Worldwide Harmonized Light Vehicles Test Procedure Research Articles

Regenerative Braking Conscious Rule-Based Energy Management Strategy for Semi-Active Hybrid Energy Storage Systems for Electric Vehicles

Hybrid Energy Storage Systems (HESS) function as a solution to extend the lifespan of battery packs by maintaining a low charge/discharge rate. The integration of HESS in electric vehicles (EVs) is facilitated by supercapacitors (SC), known for their safe operation even under harsh conditions. The widely adopted rule-based power follower energy management strategy (EMS) is employed to control and restrict battery discharge within a defined range with SC support. Additionally, it charges the SC to accommodate regenerative energy, but it often neglects exploring the SC voltage set reference. This study investigates the SC voltage reference point and introduces a method to generate the reference point by equating the SC energy with the available kinetic energy in the moving vehicle. The proposed method was simulated and compared against the benchmark method using a portion of the medium segment Worldwide Harmonized Light Vehicles Test Procedure (WLTP) driving cycle. The results demonstrate a more stable discharge power for the battery bank, accompanied by improvements in battery voltage stability. The root mean square (RMS) battery current values were found to be 43.68 A and 42.92 A for the benchmarked and proposed methods, respectively, indicating a slight improvement of about 1%. Furthermore, the proposed method reduces the voltage deviation from 2.73% to about 2.57%, showcasing an additional positive outcome. These findings suggest that dynamically adjusting the SC voltage based on kinetic energy can enhance battery life and stability in EVs, offering a promising direction for future research and development of EMS.

Research on driving condition recognition in hybrid electric vehicle energy management strategy

Abstract The driving conditions encountered by vehicles during operation are complex and varied, and the ECMS (Equivalent fuel Consumption Minimization Strategy) based on minimum equivalent fuel consumption has poor adaptability to different driving conditions. To optimize the adaptability of the ECMS and improve the overall vehicle economy, this paper proposes an operating condition recognition model based on the Back Propagation neural network algorithm to optimize the ECMS energy management control strategy. Firstly, three types of driving conditions were identified: urban, suburban, and high-speed conditions. A complete vehicle model was constructed on the Matlab/Simulink platform. The efficacy of the strategy, before and after optimization, is verified under the WLTP (Worldwide Harmonized Light Vehicles Test Procedure) driving cycle. Simulation results show that the optimized ECMS can better adjust the power apportionment among the engine and motor, reducing the fuel consumption per hundred kilometers by 4.01% and thereby enhancing the overall vehicle economy.

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

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

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

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

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

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

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

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

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

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

A comprehensive system-level model for performance evaluation of proton exchange membrane fuel cell system with dead-ended anode mode

In this study, a comprehensive system-level model was developed for a proton exchange membrane fuel cell (PEMFC) system with a dead-ended anode (DEA) mode. The global system model consisted of a series of local component models, including PEMFC stack, manifolds, air compressor, humidifier and valves. Membrane water conversion and N2 crossover were simultaneously considered in the stack component model. Based on the system-level model, the output voltage and water content of the PEMFC and the compressor power consumption at various currents and ambient pressures were investigated. Additionally, the dynamic behaviors of the cell voltage, its water content, and system hydrogen utilization rate were predicted for two legislated driving cycles—the New European Driving Cycle (NEDC) and the Worldwide Harmonized Light Vehicle Test Procedure (WLTP). The fuel cell voltage fluctuation owing to the anode purge occurred with an increase in the load current. Increasing the ambient pressure increased decay rate and recovery rate of the cell voltage, improved membrane hydration, and lowered the compressor power consumption. For the variable loads of the NEDC and WLTP, the fuel cell voltage and power fluctuated significantly. The membrane water content was controlled by the current in the low-current zone (i < 0.5 A cm−2) and was dominated by the high airflow in the high-current zone (i > 0.9 A cm−2). In the medium-current zone (0.5 A cm−2 ≤ i ≤ 0.9 A cm−2), the membrane water was saturated. The water vapor exhibited a dynamic behavior similar to that of the membrane water; the behavior fluctuated in low and high current zones and stabilized in the medium current zone. The liquid water fluctuated significantly in each current zone of the NEDC and WLTP. Additionally, the H2 utilization rates under NEDC and WLTP were greater than 99%. The developed global system model helps to understand the transient behaviors of a DEA-mode PEMFC system and improves the system responsibility for fuel cell system design and development.

Higher Order Sliding-Mode Observers for State-of-Charge and State-of-Health Estimation of Lithium-Ion Batteries

This paper proposes a new higher-order sliding mode-based approach to deal with the problem of online state of charge (SOC) and state of health (SOH) estimation for Lithium-ion batteries. The proposed approach is based on the conjunction of a Higher-Order Sliding Mode (HOSM) observer with two Generalized Super-Twisting (GST) observer-based identification algorithms. The HOSM observer provides an exact estimation of SOC, while the two identification algorithms guarantee the finite time estimation of the battery capacity and the battery inner resistance. Thanks to the relationship between the battery capacity and the SOH, an accurate estimation of this latter is obtained. The proposed approach has two main advantages: it ensures SOC estimation using only one observer and estimates the battery parameters without requiring any assumption on the system states. Experimental results using the Worldwide Harmonized Light Vehicles Test procedures (WLTP) are used to illustrate the high efficiency of the proposed approach.

Sustainable City Evaluation Using the Database for Estimation of Road Network Performance

This article introduces the Database for Estimation of Road Network Performance (DERNP) to enable wide-scale estimation of relevant Road Network Performance (RNP) factors for major German cities. The methodology behind DERNP is based on a randomized route sampling procedure that utilizes the Worldwide Harmonized Light Vehicles Test Procedure (WLTP) in combination with the tile-based HERE Maps Traffic API v7 and a digital elevation model provided by the European Union’s Earth Observation Programme Copernicus to generate a large set of independent and realistic routes throughout OpenStreetMap road networks. By evaluating these routes using the PHEMLight5 framework, a comprehensive list of RNP parameters is estimated and translated into polynomial regression models for general usage. The applicability of these estimations is demonstrated based on a case study of four major German cities. This case study considers network characteristics in terms of detours, infrastructure, traffic congestion, fuel consumption, and CO2 emissions. Our results show that DERNP and its underlying randomized route sampling methodology overcomes major limitations of previous wide-scale RNP approaches, enabling efficient, easy-to-use, and region-specific RNP comparisons.

Test Stand for a Motor Vehicle Powered by Different Fuels

This article discusses current testing methods for motor vehicle engines. Traction engines have so far been tested, for example, according to WLTP (Worldwide Harmonized Light Vehicle Test Procedure) driving tests, but due to the “VW—gate” incident, these are now to be supplemented by RDE (Real Driving Emissions) tests, conducted under real road conditions. The analyses of the state of knowledge and the directions of research to date unequivocally indicate the need for the construction of a stand that allows: testing of a complete vehicle admitted to traffic; testing of a motor vehicle with the possibility of simulating real operating conditions; load setting with the possibility of its regulation; feeding the engine with various fuels; modification of the software of controllers having a direct impact on the control strategies of the engine; transmission and traction control system; reading, recording and analysis of the parameters of the operation of control systems in real time; detailed recording and analysis of the combustion process occurring directly in the combustion chamber; and the measurement of emitted toxic substances. On a bench with the above features, tests were carried out on a diesel motor vehicle, which were based on recording changes in the parameters of the combustion and injection process. The tests were conducted under static and dynamic conditions. Tests under static conditions were conducted on a chassis dynamometer. They consisted of indicating the engine for different fuel dose control maps. The vehicle equipped with the test engine was driven at a constant speed on the chassis dynamometer and loaded with a drag force of 130 Nm. Tests under dynamic conditions were conducted under real traffic conditions. They were limited to the presentation of results under static conditions. The main results of the tests are given in the conclusion and include a general summary. In particular, the presented results of the diesel tests demonstrate an attempt to adapt the engine to co-power with hydrogen.

Investigation of the Series Hybrid Electric Powertrain Architecture with Wankel Engine as a Range Extender

In this study, a conventional vehicle is converted into a series hybrid electric vehicle. Electric motor power, battery pack capacity, range extender operating condition is determined; a computational model of the vehicle is built and simulations are conducted using Worldwide Harmonized Light Vehicles Test Procedure (WLTP). Power rating of the electric motor is calculated as 120 kW for the same acceleration performance. Wankel engine data available in the Automotive Laboratory of Istanbul Technical University are used. Energy capacity of battery pack is determined according to the daily driving distance of the WLTP. Engine on-off control strategy is used to control range extender operation. Wankel engine is operated at a speed of 4000 rpm and load of 5.15 bar, hence it is tuned to deliver an output power of 22.3 kW. Simulation results show that the performance of Wankel engine is validated as range extender by using engine on-off control strategy. State of charge of battery pack is set as minimum (30%) at the beginning of simulation and the state of charge of the battery pack charged by the range extender is assumed to be approximately 50% at the end of the cycle. For comparison, performance of Wankel engine is compared to the range extender of a mass-produced series hybrid electric vehicle, which has a battery pack capacity of 18.8 kWh. In conclusion, it is shown that a more compact range extender unit with a battery pack of 35 kWh is advantageous from the perspective of the packaging of the battery pack.

Exhaust Emissions of In-Use Euro 6d-TEMP and Euro 6d Vehicles in WLTP and RDE Conditions, a Comparison

&lt;div class="section abstract"&gt;&lt;div class="htmlview paragraph"&gt;Air pollution is a major threat to the environment and human health being responsible of more than 350 000 premature deaths every year in the European Union (EU). Road transport accounts for circa 40% of the total NOx emissions and 10% of the particulates emissions of the EU. However, these shares increase significantly in urban areas where 75% of the citizens live. In order to curb pollutant emissions from new vehicles entering the European market, the Euro 6 regulation, setting emission limits for cars and vans, was recently updated with the introduction of a new test procedure in the laboratory, the Worldwide Harmonized Light Vehicle Test Procedure (WLTP), and a novel on-road test performed with Portable Emissions Measurement Systems, the Real Driving Emissions regulation (RDE). Both test procedures WLTP and RDE were designed to represent typical driving conditions and thus help characterizing exhaust emissions of light-duty vehicles in real life operation. This manuscript presents NOx, CO, and solid particulate number PN emissions of 53 gasoline and diesel passenger cars type-approved in the EU after the entry into force of the WLTP and RDE (i.e., meeting the Euro 6d-TEMP and Euro 6d standards). Emissions data over WLTP and RDE tests on in-use vehicles was collected by the Joint Research Center (JRC) of the European Commission in the period 2018-2021. Emissions are characterized by powertrain, fuel type, and test procedure. All vehicles comply well with Euro 6 emission limits and no statistically significant differences are found in NOx, CO, and PN emissions measured over the complete WLTP and RDE tests, both for gasoline and diesel vehicles.&lt;/div&gt;&lt;/div&gt;

Surrogate models for lithium-ion battery heat generation based on orthogonal experiments by eliminating external wire connection effect

• Maximum overestimation of 39.9% in heat generation occurs due to the wire connection at 40 °C and discharge rate of 3C. • Heat generation rate is determined by C-rate, temperature and depth of discharge with ranking order of DR > T > DOD. • Average deviation of 6.90% was obtained between present surrogate model and experimental tests beyond 0.53 W. • Heat generation model of the battery was verified by comparing with experiment measurement under natural convection. In this paper, an experimental investigation on the heat generation rate is presented for the cylindrical lithium-ion batteries under different operation conditions such as changing temperature and depth of discharge by eliminating the external connection effect. The tests were conducted by placing the battery in the thermostat chamber and connecting to the charger instrument through external wires. The intermediate quantities including the overpotential, entropy heat coefficient and internal resistance were obtained by the corresponding orthogonal test in full matrix design. It is found that the maximum overestimation of 39.9% occurs at the highest temperature of 40 °C and discharge rate of 3C with the wire connection. As such, the wire connection effect must be examined and eliminated in the heat generation estimation. The consequent sensitivity analysis indicates that the discharge rate has the largest impact on the heat generation of battery, followed by the temperature and depth of discharge, namely, DR > T > DOD. The temporal heat generating surrogate models of the battery are correlated under different operation conditions with the depth of discharge, temperature, and discharge rate by the response surface analysis, which agrees with experiment results within 6.90% for heat generation rate beyond 0.53 W corresponding to the 1C discharge case. Moreover, the heat generation model is verified by comparing the numerically obtained battery temperature with experiment measurement under natural convection condition. The temporal temperature rise of the battery during actual operation conditions such as worldwide harmonized light vehicles test procedure (WLTP) can be estimated based on the present surrogate models.

A Comparative Study on Different Online State of Charge Estimation Algorithms for Lithium-Ion Batteries

With an accurate state of charge (SOC) estimation, lithium-ion batteries (LIBs) can be protected from overcharge, deep discharge, and thermal runaway. However, selecting appropriate algorithms to maintain the trade-off between accuracy and computational efficiency is challenging, especially under dynamic load profiles such as electric vehicles. In this study, seven different widely utilized online SOC estimation algorithms were considered with the following goals: (a) to compare the accuracy of the different algorithms; (b) to compare the computational time in the simulation. Since the 2-RC battery model is highly accurate and not very computationally complex, it was selected for implementing the considered algorithms for the model-based SOC estimation. The considered online SOC estimation performance was evaluated using measurement data obtained from experimental tests on commercial lithium manganese cobalt oxide batteries. The experimental analysis consisted of a dynamic current profile comprising a worldwide harmonized light vehicle test procedure (WLTP) cycle and constant current discharging pulses. In addition, the performance of the considered different algorithms was compared in terms of estimation error and computational time to understand the challenges of each algorithm. The results indicated that the extended Kalman filter (EKF) and sliding mode observer (SMO) were the best choices because of their estimation accuracy and computation time. However, achieving the SOC estimation accuracy depended on the battery modeling. On the other hand, the estimated SOC root means square error (RMSE) using a backpropagation neural network (BPNN) was less than that using a Luenberger observer (LO). Moreover, with the advantages of BPNNs, such as no need for battery modeling, the estimation error could be further reduced using a large size dataset.

Developing an optimal sampling design to monitor the vehicle fuel consumption gap

Monitoring the fuel consumption gap between official and real-world measurements is of great interest to policy makers and researchers. This study explores how sampling methods (simple random, stratified and quota sampling) can be used to supplement and validate the monitoring. Three user datasets were utilised to simulate the fuel consumption gap of the 11.6–15.5 million vehicles registered annually in the European Union (2018–2020). Results suggest that a simple random sample of 16,240 vehicles is sufficient to estimate accurately the fleets' average fuel consumption gap. Stratified sampling can reduce the sample size to less than 4,500 vehicles. To estimate accurately the fuel consumption gap of each manufacturer, the sample size increases to approximately 17,200 vehicles. The increase in sales of plug-in hybrid vehicles in 2020 led to an increase of the average fuel consumption gap by 8% and its standard deviation (variability) by 20%. This higher variability resulted in a more than double sample size, compared to previous years. It was also found that the introduction of the Worldwide Harmonized Light vehicles Test Procedure (WLTP) reduced the average gap by 20–24%. This study highlights the viability of a sampling scheme to estimate the fuel consumption gap by monitoring less than 0.05% of the fleet. Moreover the study draws attention to the need for further analysis and understanding of the real-world use and fuel consumption of plug-in hybrid vehicles.

Dynamic Programming Based Rapid Energy Management of Hybrid Electric Vehicles with Constraints on Smooth Driving, Battery State-of-Charge and Battery State-of-Health

Dynamic programming (DP) is currently the reference optimal energy management approach for hybrid electric vehicles (HEVs). However, several research concerns arise regarding the effective application of DP for optimal HEV control problems which involve a significant number of control variables, state variables and optimization constraints. This paper deals with an optimal control problem for a full parallel P2 HEV with constraints on battery state-of-charge (SOC), battery lifetime in terms of state-of-health (SOH), and smooth driving in terms of the frequencies of internal combustion engine (ICE) activations and gear shifts over time. The DP formulation for the considered HEV control problem is outlined, yet its practical application is demonstrated as unfeasible due to a lack of computational power and memory in current desktop computers. To overcome this drawback, a computationally efficient version of DP is proposed which is named Slope-weighted Rapid Dynamic Programming (SRDP). Computational advantage is achieved by SRDP in considering only the most efficient HEV powertrain operating points rather than the full set of control variable values at each time instant of the drive cycle. A benchmark study simulating various drive cycles demonstrates that the introduced SRDP can achieve compliance with imposed control constraints on battery SOC, battery SOH and smooth driving. At the same time, SRDP can achieve up to 78% computational time saving compared with a baseline DP approach considering the Worldwide Harmonized Light Vehicle Test Procedure (WLTP). On the other hand, the increase in the fuel consumption estimated by SRDP is limited within 3.3% compared with the baseline DP approach if the US06 Supplemental Federal Test Procedure is considered. SRDP could thus be exploited to efficiently explore the large design space associated to HEV powertrains.

Development and assessment of a hydrogen car operated by a vertical axis wind turbine

ABSTRACT In this study, an electrolysis unit which is driven by a vertical wind turbine in a fuel cell passenger car is investigated for hydrogen production thermodynamically through exergy and energy approaches. The proposed system essentially consists of a vertical axis wind turbine, an electrolysis unit, and a fuel cell unit, as well as the auxiliary units. The electrical energy generated by the wind turbine is used to operate the electrolyzer for hydrogen generation. The hydrogen generated is then sent to fuel cell for driving the car and/or hydrogen tank for storage. Both energy and exergy efficiencies of the electrolysis unit are found to be approximately 63% at 975 mA/cm2 current density. Additionally, the energy and exergy efficiencies of the proposed system are calculated as 39% and 56%, respectively. Furthermore, hydrogen production amount from the electrolyzer is calculated using two different test-driving procedures, which are accepted worldwide. At the end of driving cycle, the amount of hydrogen produced per cell in the WLTP (Worldwide Harmonized Light Vehicles Test Procedure) driving cycle is calculated as 17.06 mol and in the FTP −75 (Federal Test Procedure) driving cycle is calculated as 6.88 mol. The amounts of oxygen are found as 8.53 mol for the WLTP driving cycle and 3.44 mol for the FTP-75 driving cycle, respectively.

Battery Electric Vehicle Efficiency Test for Various Velocities

Since battery electric vehicle (BEV) sales are increasing, the calculation of necessary electric power supply, and energy consumption data, and vehicle range is important. The Worldwide harmonized Light vehicles Test Procedure (WLTP) currently in use can deliver data to collect comparable energy consumption data for different vehicles on defined chassis dynamometer test cycles. Nevertheless, the energy consumption and so the range of BEVs are also dependent on the individual trajectory of the user. Therefore, five velocity profiles are developed in this work. The maximum speeds are based on typical velocities in German city traffic and extra-urban traffic. The energy required to finish a single velocity profile is assumed to be constant despite varying maximum velocities. With this kind of driving profiles it is possible to create an individual and more precise statement on the energy consumption and the range of a BEV. In this work, the profiles are driven on a chassis dynamometer with an VW e-Up. The vehicle charging efficiency is tested with two different AC charging modes and is also taken into account. The drive efficiencies of the tested vehicle are presented in dependence of the velocity profile driven. Finally the results are compared with a real-driving velocity profile and the energy consumption data obtained by the board computer of the vehicle.

A comparative study of energy management systems under connected driving: Cooperative car-following case

In this work, we propose connected energy management systems for a cooperative hybrid electric vehicle (HEV) platoon. To this end, cooperative driving scenarios are established under different car-following behavior models using connected and automated vehicles technology, leading to a cooperative cruise control system (CACC) that explores the energy-saving potentials of HEVs. As a real-time energy management control, an equivalent consumption minimization strategy (ECMS) is utilized, wherein global energy-saving is achieved to promote environment-friendly mobility. The HEVs cooperatively communicate and exchange state information and control decisions with each other by sixth-generation vehicle-to-everything (6G-V2X) communications. In this study, three different car-following behavior models are used: intelligent driver model (IDM), Gazis–Herman–Rothery (GHR) model, and optimal velocity model (OVM). Adopting cooperative driving of six Toyota Prius HEV platoon scenarios, simulations under New European Driving Cycle (NEDC), Worldwide Harmonized Light Vehicle Test Procedure (WLTP), and Highway Fuel Economy Test (HWFET), as well as human-in-the-loop (HIL) experiments, are carried out via MATLAB/Simulink/dSPACE for cooperative HEV platooning control via different car-following-linked-vehicle scenarios. The CACC-ECMS scheme is assessed for HEV energy management via 6G-V2X broadcasting, and it is found that the proposed strategy exhibits improvements in vehicular driving performance. The IDM-based CACC-ECMS is an energy-efficient strategy for the platoon that saves: (i) 8.29% fuel compared to the GHR-based CACC-ECMS and 10.47% fuel compared to the OVM-based CACC-ECMS under NEDC; (ii) 7.47% fuel compared to the GHR-based CACC-ECMS and 11% fuel compared to the OVM-based CACC-ECMS under WLTP; (iii) 3.62% fuel compared to the GHR-based CACC-ECMS and 4.22% fuel compared to the OVM-based CACC-ECMS under HWFET; and (iv) 11.05% fuel compared to the GHR-based CACC-ECMS and 18.26% fuel compared to the OVM-based CACC-ECMS under HIL.