Vehicle Power Requirements Research Articles

Optimization of energy management strategy for extended range electric vehicles using multi-island genetic algorithm

This study aims to improve the fuel economy of extended range electric vehicles (EREVs) and reduce the cumulative battery workload. Energy management strategy (EMS) of EREVs has a significant impact on improving the energy efficiency, prolonging the service life of batteries, and reducing the fuel consumption. To the best knowledge of the authors, most of existing studies are aimed at optimizing fuel economy, but few researches have taken the service life of the battery into account while improving fuel economy. Our study reflects the power fluctuation range of the battery from the perspective of the battery current, and also further analyzes it from the perspective of the battery energy flow. On the premise of meeting the vehicle power requirement, matrix calculation studies are carried out on the transmission ratio and the key parameters of EMS in the cooperative operation mode of hybrid energy storage system (HESS) based on regular EMS and auxiliary power unit (APU) based on equivalent fuel consumption minimum strategy (ECMS). Via Simulink software, the corresponding vehicle EMS model is developed, and the joint simulation platform of AVL Cruise and Simulink is constructed to verify the effectiveness of the proposed EMS. In order to further tap the energy-saving potential of the proposed strategy based on HESS, the multi-island genetic algorithm is adopted. Under WLTP working condition, the global optimization is conducted with the objectives of minimizing the equivalent fuel consumption and the cumulative ampere-hour through the battery when the vehicle adopts HESS and APU & ECMS operate in concert operation mode. The correlation analysis of the optimization variables is performed. The results show that the fuel economy of the optimized operation mode under WLTP condition is increased by 4.49 %, and the cumulative ampere-hour through the battery is reduced by 11.37 %.

Hyperparameter Optimization Based Deep Belief Network for Clean Buses Using Solar Energy Model

Renewable energy has become a solution to the world’s energy concerns in recent years. Photovoltaic (PV) technology is the fastest technique to convert solar radiation into electricity. Solar-powered buses, metros, and cars use PV technology. Such technologies are always evolving. Included in the parameters that need to be analysed and examined include PV capabilities, vehicle power requirements, utility patterns, acceleration and deceleration rates, and storage module type and capacity, among others. PVPG is intermittent and weather-dependent. Accurate forecasting and modelling of PV system output power are key to managing storage, delivery, and smart grids. With unparalleled data granularity, a data-driven system could better anticipate solar generation. Deep learning (DL) models have gained popularity due to their capacity to handle complex datasets and increase computing power. This article introduces the Galactic Swarm Optimization with Deep Belief Network (GSODBN-PPGF) model. The GSODBN-PPGF model predicts PV power production. The GSODBN-PPGF model normalises data using data scaling. DBN is used to forecast PV power output. The GSO algorithm boosts the DBN model’s predicted output. GSODBN-PPGF projected 0.002 after40 h but observed 0.063. The GSODBN-PPGF model validation is compared to existing approaches. Simulations showed that the GSODBN-PPGF model outperformed recent techniques. It shows that the proposed model is better at forecasting than other models and can be used to predict the PV power output for the next day.

Three-Dimensional Fluctuating Flow of a Second-Grade Fluid along an Infinite Horizontal Plate with Periodic Suction

Laminar flow control plays a vital role in reducing drag resulting enhancement in the vehicle power by a significant amount. Theoretical and experimental studies have proven that the transition from laminar to turbulent flow (causing the drag coefficient to enhance) may be delayed/prevented by the suction of the fluid from the boundary layer to the wall. The purpose of this work is to study the effects of periodic suction on the unsteady 3-dimensional fluctuating flow of a second grade incompressible fluid flowing laminarly over a horizontal porous infinite plate. The plate is subjected to a sinusoidal transverse suction velocity while the free stream velocity oscillates in time about a constant mean. The flow turns out to be 3-dimensional because of transverse direction of the periodic suction velocity. By the series expansion method, analytic expressions for transient velocity, skin friction components, and pressure are attained. Impact of nondimensional parameters evolving in the mathematical model of the problem on these physical quantities are visualized graphically and discussed analytically. The velocity component u is found to be rising with a growth in suction parameter α. The pressure is noted to be growing with a growth in Reynolds number R e . Further, due to suction at the plate and transient free stream velocity, the pressure increases near the plate and then reaches to a steady value far-off the plate. The skin friction along the main flow is noticed to be decreasing with the rise in α for 0.5 ≤ R e ≤ 1.4 , however, an exponential rise is observed for R e > 1.4. The skin friction along the cross flow is noted to be enhancing for the rising values of suction parameter and elastic parameter. The present results have excellent agreement with previous published results in the limiting sense. This study is beneficial in designing and manufacturing laminar flow control system to enhance the vehicle power requirement.

Energy management for hybrid energy storage system in electric vehicle: A cyber-physical system perspective

Adoption of the hybrid energy storage system (HESS) brings a bright perspective to improve the total economy of plug-in hybrid electric vehicles (PHEVs). This paper proposes a novel energy management method to improve the total economy of PHEV by exploiting the energy storage capability of HESS. Firstly, A cyber-physical energy management framework that enables the synergistic scheduling of fuel engine, battery, and supercapacitor is designed to derive the optimal power distribution strategy for PHEV with HESS. Then, an optimization-based energy management model is established to distribute the vehicle power requirement between the fuel engine and driving motor. The reduction of fuel cost and the mitigation of the battery aging phenomenon are designed as the optimization objectives. In a further stage, an online power distribution algorithm is designed for the optimal control of HESS, where the supercapacitor is used to prolong the battery life. The qualitative and quantitative analyses indicate that both the fuel economy and battery aging cost are sensitive to the power system topology and power distribution algorithm. With the proposed methods, the PHEV total economy can be improved by 8.9% and 6.9% compared to the conventional PHEV structure and power distribution strategy while guarantee the vehicle dynamic performance.

Emission Implications of Plug-in Hybrid Electric Vehicles Through an Empirical Exploration of Engine Starts

This paper aims to characterize the engine start activity profiles and emission potential of various plug-in hybrid electric vehicle (PHEV) models by examining the characteristics associated with engine starts, identifying the travel conditions that trigger engine starts, and determining the frequency of different types of engine starts. The study analyzed on-road vehicle data from six PHEV models: Toyota Prius Plug-in, Ford C-Max Energi, Ford C-Max Fusion, Toyota Prius Prime, Chrysler Pacifica, and Chevrolet Volt. An analysis on travel conditions before engine starts revealed that low state-of-charge is the dominant engine start trigger for PHEVs with high all-electric range whereas high vehicle power requirement is the most critical trigger for PHEVs with low all-electric range. For PHEVs with mid-range capabilities, several vehicle specifications, ranging from peak electric motor power to curb weight, could be engine start determinants. A strong inverse correlation exists between battery capacity and the annual frequency of engine starts but this relationship does not hold for cold and high-power cold starts. Both the low and the high battery capacity PHEVs logged fewer cold starts than the mid-sized battery vehicles, indicating that there could be a fundamental tradeoff between engine start emissions and fuel displacement for PHEVs to a certain degree. Despite this tradeoff, all PHEV models in the study logged fewer cold starts than comparable conventional internal combustion engine vehicles, performing the same trips. Ultimately, long-range PHEVs with high battery capacity are found to be ideal for both curbing start emissions and reducing fuel use.

Validation of a GPS-based vehicle mobility power model

Recently, military vehicles have been equipped with hybrid, diesel-electric drives to improve fuel efficiency and stealth capabilities, and these vehicles require accurate power duty cycle estimates. A GPS-based mobility power and duty cycle model was developed and is used to predict the vehicle power requirements. The dynamic vehicle parameters needed to estimate the forces and power developed during locomotion are determined from the global positioning system (GPS) tracking data. Controlled tests were performed and the predicted mobility power values predicted from a GPS receiver were compared to the measured drivewheel power estimated from engine data transmitted on the vehicle's controller area network (CAN). The results from the validation tests indicated that the model was reasonably accurate in predicting the average power requirements of the vehicle.

New Optimal Power Management Strategy for Series Plug-In Hybrid Electric Vehicles

Recently Plug-in hybrid electric vehicles (PHEVs) have gained increasing attention due to their ability to reduce the fuel consumption and emissions. In this paper a new efficient power management strategy is proposed for a series PHEV. According to the battery state of charge (SOC) and vehicle power requirement, a new rule-based optimal power controller with four different operating modes is designed to improve the fuel economy of the vehicle. Furthermore, the teaching-learning based optimization (TLBO) method is employed to find the optimal engine power and battery power under the specified driving cycle while the fuel consumption is considered as the fitness function. In order to demonstrate the effectiveness of the proposed method, four different driving cycles with various numbers of driving distances for each driving cycle are selected for the simulation study. The performance of the proposed optimal power management strategy is compared with the rule-based power management method. The results verify that the proposed power management method could significantly improve the fuel economy of the series PHEV for different driving conditions.

Investigation of the effects of battery types and power management algorithms on drive cycle simulation for a range-extended electric vehicle powertrain

ABSTRACTFuel cell (FC) hybrid vehicle power trains are an attractive technology especially for automotive applications because of their higher efficiency and lower emissions compared to conventional vehicles. This study focuses on the design of an FC hybrid power train system and evaluation of its simulations for a given speed profile through two alternative power management algorithms (PMAs). Parameters suitable for a small vehicle were taken into consideration in the mathematical model of the vehicle. The proposed hybrid power train consists of an energy storage system, composed of a 4-kg battery pack (either lithium-ion (Li-ion) battery, nickel metal hydride, or nickel–cadmium) and a direct methanol fuel cell (DMFC) as the range extender. The PMAs basically aim to fulfill the power requirements of the vehicle and decide how to command the power split between the battery and the FC. The model comprising a DMFC, a battery, and PMAs was developed in MATLAB/Simulink environment. The polarization curve of the FC was obtained using a one-dimensional DMFC model. Vehicle power requirements for a drive cycle were calculated using the equations of longitudinal dynamics of vehicle, and the results were integrated into MATLAB/Simulink model. As a result of the simulations, methanol consumption, state of charge of the battery, and power output of the FC were compared for the PMAs. This comparison shows the effect of PMAs on the hybrid vehicle performance for three battery types. The results indicate that the vehicle range could be increased when proper strategy is used as PMA.

Robust proportional ECMS control of a parallel hybrid electric vehicle

Improved fuel efficiency in hybrid electric vehicles requires a delicate balance between the internal combustion engine usage and battery energy, using a carefully designed energy management control algorithm. Numerous energy management strategies for hybrid electric vehicles have been proposed in literature, with many of these centered on the equivalent consumption minimisation strategy (ECMS) owing to its potential for online implementation. The key challenge with the equivalent consumption minimisation strategy lies in estimating or adapting the equivalence factor in real-time so that reasonable fuel savings are achieved without over-depleting the battery state of charge at the end of the defined driving cycle. To address the challenge, this paper proposes a novel state of charge feedback ECMS controller which simultaneously optimises and selects the adaption factors (proportional controller gain and initial equivalence factor) as single parameters which can be applied in real time, over any driving cycle. Unlike other existing state of charge feedback methods, this approach solves a conflicting multiple-objective optimisation control problem, thus ensuring that the obtained adaptation factors are optimised for robustness, charge sustenance and fuel reduction. The potential of the proposed approach was thoroughly explored over a number of legislative and real-world driving cycles with varying vehicle power requirements. The results showed that, whilst achieving fuel savings in the range of 8.40 −19.68% depending on the cycle, final battery state of charge can be optimally controlled to within ±5% of the target battery state of charge.

Modelling driving behaviour and its impact on the energy management problem in hybrid electric vehicles

Perfect knowledge of future driving conditions can be rarely assumed on real applications when optimally splitting power demands among different energy sources in a hybrid electric vehicle. Since performance of a control strategy in terms of fuel economy and pollutant emissions is strongly affected by vehicle power requirements, accurate predictions of future driving conditions are needed. This paper proposes different methods to model driving patterns with a stochastic approach. All the addressed methods are based on the statistical analysis of previous driving patterns to predict future driving conditions, some of them employing standard vehicle sensors, while others require non-conventional sensors (for instance, global positioning system or inertial reference system). The different modelling techniques to estimate future driving conditions are evaluated with real driving data and optimal control methods, trading off model complexity with performance.

Research and Development of Engine-Generator Set Control System for Tracked Vehicle Electric Transmission System

As an energy generating equipment, the engine-generator set supplies power to the electric transmission. Therefore, its control is one of the key technologies of electric vehicles. Based on the discussion about the demands to the engine-generator set in tracked vehicles, the detailed function of engine-generator and the contro1 strategy are determined. The hardware and software of the control system are also developed and tested in a prototype vehicle. The experiment results show that the control system has good reliability and can satisfy the power requirements of vehicles under all operating conditions.

VRLA automotive batteries for stop&go and dual battery systems

The electrical power requirements for vehicles are continuing to increase and evolve. A substantial amount of effort has been directed towards the development of 36/42 V systems as a route to higher power with reduced current levels but high implementation costs have resulted in the introduction of these systems becoming deferred. In the interim, however, alternator power outputs at 14 V are being increased substantially and at the same time the requirements for batteries are becoming more intensive. In particular, stop&go systems and wire-based vehicle systems are resulting in new demands. For stop&go, the engine is stopped each time the vehicle comes to rest and is restarted when the accelerator is pressed again. This results in an onerous duty cycle with many shallow discharge cycles. Flooded lead–acid batteries cannot meet this duty cycle and valve-regulated lead-acid (VRLA) batteries are needed to meet the demands that are applied. For wire-based systems, such as brake-by-wire or steer-by-wire, electrical power has become more critical and although the alternator and battery provide double redundancy, triple redundancy with a small reserve battery is specified. In this case, a small VRLA battery can be used and is optimised for standby service rather than for repeated discharges. The background to these applications is considered and test results under simulated operating conditions are discussed. Good performance can be obtained in batteries adapted for both applications. Battery management is also critical for both applications: in stop&go service, the state-of-charge (SOC) and state-of-health (SOH) need to be monitored to ensure that the vehicle can be restarted; for reserve or back-up batteries, the SOC and SOH are monitored to verify that the battery is always capable of carrying out the duty cycle if required. Practical methods of battery condition monitoring will be described.

On-road emission rates of carbon monoxide, nitrogen oxides, and gaseous hydrocarbon

On-road emission rate measurements of carbon monoxide (CO), nitrogen oxides (NO/sub x/), and gaseous hydrocarbons (HC) from light-duty gasoline (spark-ignition) vehicles and from heavy-duty diesel vehicles operating at constant speed highway conditions are described. The measurements were made at the Allegheny Mountain Tunnel on the Pennsylvania Turnpike during July of 1981. Over 98,000 highway vehicle km were monitored during the study. The on-road results are compared with predictions from the EPA computer model MOBILE 2 and to other vehicle emissions studies. An effective vehicle speed was determined to account for the actual power requirements of vehicles going through the tunnel. The effective speed was then used in MOBILE 2 to predict the on-road results. CO and HC emission rates calculated by MOBILE 2 for low altitude compare within the standard deviations of the on-road measurements at the Allegheny elevation of 707 m, while predicted diesel NO/sub x/ is 2.3sigma above the on-road result and gasoline NO/sub x/ is 100% above the on-road upper limit. If the Allegheny elevation is sufficient to affect on-road emission rates, then MOBILE 2 also overpredicts CO emission rates by as much as 33%.

An analysis of vehicle power requirements in deep snowpack

Power requirements for tracked vehicles moving in deep medium-to-high density snow are calculated. A volumetric constitutive law is used to determine the energy absorbed by the pressure bulb under the vehicle. Also shearing effects along the pressure bulb wall and in the pressure bulb are taken into account. A parametric study is made to determine the effect of initial density, track pressure, vehicle speed and track geometry on track efficiency. The results show that if the snow material properties are expressible in terms of a constitutive law, the method presented can be of considerable use for predicting certain aspects of vehicle performance in snow and for evaluating the structure of the pressure bulb below the vehicle track.

Modeling energy and power requirements of electric vehicles

In this paper we present a model of electric vehicle performance that can be used to estimate energy and power requirements of vehicles in various driving environments. The model is developed from basic vehicle parameters and, when coupled with a simple model of battery performance, is used to examine effective ranges and per-mile traveled energy requirements of electric vehicles as compared with internal combustion powered vehicles under similar driving conditions. A number of classes of vehicles are discussed, each of which is identified by characteristic values of the model's vehicle and driving pattern parameters. A simplified driving cycle is adopted and range calculations are made on each of the vehicle classes using a simple model of battery performance for a typical lead-acid battery and for a theoretical high-performance battery. The relationships developed here not only provide a simple estimating model of vehicle performance, but also lay the groundwork for a more comprehensive simulation model, the development of which is currently in progress. The simplified model has a number of restrictions on modeling alternative driving patterns. These restrictions are discussed along with logical extensions to the model that address the restrictions. The preliminary results of the model indicate that electric vehicles, employing either current technology or high-performance batteries, may be energy efficient in urban environments. However, in inter-city applications, where internal combustion engines are most efficient, the comparative energy efficiency of electrics is minimal.

Power systems for an unmanned lunar roving vehicle.

Unmanned lunar roving vehicle power requirements, comparing radioisotope thermoelectric generator and solar cell/battery systems