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The purpose of this study is to monitor the battery performance of plug-in hybrid electric vehicles (PHEVs) on a chassis dynamometer using the US06, NEDC, and EPA highway driving cycles. The chassis dynamometer simulates vehicle operation and driving conditions and allows for precise simulation of pre-defined driving cycles, including simulations of acceleration, deceleration, stopping, and re-acceleration on the road. In the case of the US06 driving cycle, the results for (EV mode) compared with energy consumption during electric testing revealed a consistent decrease in the SOC (state of charge) due to the rapid response of the electric motor distribution to the changing power, as well as electric power fluctuations during driving conditions. Under the NEDC, the test results for electric power (EV) compared with energy consumption during electric testing revealed that the SOC gradually decreased at the start of the test due to low driving speeds. Towards the end, at around 800 s, an increase in driving speed resulted in a noticeable drop in SOC. The electric power varied during the driving cycle in this test due to the motor’s rapid response to changes in power distribution while driving. For the EPA Highway driving cycle test, the test results for electric power (EV) compared with energy consumption during continuous electric testing indicated a gradual decrease in the SOC at first due to low driving speeds. As the driving speed increased after about 300 s, the SOC rapidly decreased. Because of the motor’s quick response to changes in the power distribution while driving, the electric power varied according to the driving cycle.

Accurate driving cycles are key for effectively evaluating electric vehicle performance. The K-means algorithm is widely used to construct driving cycles; however, this algorithm is sensitive to outliers, and determining the K value is difficult. In this paper, a novel driving cycle construction method based on principal component analysis and hierarchical clustering is proposed. Real road vehicle data were collected, denoised, and divided into vehicle microtrip data. The eigenvalues of the microtrips were extracted, and their dimensions were reduced through principal component analysis. Hierarchical clustering was then performed to classify the microtrips, and a representative set of microtrips was randomly selected to construct the driving cycle. The constructed driving cycle was verified and compared with a driving cycle constructed using K-means clustering and the New European Driving Cycle. The average relative eigenvalue error, maximum speed acceleration probability distribution difference rate, average cycle error, and simulated relative power consumption error per 100 km between the hierarchical driving cycle and the real road data were superior to those of the K-means driving cycle, which indicated the effectiveness of the proposed method. Though the methodology proposed in this paper has not been verified in other regions, it provided a certain reference value for other research of the developing driving cycle.

Emissions reductions from automotive vehicles are significant in protecting the environment. This study presents the development of new Indian motorcycle driving cycles (IMDC-1 & IMDC-2) based on the maximum speed and average speed of micro-trips. Time-speed data were collected using popular make motorcycles fitted with GPS on four different routes in Delhi-National Capital Region (NCR). The new IMDC cycles are comparable to WMTC when comparing the driving cycle parameters; however, they differ significantly from WMTC with a higher rate of acceleration and decoration. The fuel economy of new cycles was lower with higher CO and HC with insignificant change in NOx emissions compared to WMTC. The higher emissions with fuel penalty may be due to the higher rate of acceleration and deceleration experienced by the vehicles on Indian roads. Compared to WMTC, the IMDC cycles were found severe and very close to on-road driving patterns on comparing the driving cycle parameters; this indicates that the new cycles represent the real-world driving patterns experienced by vehicles in India. The new WMTC cycles can estimate realistic emissions factors that will support policymakers to form a new framework to reduce vehicular emissions to protect the environment.

The challenges in the development of plug-in electric vehicle (PEV) powertrains are efficient energy management and optimum energy storage, for which the role of driving cycles that represent driver behaviour is instrumental. Discrepancies between standard driving cycles and real driving behaviour stem from insufficient data collection, inaccurate cycle construction methodology, and variations because of geography. In this study, we tackle the first issue by using the collected data from real-world driving of a fleet of 76 cars for more than one year in the city of Winnipeg (Canada), representing more than 44 million data points. The second issue is addressed by a proposed novel stochastic driving cycle construction method. The third issue limits the results to mainly Winnipeg and cities that have similar features, but the methodology can be used anywhere. The methodology develops the driving cycle using snippets extracted from recorded time-stamped speed of the vehicles from the collected database. The proposed Winnipeg Driving Cycle (WPG01) characteristics are compared to eight existing standard driving cycles and are more able to represent aggressive driving, which is critical in PEV design. An attempt is made to isolate how many differences could be attributed to the sample size and the methodology. The proposed construction methodology is flexible to be optimized for any selection of driving parameters and thus can be a recommended approach to develop driving cycles for any drive train topology, including internal combustion engine vehicles, hybrid vehicles, plug-in hybrid, and battery electric vehicles. Characterization of vehicle parking durations and types of parking (home, work, shopping), critical for duty cycles for PEV powertrains, are reported elsewhere. Here, the focus is on the mathematical approach to develop a drive cycle when a large database with high resolution of driving data is available.

A program to develop a harmonized light duty test cycle that will represent typical conditions for Malaysia is going to be developed, named Malaysia Driving Cycle (MDC). The development of MDC will adapt the methodology from the worldwide harmonized light duty driving test cycle (WLTC). Terengganu has been chosen as one of the regions for in-use driving data collection. In developing MDC, the route selection phase must be carried out first before proceeding to the route data collection. This paper describes briefly on the methodology used for the route selection, the selected routes and the driving conditions for route data collection in Terengganu region. Road Traffic Volume Malaysia (RTVM) version 2015 and Google Maps are used to study the route selection in Terengganu region. By adapting the methodology of WLTC, 11 routes in the Terengganu region have been selected to contribute the speed-time data for development of Malaysia Driving Cycle (MDC). The selected routes consist of urban, rural, and motorway road. For the route data collection, the driving conditions (peak, off-peak, weekend) are considered to collect the samples for each route and direction.

In order to evaluate fuel consumption and tailpipe emission of a vehicle, standard driving cycles are used to prescribe vehicle driving condition such as speed, gear shift, fluid temperature and so on. New european driving cycle (NEDC) has prevailed as the only driving cycle for emission and fuel consumption while Federal Test Procedure 75(FTP-75) mode is used in the United States. In South Korea, NEDC is applied for emission certification and FTP-75 mode is used for fuel consumption of a vehicle powered by diesel engine. Because these driving cycles are mixed of static phase (cruising and idle) and transient phase (acceleration and deceleration), they need to be transformed to static engine operation condition so that optimization is possible using engine dynamometer for each representative engine operation condition. This study set up two models to convert vehicle driving conditions to engine operation condition based on work which the engine should produce to follow the driving cycle and based on representative vehicle speed of NEDC. Accuracy of each model was compared with actual vehicle test result on a chassis dynamometer and the characteristics of each model were analyzed.

Hybrid Electric Vehicles (HEVs) have been proven to be a promising solution to environmental pollution and fuel savings. The benefit of the solution is generally realized as the amount of fuel consumption saved, which by itself represents a challenge to develop the right energy management strategies (EMSs) for HEVs. Moreover, meeting the design requirements are essential for optimal power distribution at the price of conflicting objectives. To this end, a significant number of EMSs have been proposed in the literature, which require a categorization method to better classify the design and control contributions, with an emphasis on fuel economy, providing power demand, and real-time applicability. The presented review targets two main headlines: (a) offline EMSs wherein global optimization-based EMSs and rule-based EMSs are presented; and (b) online EMSs, under which instantaneous optimization-based EMSs, predictive EMSs, and learning-based EMSs are put forward. Numerous methods are introduced, given the main focus on the presented scheme, and the basic principle of each approach is elaborated and compared along with its advantages and disadvantages in all aspects. In this sequel, a comprehensive literature review is provided. Finally, research gaps requiring more attention are identified and future important trends are discussed from different perspectives. The main contributions of this work are twofold. Firstly, state-of-the-art methods are introduced under a unified framework for the first time, with an extensive overview of existing EMSs for HEVs. Secondly, this paper aims to guide researchers and scholars to better choose the right EMS method to fill in the gaps for the development of future-generation HEVs.

Electric vehicles are the most innovative and promising area of the automotive industry. The efficiency of a traction battery is an important factor in the performance of an electric vehicle. This paper presents a mathematical model of an electric truck, including modules for the traction battery to determine the depth of battery discharge during the operation of the electric truck, a traction electric system for the electric truck and a system for calculating traction forces on the shaft in electric motors. As a result of the modelling, the charging and discharging currents of an accumulator battery in a real cycle of movement in peak and nominal modes of operation in electric motors and at different voltages of the accumulator battery are determined. A functional scheme of a generalized model of the electric vehicle traction electrical equipment system is developed. An experimental battery charge degree, torques of asynchronous electric motors, temperature of electric motors and inverters, battery voltage and the speed of electric motors have been measured and analysed. The developed complex mathematical model of an electric vehicle including a traction battery, two inverters and two asynchronous electric motors integrated into an electric portal bridge allowed us to obtain and study the load parameters of the battery in real driving cycles. Data were verified by comparing simulation results with the data obtained during driving.

This paper evaluates the electromagnetic and thermal performance of several traction motors for electric vehicles (EVs). Two different driving cycles are employed for the evaluation of the motors, one for urban and the other for highway driving. The electromagnetic performance to be assessed includes maximum motor torque output for vehicle acceleration and the flux weakening capability for wide operating range under current and voltage limits. Thermal analysis is performed to evaluate the health status of the magnets and windings for the prescribed driving cycles. Two types of traction motors are investigated: two interior permanent magnet motors and one permanent magnet-assisted synchronous reluctance motor. The analysis results demonstrate the benefits and disadvantages of these motors for EV traction and provide suggestions for traction motor design. Finally, experiments are conducted to validate the analysis.

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.