisfying vehicle driving constraints, the ESOF-based controller can effectively coordinate the operation of each motor in the high-efficiency range under driving and braking conditions. After one motor fault is detected, the ESOF-based controller is replaced with an FPRC-based controller to distribute the vehicle demand torque. A co-simulation platform integrating MATLAB/Simulink and CarSim is developed to verify the effectiveness of the proposed ESOF-based controller in the New European Driving Cycle (NEDC) and Federal Test Procedure 75 (FTP75) driving cycles. The effectiveness of the FPRC-based controller in step steering condition is verified using the co-simulation platform. The simulation results indicate that the vehicle economy and driving range of the ESOF-based controller improved compared with the results afforded by the typical torque distribution strategy based on the front–rear axle dynamic load ratio. The average efficiencies of the motors in the NEDC and FTP75 driving cycles increased by 2.94% and 2.4%, respectively. More importantly, the FPRC-based controller can more significantly improve the steering stability of a vehicle with motor failure compared with the ESOF-based controller.

Effective emissions reduction of high-mileage fleets through a catalytic converter and oxygen sensor replacement program

High-mileage vehicles such as taxis make disproportionately large contributions to urban air pollution due to their accelerated engine deterioration rates and high operation intensities despite their small proportions of the total fleet. Controlling emissions from these high-mileage fleets is thus important for improving urban air quality. This study evaluates the effectiveness of a pilot repair program in reducing emissions from taxis in Hong Kong which account for about 2 % of the total licensed vehicles. The emission factors of a large sample of 684 in-service taxis (including 121 for an emission survey program and 563 for a pilot repair program) were measured on transient chassis dynamometers. The results showed that 63 % of the sampled taxis failed the driving cycle test before the pilot repair program. Most of failed taxis were NO related and 91 % of failed taxis exceeded the emission limits of at least two regulated pollutants simultaneously. After the pilot repair program by replacing catalytic converters and oxygen sensors, the failure rate was significantly reduced to only 7 %. In addition, the fleet average NO, HC and CO emission factors were reduced by 85 %, 82 % and 56 %, respectively. In addition, on-road remote sensing measurements confirmed the real-world emission reductions from the taxis that participated in the pilot repair program. These findings led to the implementation of a large-scale replacement program for all taxis in Hong Kong during 2013–2014, which was estimated to have reduced the total HC, CO and NO emissions by about 420, 2570 and 1000 t per year, respectively (equivalent to 5–8 % emission reductions from the whole road transport sector). Therefore, reducing emissions from the small high-mileage fleets is a highly cost-effective measure to improve urban air quality.

Impact of Active Diesel Particulate Filter Regeneration on Carbon Dioxide, Nitrogen Oxides and Particle Number Emissions from Euro 5 and 6 Vehicles under Laboratory Testing and Real-World Driving

Particulate mass concentration is a crucial parameter for characterising air quality. The diesel particulate filter (DPF) is the primary technology used to limit vehicle particle emissions, but it needs periodic cleaning, a process called regeneration. This study aims to assess the impact of active DPF regeneration on the performance and emissions of Euro 5 and 6 vehicles. The study examined both carbon dioxide (CO2) and pollutant (nitrogen oxides (NOx) and particle number (PN)) emissions for eight vehicles tested in the laboratory and on the road. Apart from the DPF, a wide range of emission control systems was covered in this experimental campaign, including exhaust gas recirculation (EGR), diesel oxidation catalyst (DOC), lean NOx trap (LNT) and selective catalytic reduction (SCR) catalyst, revealing the different impacts on NOx emissions. The regeneration frequency and duration were also determined and used to calculate the Ki factor, which accounts for the emissions with and without regeneration, weighted over the distance driven between two consecutive regeneration events. Based on these outcomes, representative emission factors (EF) were proposed for the regeneration phase only and the complete regeneration interval. In addition, the effect of regeneration on efficiency was estimated and compared with other energy consumers. The results indicated a significant impact of DPF regeneration on CO2, NOx and PN emissions, higher in the case of driving cycle testing in the laboratory. The relevant mechanisms behind the elevated emission levels were analysed, focusing on the regeneration period and the test phase following immediately after. The calculation of the Ki factor and the comparison with the official values revealed some weaknesses in its application in real-world conditions; to overcome these, new NOx EF values were calculated, depending on the emission control system. It was revealed that Euro 6 vehicles equipped with SCR could comply with the applicable limits when considering the complete regeneration interval. Finally, it was indicated that the DPF regeneration impact on vehicle efficiency is similar to that of driving with the air conditioning (A/C) system and headlights on.

Assessment of particle and gaseous emissions and reductions from gasoline direct injection passenger car and light-duty truck during passive regeneration

This study evaluated the effectiveness of two passive regenerating gasoline particulate filters (GPFs) on reducing both gaseous and particle phase pollutants from a gasoline direct inject (GDI) passenger car (PC) and light-duty truck (LDT). In the absence of filter regeneration, observations from this study are consistent with other studies demonstrating how particle number (PN), particulate matter (PM), and black carbon (BC) emissions were reduced from the two vehicles with the use of GPFs. The significance of this study was to demonstrate the ability of the GPF to mitigate gaseous and particulate pollutants during severe passive filter regeneration, which was often observed on the LDT during aggressive US06 drive cycle testing. Partial filter regeneration happened on the LDT during some FTP-75 tests, as well as on the PC during some US06 drive cycles, however, this did not impact the GPF filtration efficiency (FE) to reduce particulate and gaseous pollutants. Using a cleaner fuel with lower overall tailpipe PM emissions could potentially lead to more frequent partial regenerations. This could produce the benefit of lower exhaust back pressure during and immediately after regeneration but still provide sufficient reduction in both particle and gaseous emissions.