Harmonized Light Vehicles Test Cycle Research Articles

The uniqueness of pollutant emission and fuel consumption test results for road vehicles tested on a chassis dynamometer

The article considers the issues of non-repeatability of exhaust emission and fuel consumption test results for road vehicles tested on a chassis dynamometer. Empirical results of passenger car tests on a chassis dynamometer in the WLTC (Worldwide Harmonized Light Vehicles Test Cycle) test were used. Tests were conducted in four repeated sets to assess the repeatability of the test results. The road emission of hydrocarbons, non-methane hydrocarbons, carbon monoxide, nitrogen oxides, particulate matter and carbon dioxide, the road number of particulate matter and operational fuel consumption were determined. The coefficient of variation of the values measured in the four tests was determined, taken as a measure of the repeatability of the test results. The coefficient of variation of the road emission of carbon dioxide and operational fuel consumption is the lowest. No significant differences were found in the values of the measured exhaust emission.

Multi-Step Ageing Prediction of NMC Lithium-Ion Batteries Based on Temperature Characteristics

The performance of lithium-ion batteries depends strongly on their ageing state; therefore, the monitoring and the prediction of the battery state of health (SoH) is necessary for an optimized and secured functioning of battery systems. This paper evaluates and compares three artificial neural network architectures for multi-step ageing prediction of lithium-ion cells: Recurrent Neural Network (RNN), Gated Recurrent Unit (GRU) and Long short-term memory (LSTM). These models use the features extracted from the cell’s temperature to predict the cell’s capacity. The features are extracted from experimental measurements of the cell’s surface temperature and selected based on Spearman correlation analysis. The prediction results were evaluated and compared considering three different percentages of the training dataset: 60%, 70%, and 80%. Training and testing data were generated experimentally based on accelerated ageing cycling tests. During these experiments, four Nickel Manganese Cobalt/Graphite (NMC) cells were cycled under a controlled temperature environment based on a dynamic current profile extracted from the Worldwide Harmonized Light Vehicles Test Cycles.

Non-repeatability of the WLTP vehicle test results

The paper presents results of considerations on the repeatability of the passenger car test results obtained in the WLTP procedure on a chassis dynamometer. The research concerned the aspects of exhaust emissions and fuel consumption. Measurements were carried out in the WLTC test with a cold engine start and then in four WLTC (Worldwide harmonized Light vehicles Test Cycle) tests with a hot engine start. The following values were measured: average specific distance emissions of hydrocarbons, non-methane hydrocarbons, carbon monoxide, nitrogen oxides, particulate matter and carbon dioxide, specific distance particulate number, and operational fuel consumption. Thus, it was possible to assess the impact of the engine's thermal state at start-up on the test results and the nature of test results repeatability with the start-up of a hot engine. The repeatability of the test results was assessed based on the coefficient of variation obtained in the individual tests and the relationship of the maximum difference between measurement results values in the individual tests for a hot engine start. The obtained test results turned out to be very diverse for the considered parameters and indicated low repeatability. Values of carbon dioxide emissions and operational fuel consumption were definitely the least varied in individual hot engine start tests. The exhaust emission of particulate matter varied the most in individual test iterations. However, the specific distance particulate number was relatively similar between individual tests, less so than the exhaust emission of other pollutants. In the case of different engine thermal state at start-up, the emitted particulate number varied the most in the test results, while the emission of carbon dioxide and operational fuel consumption varied the least. The repeatability of executing the velocity process in WLTC tests at hot engine and cold engine start-up was also examined as processes determining exhaust emissions and fuel consumption. These tests were much more repeatable than the exhaust emission and fuel consumption.

Dynamic K-Decay Learning Rate Optimization for Deep Convolutional Neural Network to Estimate the State of Charge for Electric Vehicle Batteries

This paper introduces a novel convolutional neural network (CNN) architecture tailored for state of charge (SoC) estimation in battery management systems (BMS), accompanied by an advanced optimization technique to enhance training efficiency. The proposed CNN architecture comprises multiple one-dimensional convolutional (Conv1D) layers followed by batch normalization and one-dimensional max-pooling (MaxPooling1D) layers, culminating in dense layers for regression-based SoC prediction. To improve training effectiveness, we introduce an advanced dynamic k-decay learning rate scheduling method. This technique dynamically adjusts the learning rate during training, responding to changes in validation loss to fine-tune the training process. Experimental validation was conducted on various drive cycles, including the dynamic stress test (DST), Federal Urban Driving Schedule (FUDS), Urban Dynamometer Driving Schedule (UDDS), United States 2006 Supplemental Federal Test Procedure (US06), and Worldwide Harmonized Light Vehicles Test Cycle (WLTC), spanning four temperature conditions (−5 °C, 5 °C, 25 °C, 45 °C). Notably, the test error of DST and US06 drive cycles, the CNN with optimization achieved a mean absolute error (MAE) of 0.0091 and 0.0080, respectively at 25 °C, and a root mean square error (RMSE) of 0.013 and 0.0095, respectively. In contrast, the baseline CNN without optimization yielded higher MAE and RMSE values of 0.011 and 0.014, respectively, on the same drive cycles. Additionally, training time with the optimization technique was significantly reduced, with a recorded time of 324.14 s compared to 648.59 s for the CNN without optimization at room temperature. These results demonstrate the effectiveness of the proposed CNN architecture combined with advanced dynamic learning rate scheduling in accurately predicting SoC across various battery types and drive cycles. The optimization technique not only improves prediction accuracy but also substantially reduces training time, highlighting its potential for enhancing battery management systems in electric vehicle applications.

Comparing and Parameterizing the Electrical Energy Consumption Models in SUMO

This paper examined the performances of the current four battery models in SUMO. The possibility of expanding the model parameterization was also investigated and the corresponding extension was carried out for PHEMlight. Accordingly, the models can be compared more fairly. Three scenarios were used, namely the Worldwide harmonized Light vehicles Test Cycle, a constant high-speed highway scenario and an area scenario with a relatively complex traffic situation. The results show that all models can address recuperation and propulsion, and deliver the similar result at very low acceleration. The models based on average vehicle data generally tend to deliver higher battery consumption than the models with individual vehicle type-specific parameterization, especially PHEMlight5, while HBEFA4 only has one electric vehicle class and is therefore not sensitive to various vehicle characteristics. Moreover, the model by Kurczveil and López (EVM) seems to tend to have the lowest consumption of all models.

Decelerating catalyst aging of natural gas engines using organic Rankine cycle under road conditions

High exhaust temperature is an intrinsic nature of natural gas engines which underlies power de-rating and thermal aging of after-treatment system; therefore, this study integrates an organic Rankine cycle (ORC) system between engine and it's three-way catalyst (TWC) to address these challenges. ORC facilitates power output enhancement through exhaust energy recovery and alleviates thermal aging by reducing exhaust temperature. To estimate the effectiveness of this hypothesized system, a simulation-based investigation is performed. First, simulation models, including engine, TWC, and vehicle dynamic models, are built and validated by experimental data. According to the temperature characteristics of different TWCs, three scenarios, representing old, current, and prospective TWC technology, are formulated to estimate the ORC performance under Worldwide Harmonized Light Vehicles Test Cycle. Results show that ORC system can substantially alleviate the thermal damage caused by high exhaust temperature and extend TWC lifespan. It is estimated that over 98.5 % of thermal damage can be decreased by proper ORC setting, and the average TWC lifespan extension can be at least 55.4, making a reduced noble metal usage and cost of TWC. Meanwhile, with the decrease of the working temperature of TWC, ORC can recover exhaust energy under more road conditions, further improving the net power and shortening the payback period of extra ORC hardware costs. A reduction in the working temperature of TWC from 770.5 K to 618 K yields a 109 % enhancement in maximum power, coupled with a 62.30 % reduction in the payback period. These findings fully reflect the advantage of ORC-TWC coupling and indicate that ORC is supposed to be used more for the TWC with a low working temperature to maximize economic effectiveness. This study provides a novel pathway for thermal aging alleviation of TWC and a valuable reference for prospective studies on matching ORC with TWC under road conditions.

Effects of fuel and driving conditions on particle number emissions of China-VI gasoline vehicles: based on corrections to test results.

The increasing number of vehicles are emitting a large amount of particles into the atmosphere, causing serious harm to the ecological environment and human health. This study conducted the Worldwide Harmonized Light Vehicles Test Cycle (WLTC) to investigate the emission characteristics of particle number (PN) of China-VI gasoline vehicles with different gasoline. The gasoline with lower aromatic hydrocarbons and olefins reduced particulate matter (PM) and PN emissions by 24% and 52% respectively. The average PN emission rate of the four vehicles during the first 300s (the cold start period) was 7.2 times that of the 300s-1800s. Additionally, because the particle transmission time and instrument response time, the test results of instantaneous emissions of PN were not synchronized with vehicle specific power (VSP). By calculating the Spearman correlation coefficient between pre-average vehicle specific power (PAVSP) and the test results of PN instantaneous emissions, the delay time was determined as 10s. After the PN emissions results were corrected, the PN emissions were found to be more related to VSP. By analyzing the influence of driving status on emission, this study found that vehicles in acceleration mode increased PN emissions by 76% compared to those in constant speed mode.

Adaptive extended kalman filter for PEMFC membrane water content estimation

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

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.

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

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

OPTIMISATION OF HYBRID ELECTRIC VEHICLE ENERGY MANAGEMENT USING MACHINE LEARNING

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

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

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

Optimal component sizing and performance of Fuel Cell – Battery powered vehicle over world harmonized and new european driving cycles

Optimal component sizing and hybridization play vital task in reducing fuel utilization, increasing fuel cell system longevity and fuel economy of fuel cell electric vehicles. This paper proposes a fuel cell-battery powered vehicle is modeled and analyzed in MATLAB/Simulink tool known as Advanced Vehicle Simulator (ADVISOR). In addition to the optimal component sizing of power source components, the new World harmonized Light Vehicle Test Cycle (WLTC) is introduced and embedded into the ADVISOR software to estimate the driving behavior of the proposed fuel cell electric vehicle (FCEV). Approximately 10.5 % more gradeability performance achieved for the proposed powertrain against benchmark fuel cell powered vehicle Moreover, the world light-duty test cycle exhibit 18.5 % more fuel cell cut–off time and 29 % less run on time when compared with new european driving cycle (NEDC) cycle. The results of this study showed better fuel cell energy efficiency in terms of Wh/mile, fuel cell on–off strategy, cold start ability, acceleration and gradeability performance over the world light-duty test cycle drive cycle against new european driving cycle.

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

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

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

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

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

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

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

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

Virtual battery electric vehicle development via 1D tools

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

Estimating the Health Status of Li-ion NMC Batteries From Energy Characteristics for EV Applications

Capacity and Direct Current Internal Resistance are good health indicators for Li-ion batteries. But they cannot be measured. This work proposes to estimate these indicators from already available current and voltage measurements. The estimators are based on third-order polynomials and energy features extracted during partial discharge and different depths of discharge under a dynamic profile (World harmonized Light vehicles Test Cycles). The estimations are validated with experimental measurements obtained from cycling three Lithium-Nickel-Manganese-Cobalt-Oxide/Graphite cells at a controlled temperature. The mean relative error for the Direct Current Internal Resistance estimation is less than 5% when the depth of discharge lies between 25% and 40%. It is lower than 2% for the capacity estimation. The method is simple and suitable for embedded battery monitoring as it uses already available voltage and current measurements.

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

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