Real Driving Cycles Research Articles

Energy Modeling for Electric Vehicles Based on Real Driving Cycles: An Artificial Intelligence Approach for Microscale Analyses

This paper presents the process of creating a model for electric vehicle (EV) energy consumption, enabling the rapid generation of results and the creation of energy maps. The most robust validation indicators were exhibited by an artificial intelligence method, specifically neural networks. Within this framework, two predictive models for EV energy consumption were developed for winter and summer conditions, based on actual driving cycles. These models hold particular significance for microscale road analyses. The resultant model, for test data in summer conditions, demonstrates validation indicators of an R2 of 86% and an MSE of 1.4, while, for winter conditions, its values are 89% and 2.8, respectively, confirming its high precision. The paper also presents exemplary applications of the developed models, utilizing both real and simulated microscale data. The results obtained and the presented methodology can be especially advantageous for decision makers in the management of city roads and infrastructure planners, aiding both cognitive understanding and the better planning of charging infrastructure networks.

Non linear model predictive control strategy for the energy management of a P4 parallel hybrid electric vehicle

In this paper, the energy management strategies (EMS) as main fuel saving approaches are studied for a P4 parallel hybrid electric vehicle (HEV). The multiple power sources of the analysed HEV (one thermal engine and two electric motors) and the different vehicle driving conditions increase the complexity in designing an optimal EMS. To efficiently solve the fuel minimization problem, a non linear Model Predictive Control (NL-MPC) is proposed as energy optimization strategy of the examined HEV. First, a vehicle simulation model is developed in Matlab/Simulink environment. A NL-MPC-controller is designed, implemented into the adopted code and coupled to the vehicle model. The effectiveness of developed NL-MPC approach is evaluated in two different driving cycles, also including various initial battery State of Charge. A comparison with a well-recognized real-time EMS strategy, namely heuristic/rule based (RB) approach, is performed over WLTC and a Real Driving Cycle (RDC). The numerical outcomes demonstrate the capability of NL-MPC controller at significantly improving the fuel consumption with respect to the RB strategy (maximum advantage of 9% and 15% over WLTC and RDC), thus providing an excellent and robust method in the HEV powertrain control with satisfactory performance.

Experimental Evaluation of Hybrid Vehicle Fuel Economy in Real Driving Cycles: a Case Study in Hanoi

Experimental Evaluation of Hybrid Vehicle Fuel Economy in Real Driving Cycles: a Case Study in Hanoi

Real Driving Cycle Simulation of a Hybrid Bus by Means of a Co-Simulation Tool for the Prediction of Performance and Emissions

This work is focused on the simulation of a complete hybrid bus vehicle model performing a real-world driving cycle. The simulation framework consists of a coupled co-simulation environment, where all the vehicle sub-system models are linked to achieve a real time exchange of input and output signals. In the vehicle model also the electric devices of the powertrain and accumulation system are included. This co-simulation platform is applied to investigate the hybridization of a 12-m city bus, performing a typical urban driving mission. A comparison between the conventional powertrain is performed against the hybridized version, to highlight the advantages and challenges. In particular, the novelty of this modeling approach is that the IC engine simulation does not rely on pre-processed look-up tables, but exploits a high-fidelity one-dimensional thermo-fluid dynamic model. However, it was necessary to develop a fast simulation methodology to exploit this predictive tool, achieving a low computational cost. The 1D engine model is first validated against the experimental engine map data available, showing a good model predictivity. Then the 1D engine model and the other models of the powertrain are coupled to the vehicle model, in order to follow the prescribed velocity profile of the driving cycle. The complete model is applied under different conditions, to evaluate the impact on performance and emissions and assess the simulation predictivity.

Driving cycle prediction based on Markov chain combined with driving information mining

Driving cycle prediction (DCP) is of great importance for vehicle’s awareness of surrounding environment and optimization of control strategy. It has won much attention around the world. However, the used driving database could not reflecting the great diversity in multi driving cycles in real world. The characteristic parameters could not represent the complex and diverse driving conditions. Therefore, DCP models’ adaptability and generalization ability are limited. Moreover, prediction methods in existing researches always take large computation burden, difficult to apply in practice. To tackle these issues, the present research focus on DCP based on Markov chain combined with driving information mining (DIM). Firstly, many standard test cycles and real cycles are collected to construct an database of great diversity. Secondly, DIM technologies are studied to extract the optimal parameter set reflecting driving characteristics and determine the driving cycle categories. Thirdly, prediction method based on recursive self-learning Markov chain is proposed. Recursive equation of Markov state transition probability matrix (TPM) is deduced to save computation time. The key parameter of prediction method is adjusted to achieve optimal prediction performance. Synthesized standard test driving cycles (SSTDC) and real driving cycles (RDC) in database, as well as a new real driving cycle (NRDC) are tested. Driving pattern prediction accuracy of the proposed method is 99.25% for SSTDC, 100.00% for RDC, and 99.84% for NRDC, respectively. Compared with the benchmark method, the prediction accuracy is improved by 18.87% for SSTDC, 10.07% for RDC and 23.51% for NRDC, respectively. In addition, processing time of the prediction program is significantly reduced by 2.23 s (36.14%) for SSTDC, 12.30 s (51.25%) for RDC and 3.99 s (52.16%) for NRDC, respectively. It can be verified that the proposed method has high precision, short response time, as well as good adaptability and charming generalization ability for new cycles.

Research on Multifractal Characteristics of Vehicle Driving Cycles

Vehicle driving cycles have complex characteristics, but there are few publicly reported methods for their quantitative characterization. This paper innovatively investigates their multifractal characteristics using the fractal theory to characterize their complex properties, laying the foundation for applications such as vehicle driving cycle feature identification, vehicle energy management strategies (EMS), and so on. To explore the scale-invariance of the vehicle driving cycles, the four vehicle driving cycles were analyzed using the Multifractal Detrended Fluctuation Analysis (MF-DFA) method, three of which are standard vehicle test cycles: the New European Driving Cycle (NEDC), the World-wide harmonized Light-duty Test Cycle (WLTC) and the China Light-duty Vehicle Test Cycle for Passenger Car (CLTC-P), and the other is the Urban Road Real Driving Cycle (URRDC), which was obtained by analyzing and processing vehicle driving data collected in actual urban driving conditions. The fluctuation functions, the generalized Hurst exponents, the mass exponent spectra, the multifractal singularity spectra, and the multifractal characteristic parameters were calculated to verify the multifractal characteristics, and to quantify the fluctuation singularities of different driving cycles as the time series. The results show that the fluctuations of all four driving cycles have long-range anticorrelations and exhibit significant multifractal characteristics. The results can provide a basis for the analysis of the complexity of the vehicle driving cycles.

An energy management strategy for plug-in hybrid electric vehicles based on deep learning and improved model predictive control

In order to reasonably allocate the power needs and manage the energy of the plug-in hybrid electric vehicle (PHEV) more efficiently, an energy management strategy (EMS) based on deep learning and improved model predictive control was proposed. Firstly, the vehicle energy flow test was carried out for the PHEV, and the multi-physics (mechanical-electrical-thermal-hydraulic) model was constructed and validated. Secondly, six prediction models were built based on different algorithms and the effects were compared and analyzed in detail. Finally, a long short-term memory based improved model predictive control algorithm (LSTM-IMPC) was developed, and the effects of three EMSs based on the charge-depleting charge-sustaining rule (CD-CS), dynamic programming (DP) and LSTM-IMPC were investigated under Worldwide Light-duty Test Cycle (WLTC), New European Driving Cycle (NEDC) and real driving cycle (RDC). The results show that the fuel-saving rates of the LSTM-IMPC-based EMS under these three cycles are respectively 3.81%, 5.6% and 18.71% compared with the CD-CS-based EMS, which prove the good fuel-saving performance and strong robustness of the proposed EMS. The fuel-saving rates of the LSTM-IMPC-based EMS are close to the DP-based EMS, which are the global optimal under these three cycles.

Exhaust Emissions from Plug-in and HEV Vehicles in Type-Approval Tests and Real Driving Cycles

The amount of hybrid vehicles and their contribution have increased in the global market. They are a promising aspect for a decrease in emissions. Different tests are used to determine the factors of such emissions. The goal of the present study was to compare the emissions of two hybrid vehicles of the same manufacturer: the plug-in version and the HEV version (gasoline + electric engine). These vehicles were chosen because they comprise the largest market share of hybrid cars in Poland. The exhaust emission tests were conducted in the WLTC tests on a chassis dynamometer and under real traffic conditions. Simultaneous testing on a dyno and under real driving is the most adequate test to assess the environmental aspects of vehicles—especially hybrids. The combustion engines of the tested vehicles were supplied with gasoline containing 5% biocomponents. The emissions, including CO2, CO, NOx, THC and PNs, were measured in accordance with the European Union procedure. According to the latter, the resistance to motion of the chassis dyno was adjusted to the road load, allowing the hybrid vehicles to move in electric mode and allowing the dynamometer to operate in energy recovery mode. The obtained emissions of CO2, CO, NOx and THC in the case of the plug-in hybrid vehicle were lower by 3%, 2%, 25%, and 13%, respectively, compared to the case of HEV. Fuel consumption in the case of the plug-in hybrid vehicle was lower by 3%, and PN was lower by 10% compared to the case of HEV (WLTC). In real driving conditions, the differences were more pronounced in favour of the plug-in vehicle: CO2 emissions in the RDE test were 30% lower, NOx emissions were 50% lower, and PN was 10% lower. An increase in emissions was only observed for CO2 emissions—the plug-in vehicle’s on-road emissions were 6% higher compared to the HEV. The obtained emissions for FC and PN varied with actual velocity values due to competitive driving between a combustion engine and an electric motor, as well as existing acceleration and deceleration events during the test and other factors.

Experimental Characterization of Real Driving Cycles in a Light-Duty Diesel Engine under Different Dynamic Conditions

This paper studies the behavior of a Euro 6 diesel engine tested under dynamic conditions corresponding to different real driving emissions (RDE) scenarios. RDE cycles have been performed in an engine test bench by simulating its operation in a long van application. A computer tool has been designed to define the cycle accounting for different dynamic characteristics and driver behaviors to study their influence on CO2 and pollutant emissions, particularly CO, THC, and NOX. Different dynamic parameters have been established in terms of power, torque, engine speed, or vehicle speed. Additionally, a tool to estimate the emission of an RDE cycle from steady-state maps has been developed, helping to identify emission trends in a clearer way. Finally, the conclusions suggest that driving patterns characterized by lower engine speeds lead to fewer emissions. In addition, the analysis of RDE cycles from stationary maps helps to estimate the final tailpipe emissions of CO2 and NOX, offering the possibility to rely on tests carried out on engine test bench, dynamometer, or on the road.

Real Driving Cycle-Based State of Charge Prediction for EV Batteries Using Deep Learning Methods

An accurate prediction of the State of Charge (SOC) of an Electric Vehicle (EV) battery is important when determining the driving range of an EV. However, the majority of the studies in this field have either been focused on the standard driving cycle (SDC) or the internal parameters of the battery itself to predict the SOC results. Due to the significant difference between the real driving cycle (RDC) and SDC, a proper method of predicting the SOC results with RDCs is required. In this paper, RDCs and deep learning methods are used to accurately estimate the SOC of an EV battery. RDC data for an actual driving route have been directly collected by an On-Board Diagnostics (OBD)-II dongle connected to the author’s vehicle. The Global Positioning System (GPS) data of the traffic lights en route are used to segment each instance of the driving cycles where the Dynamic Time Warping (DTW) algorithm is adopted, to obtain the most similar patterns among the driving cycles. Finally, the acceleration values are predicted from deep learning models, and the SOC trajectory for the next trip will be obtained by a Functional Mock-Up Interface (FMI)-based EV simulation environment where the predicted accelerations are fed into the simulation model by each time step. As a result of the experiments, it was confirmed that the Temporal Attention Long–Short-Term Memory (TA-LSTM) model predicts the SOC more accurately than others.

Driving behaviour and trip condition effects on the energy consumption of an electric vehicle under real-world driving

This work evaluates driving behaviour, trip distance, ambient temperature, traffic condition and road grade effects on the specific energy consumption (SEC) of an electric vehicle (EV) under different operation modes according to a real driving cycle (RDC) test schedule. A compact size EV was operated in the roads of the second largest populated city in the United Kingdom for nearly four years, with real-time data collected, processed and stored by a monitoring software communicating with the electronic central unit. The trips were selected according to fully compliance with the RDC test in the individual operation modes based on vehicle speed – urban, rural and motorway – and those with shorter distances than the specifications. The driving behaviour was classified as aggressive, moderate and passive, according to dynamic operation limits, and the parameters representing the traffic conditions were stop time percentage and average vehicle speed in urban driving. The results show that the SEC is highly influenced by changes in the outside temperature, nearly doubling from operation at moderate temperatures of around 20 °C to operation at temperatures as low as 0 °C. Short trips below 16 km caused nearly 10% SEC average increase in comparison with longer ones, showing more awkward effects in motorway operation with a SEC rise up to 29%. Traffic conditions and driving behaviour also demonstrated a high influence on SEC, increasing it by as much as 40% and 16%, respectively, from the most favourable to the most unfavourable condition. In comparison with flat roads, ascending roads with 3% grade increased SEC by 50% while descending roads with −3% grade decreased SEC by 80%, with the assistance of the regenerative brake system.

Machine Learning Applied to the NOx Prediction of Diesel Vehicle under Real Driving Cycle

Euro 6 standards impose stringent nitrogen oxide (NOx) emission limits on diesel cars. NOx emissions are significantly different between Euro 6 diesel cars and the previous standards in real-world driving. In this research, the NOx concentrations of Euro 6 diesel engines during real-world driving were studied considering various factors. Real driving emission (RDE) tests were conducted using vehicles equipped with portable emissions measurement systems. Urban, rural, and motorway test routes were utilized. Road environment, atmospheric, and after-treatment performance factors were collected in each case. An artificial neural network was used for evaluation using RDE test data and various statistical parameters. It was found that the proposed method predicted the pollutant emissions effectively. Lastly, the relative importance of each predictor was derived, and the NOx concentrations were analyzed. These approaches provide accurate emission information for an environmental effect evaluation that reflects more realistic road conditions.

Exhaust Emissions and Energy Consumption Analysis of Conventional, Hybrid, and Electric Vehicles in Real Driving Cycles

One of the environmental aims of the European Union is to achieve climate neutrality by 2050. According to European Parliament data, transport emissions accounted for about 25% of global carbon dioxide emissions in 2016, in which road transport had the largest share (approximately 72%). This phenomenon is particularly visible in urban agglomerations. The solution examples are the popularization of hybrid vehicles and the development of electromobility. The aim of this paper is an assessment of the energy consumption and exhaust emissions from passenger cars fitted with different powertrains in actual operation. For the tests, passenger cars with conventional engines of various emission classes were used as well as the latest hybrid vehicles and an electric car. It enabled a comparative assessment of the energy consumption under different traffic conditions, with particular emphasis on the urban phase and the entire RDE (Real Driving Emissions) test. The results were analyzed to identify changes in these environmental factors that have occurred with the technical advancement of vehicles. The lowest total energy consumption in real traffic conditions is characteristic of an electric vehicle; the plug-in hybrid vehicle with a gasoline engine is about 10% bigger, and the largest one is a combustion vehicle (30% bigger than an electric vehicle). These data may contribute to the classification of vehicles and identification of advantages of the latest developments in conventional, hybrid, and electric vehicles.

Synthetic Vs. Real Driving Cycles: A Comparison of EV Battery Degradation

Automobile dependency and the inexorable proliferation of electric vehicles (EV) compels accurate predictions of cycle life across multiple usage conditions and for multiple lithium-ion battery systems. Synthetic driving cycles have been essential in accumulating data on EV battery lifetimes. However, since battery deterioration is path-dependent, the representability of synthetic cycles must be questioned. Hence, this work compared three different synthetic driving cycles to real-driving data in terms of mimicking actual EV battery degradation. It was found that the average current and charge capacity during discharge were important parameters in determining the appropriate synthetic profile and traffic conditions have a significant impact on cell lifetimes. In addition, a stage of accelerated capacity fade was observed and shown to be induced by an increased loss of lithium inventory (LLI) resulting from irreversible Li plating. New metrics, the ratio of the loss of active material at the negative electrode (LAMNE) to the LLI and the plating threshold, were proposed as possible predictors for a stage of accelerated degradation. The results presented here demonstrate tracking properties such as capacity loss and resistance increase were insufficient in predicting cell lifetimes and support the adoption of metrics based on the analysis of degradation modes.

Synthetic vs. Real Driving Cycles: A Comparison of Electric Vehicle Battery Degradation

Automobile dependency and the inexorable proliferation of electric vehicles (EVs) compels accurate predictions of cycle life across multiple usage conditions and for multiple lithium-ion battery systems. Synthetic driving cycles have been essential in accumulating data on EV battery lifetimes. However, since battery deterioration is path-dependent, the representability of synthetic cycles must be questioned. Hence, this work compared three different synthetic driving cycles to real driving data in terms of mimicking actual EV battery degradation. It was found that the average current and charge capacity during discharge were important parameters in determining the appropriate synthetic profile, and traffic conditions have a significant impact on cell lifetimes. In addition, a stage of accelerated capacity fade was observed and shown to be induced by an increased loss of lithium inventory (LLI) resulting from irreversible Li plating. New metrics, the ratio of the loss of active material at the negative electrode (LAMNE) to the LLI and the plating threshold, were proposed as possible predictors for a stage of accelerated degradation. The results presented here demonstrated tracking properties, such as capacity loss and resistance increase, were insufficient in predicting cell lifetimes, supporting the adoption of metrics based on the analysis of degradation modes.

Analysis of a series hybrid vehicle concept that combines low temperature combustion and biofuels as power source

Abstract This work evaluates the potential of a series hybrid vehicle concept that combines low temperature combustion (LTC) and biofuels as power source. To do this, experimental data from a previous work obtained in a single-cylinder engine running under ethanol-diesel dual-fuel combustion is used. Then, vehicle systems simulations are used to estimate performance and emissions of the LTC hybrid vehicle and compare them versus conventional diesel combustion (CDC). The vehicle selected to perform the simulations is the Opel Vectra, which equips the compression ignition engine used in the experimental tests. The results from the simulations used for the analysis are firstly optimized by combining design of experiments and the Kriging fitting method. The multi-objective optimization allows to determine some characteristics and controls of the hybrid vehicle. The comparison of the estimated performance and emissions of the LTC-hybrid concept versus CDC over the worldwide harmonized light vehicles test cycle (WLTC) and real driving cycle (RDE) revealed clear benefits in terms of energy consumption, CO2 and NOx and soot emissions. In this sense, the hybrid concept enabled a reduction of the final energy consumed of 3% in the RDE cycle and 6.5% in the WLTC as compared to CDC. In terms of engine-out emissions, the CO2 was reduced around 16% versus CDC, and engine-out NOx and soot were reduced below the levels imposed by the Euro 6 regulation. As a penalty, the engine-out HC and CO emissions increased to more than double than CDC. However, based on previous experimental results, it is expected that a conventional diesel oxidation catalyst can reduce the tail-pipe HC and CO levels below the Euro 6 limits.

Comparison of Three Methods for Constructing Real Driving Cycles

This work compares the Micro-trips (MT), Markov chains–Monte Carlo (MCMC) and Fuel-based (FB) methods in their ability of constructing driving cycles (DC) that: (i) describe the real driving patterns of a given region and (ii) reproduce the real fuel consumption and emissions exhibited by the vehicles in that region. To that end, we selected four regions and monitored simultaneously the speed, fuel consumption and emissions of CO2, CO and NOx from a fleet of 15 buses of the same technology during eight months of normal operation. The driving patterns exhibited by drivers in each region were described in terms of 23 characteristic parameters (CPs) such as average speed and average positive kinetic energy. Then, for each region, we constructed their DC using the MT method and evaluated how close it describes the observed driving pattern in each region. We repeated the process using the MCMC and FB methods. Given the stochastic nature of MT and MCMC methods, the DCs obtained changed every time the methods were applied. Hence, we repeated the process of constructing the DCs up to 1000 times and reported their average relative differences and dispersion. We observed that the FB method exhibited the best performance producing DCs that describe the observed driving patterns. In all the regions considered in this study, the DCs produced by this method showed average relative differences smaller than 20% for all the CPs considered. A similar performance was observed for the case of fuel consumption and emission of pollutants.

Research on the influence factors of real driving cycle with statistical analysis and dynamic time warping

The real driving cycle (RDC), which aims to reflect the real driving behaviour of vehicles, plays an important role in evaluating the performance or pollution of vehicles. At present, the related researches most focus on developing RDCs of different functions or regions, while the influence factors and the rules of RDC construction are not involved. In this study, through statistical analysis and theoretical analysis of RDC construction process, the influence factors of candidate RDC are explored. These factors include statistical characteristics, common factors number, cluster number, number selection principle, and order principle. A different value of factors means a different candidate RDC and different proximity of RDCs to the real driving data. Through the dynamic time warping index, the proximity of candidate RDCs is calculated, then the factors rules and the optimum RDC are obtained. When the slope is added as one statistical characteristic, the common factors number is set as 5, the cluster number is set as 6, the number selection principle is set as ratio principle, and order principle chooses positive sequence, the candidate RDC is the optimal cycle which is closest to the real vehicles driving conditions.

Urban Global Driving Cycle Construction Method and Global Optimal Energy Management in Plug-in Hybrid Electric Vehicle

This paper proposes a method of constructing global urban driving cycle based on real-time traffic information and applied in energy control strategy. Urban district driving cycles are collected in substantial in California through performance measure system (PeMS) to establish local speed fragment database. A real driving cycle (RDC) is selected in an urban district and established the corresponding time traffic information tensor model by using the data in PeMS. The real-time traffic information incomplete situation is solved by tensor completion algorithm. According to the average speed of road sections at different times, the kinematic fragments are stochastically selected in the speed fragment database to construct a real-time driving cycle of each section. The comparison between global urban construction driving cycle (CDC) and RDCs show that the CDCs well reflect the real road driving characteristic parameters. Compared to its application in the plug-in hybrid electric vehicle (PHEV) global energy management, CDCs, and RDCs fuel consumption are similarity within approximately 7% and the fuel consumption improved up to 17% in peak time compared with the rule-based control strategy.

Simulation of Real Driving Cycles of Electric Cars in Laboratory Conditions

This article is focused on the area of research and development of vehicles with electric drive. The paper presents the results of research based on real electric vehicle EDISON EV as the real traffic in laboratory conditions reflecting. The analysis of special driving style was created in order to increase electric vehicle range.