Powertrain Efficiency Research Articles

Investigation of the Effects of Transmission Layout on Powertrain Efficiency, Performance, and Exhaust Emissions in Parallel Hybrid Vehicles

Abstract The investigation studies about increasing powertrain efficiency and reducing exhaust emissions and fuel consumption have been focused on hybrid vehicles for a few decades. However, the investigations are related to improvement of an individual powertrain component or energy management strategy for hybrid vehicles. Therefore, this study investigates the effects of the layout of the three-speed transmission and various gear selection procedures on vehicle performance, component efficiency, and exhaust emissions for same vehicle parameters, gear ratios, and capacity of the components in a parallel hybrid powertrain. Three design alternatives have been modeled and powertrain simulations have been performed according to the three different drive cycles as New European Drive Cycle (NEDC), Federal Test Procedure (FTP-72), and Istanbul Drive Cycle (IDC) using matlab®—Simulink® environment. The main simulation results are given for vehicle performance (acceleration, grading capacity, and maximum speed) and component efficiencies (battery and electric motor), brake-specific fuel consumption, and exhaust emissions of the engine as CO2, CO, HC, and NOx during the operation on the three-drive cycle. The simulation results showed that better vehicle performance, fuel consumption, and exhaust emissions can be achieved separately by changing the layout of the transmission in parallel hybrid vehicles.

On the Range Equation for Hybrid-Electric Aircraft

This paper proposes a new range equation for hybrid-electric aircraft. The paper revisits the theory of the range equation for a hybrid-electric aircraft with constant power split published earlier in the literature and proposes a new efficiency-based definition of the degree of hybridization (φ), one which includes the efficiencies of the electric or fuel-powered drivetrain. The paper shows that the efficiencies of the respective drivetrains play a significant role in the range estimation of the hybrid-electric aircraft. The paper makes use of a case study to show the relationship between battery energy density, powertrain efficiency and modification in the definition of the degree of hybridization φ with aircraft range. We show that for every aircraft design, there is a battery energy density threshold, for which the aircraft range becomes independent of the degree of hybridization. Below this threshold, the range decreases with an increase in the degree of hybridization. Conversely, beyond this threshold, the aircraft range increases with the degree of hybridization. Our study finds that the new definition of φ has shifted this threshold significantly upwards compared to earlier publications in the literature. This makes the design of an aircraft with a high degree of hybridization less optimistic.

Improved efficiency with adaptive front and rear axle independently driven powertrain and disconnect functionality

Front and rear axle independently driven (FRID) powertrains are becoming a popular solution for electric vehicles (EVs) due to torque distribution capability which can enhance powertrain energy efficiency. Typically, permanent magnet synchronous machines (PMSMs) are used for FRID powertrains due to their high torque, and power density. However, the drive-cycle efficiency of FRID powertrains with PMSMs is typically reduced in comparison to single motor drives. This is due to the unwanted no-load losses of PMSMs in the field weakening region. To overcome this drawback of PMSM FRIDs, this paper proposes an adaptive front- and rear-axle independently driven (AFRID) powertrain, utilizing two dog clutches, so that the powertrain can be operated in different modes (rear, front, and all-wheel drive) by adaptively connecting and disconnecting the front and/or rear electric drive unit (EDU). A rule-based mode selection strategy is developed to utilize the flexibility of different powertrain operating modes of the powertrain for maximizing the energy efficiency of the EDU. The simulation results show that the suggested AFRID powertrain, in comparison to a common FRID powertrain, can improve the WLTC drive-cycle consumption from 22.17 kWhh to 20.50 kWhh per 100 km. Based on the route and road-load information, the energy-saving potential of the AFRID powertrain can be further improved to 20.37 kWhh per 100 km by a suggested predictive mode selection strategy, achieving an optimal mode selection.

Assessment of a Second Life City Vehicle Refurbished to Include Hybrid Powertrain Technology

Due to increased powertrain efficiency, electrified propulsion has seen significant diffusion in the automotive sector in recent years. Despite the possible reduction in tailpipe CO2 emissions, the advancements in the technology are not sufficient to tackle the challenge of global greenhouse emissions. An additional action could be the use of second life vehicles to drastically reduce the emissions associated with vehicle manufacturing and recycling/disposal. Urban vehicles are the most suitable to be electrified due to the large start-and-stop cycling and the possibility of using regenerative braking. Therefore, this work considered the hypothesis of hybridizing a small size passenger car with parallel and Series technology. The powertrain is designed for an old vehicle suitable for second life use after refurbishment. A numerical model of the propulsion components was built and applied after previous validation in homologation conditions. Several urban cycles representative of European cities were considered. The final hybrid model is compared with two baselines: non-hybrid and pure electric version already lunched in the market by the manufacturer. The findings indicate that used HEV cars could be a viable option for cutting CO2 emissions from city vehicles without reducing their range. In comparison to non-hybrid vehicles, the series can typically reduce CO2 emissions by 41%, compared to the P2’s 32%.

Multi-Objective Real-Time Optimal Energy Management Strategy Considering Energy Efficiency and Flexible Torque Response for a Dual-Motor Four-Drive Powertrain

To exhaust the potential of energy efficiency and dynamic performance of the dual-motor four-drive powertrain, this study developed a multi-objective real-time optimal energy management strategy considering energy efficiency and flexible torque response. First, a theoretical analysis of energy loss and operating characteristics was performed to elucidate the energy-saving advantages and control challenges of the dual-motor four-drive powertrain. Second, an economic strategy based on the adaptive nonlinear particle swarm optimization (ANLPSO) and optimization freezing tolerance mechanism was devised to realize real-time optimal power distribution. Then, the pre-shifting recognition schedule and gradient torque recovery strategy were developed to achieve flexible torque response during gear shifting. Finally, smooth switching logic was created to assure a seamless transition between the two strategies. Numerous simulation results indicate that compared with the single-motor drive strategy, the proposed strategy can increase energy efficiency by 8.1%, 4.02%, and 9.49% under NEDC, WLTC, and CLTC, respectively. During shifting, the longitudinal acceleration and jerk of the proposed strategy are significantly superior to those of the original strategy, thereby enhancing the vehicle’s dynamic performance and ride comfort. The results of the drum experiment validate the efficacy of the proposed method for energy consumption optimization and torque coordination control in the actual vehicle environment.

Modified Particle Swarm Optimization Based Powertrain Energy Management for Range Extended Electric Vehicle

The efficiency of hybrid electric powertrains is heavily dependent on energy and power management strategies, which are sensitive to the dynamics of the powertrain components that they use. In this study, a Modified Particle Swarm Optimization (Modified PSO) methodology, which incorporates novel concepts such as the Vector Particle concept and the Seeded Particle concept, has been developed to minimize the fuel consumption and NOx emissions for an extended-range electric vehicle (EREV). An optimization problem is formulated such that the battery state of charge (SOC) trajectory over the entire driving cycle, a vector of size 50, is to be optimized via a control lever consisting of 50 engine/generator speed points spread over the same 2 h cycle. Thus, the vector particle consisted of the battery SOC trajectory, having 50 elements, and 50 engine/generator speed points, resulting in a 100-D optimization problem. To improve the convergence of the vector particle PSO, the concept of seeding the vector particles was introduced. Additionally, further improvements were accomplished by adapting the Time-Varying Acceleration Coefficients (TVAC) PSO and Frankenstein’s PSO features to the vector particles. The MATLAB/SIMULINK platform was used to validate the developed commercial vehicle hybrid powertrain model against a similar ADVISOR powertrain model using a standard rule-based PMS algorithm. The validated model was then used for the simulation of the developed, modified PSO algorithms through a multi-objective optimization strategy using a weighted sum fitness function. Simulation results show that a fuel consumption reduction of 12% and a NOx emission reduction of 35% were achieved individually by deploying the developed algorithms. When the multi-objective optimization was applied, a simultaneous reduction of 9.4% fuel consumption and 7.9% NOx emission was achieved when compared to the baseline model with the rule-based PMS algorithm.

Practical Energy Management Control of Fuel Cell Hybrid Electric Vehicles Using Artificial-Intelligence-Based Flatness Theory

This paper proposes a practical solution to address the energy management issue in fuel cell hybrid electric vehicles (FCHEVs). This solution revolves around a powertrain system that contains a fuel cell (FC) as the main supply, a photovoltaic cell (PC) as the secondary energy source, and a battery bank (Batt) as backup storage to compensate for the FC’s low response rate. The energy in this hybrid powertrain system alternated between the designated elements and the load via a DC bus, and to maintain a stable output voltage, the DC link was adjusted using a nonlinear approach that is based on the flatness theory and the nonlinear autoregressive moving average (NARMA-L2) neuro-controller. As for the current regulation loops, the sliding mode technique was employed to attain the high dynamic of the reference signals produced by the energy manager loop. To validate the accuracy of the proposed energy management approach (EMA), a test bench was equipped with digital, electronic circuits and a dSPACE DS-1104 unit. This experimental bench contained a fuel cell emulator FC of 1200 W and 46 A, lithium-ion batteries of 24 V, and a solar source capable of 400 W. The obtained results, indeed, attested to the validity of the approach used, yielding a notable performance during multiple charge variations. This ultimately demonstrated that the management approach enhanced the efficiency of the hybrid powertrain.

Maximizing Efficiency in Smart Adjustable DC Link Powertrains with IGBTs and SiC MOSFETs via Optimized DC-Link Voltage Control

In recent years, the push towards electrifying transportation has gained significant traction, with battery-electric vehicles (BEVs) emerging as a viable alternative. However, the widespread adoption of BEVs faces multiple challenges, such as limited driving range, making powertrain efficiency improvements crucial. One approach to improve powertrain energy efficiency is to adjust the DC-link voltage using a DC-DC converter between the battery and inverter. Here, it is necessary to address the losses introduced by the DC-DC converter. This paper presents a dynamic programming approach to optimize the DC-link voltage, taking into account the battery terminal voltage variation and its impact on the overall powertrain losses. We also examine the energy efficiency gains of IGBT-based and silicon carbide (SiC) MOSFET-based adjustable DC-link voltage powertrains during WLTC driving cycles through PLECS and Matlab/Simulink simulations. The findings indicate that both IGBT and MOSFET-based adjustable DC-link voltage powertrains can enhance the WLTC drive-cycle efficiency up to 2.51% and 3.25% compared to conventional IGBT and MOSFET-based powertrains, respectively.

Commuting-pattern-oriented stochastic optimization of electric powertrains for revealing contributions of topology modifications to the powertrain energy efficiency

The performance of an electric vehicle (EV) powertrain is greatly influenced by the design scheme, control strategy, and driving conditions. Owing to uncertainties in driving conditions and the mutual correlation between design scheme and control strategy, identifying the optimal topology and component parameters of EV powertrains for improving the energy efficiency of EVs remains a challenging task. Oriented to the commuting patterns of individuals, this paper proposes a stochastic optimization method for identifying the superior powertrain design that optimizes the expected energy economy of the EV powertrain and reduces the energy economy variability in random driving conditions. This paper also introduces a machine-learning solution to the complex mutual restrictions among design variables, control strategies, and driving conditions, which determines the feasible design space and powertrain performance. Suggested by massive validations, the stochastic optimization method reduces the expectation and deviation of the energy consumption of EV powertrains up to 12.0% and 5.6%, compared with deterministic optimization methods. Additionally, by comparative evaluation among stochastic optimal design results of different topologies, contributions of topology modifications to the energy efficiency of EV powertrains are clarified, and the superior powertrain topology is identified, which improves 17.1% expected energy economy compared with the current popular topology.

Optimization of multi-speed transmission for electric vehicles based on electrical and mechanical efficiency analysis

Multi-speed transmissions for electric vehicles (EVs) can better improve EV efficiency and performance in comparison with conventional single-speed transmissions. To achieve a superior multi-speed transmission design, gear ratios and shifting patterns should be optimized by considering the electrical and mechanical efficiencies. Because most powertrain losses occur in the inverter, motor, and transmission, loss analysis was conducted according to the characteristics of each component. To confirm the effects of these component efficiencies on EVs, an EV efficiency analysis was performed based on three cases of powertrain efficiency by considering the variable efficiency of each component combination: i) motor only, ii) motor and transmission, and iii) inverter, motor, and transmission. An optimization problem was formulated by adopting design variables, such as gear ratios and shifting patterns, and the objectives of energy consumption and acceleration ability. An artificial neural network (ANN)-based multi-objective optimization process was proposed as an alternative to the excessive effort for calculation in the optimization process. To verify the importance of considering the efficiency of various powertrain components, transmission optimization was performed for the variable efficiency of each component combination. When comparing the highest efficiency solutions of the cases considering only part of component efficiency with the case considering all components, the energy consumption and acceleration ability of the latter case were found to be superior by approximately 1.7% and 5.1%, respectively. Furthermore, since the computational time for the EV and ANN models required approximately nine months and one day, respectively, this result demonstrates the effectiveness of the ANN-based multi-objective optimization process.

Discrete Mixed-Integer Shooting (DMIS): Algorithm and Application to Plug-In Hybrid Electric Vehicle Energy Management Accounting for Fuel Cranking and Actual Powertrain Efficiency Maps

This article presents an algorithm for mixed-integer nonlinear optimal control problems (OCPs) formulated directly in discrete time. In our current application, we address the energy management of a plug-in hybrid electric vehicle (PHEV) seeking to achieve minimum trip fuel consumption under a terminal state-of-charge (SOC) equality constraint while avoiding powertrain efficiency map approximations in order to maximize its fuel economy (FE). An additional engine cranking state with a nonsmooth state and control transition cost is then incorporated to prevent undesired engine on/off behaviors. We pursue a single shooting-based numerical algorithm, motivated by the success of the computationally efficient Pontryagin’s Minimum Principle (PMP)-based single shooting in achieving similar FE results as dynamic programming (DP) for the power-split optimization problem without the additional engine cranking state shown in prior work. The Discrete Mixed-Integer Shooting (DMIS) algorithm, with costate backward-in-time propagation, is presented here, avoiding unstable shooting iterations that are typically encountered in PMP-based single shooting. When applying DMIS to solve the power-split problem with cranking fuel consideration, we show through simulation that it leads to better FE results than the PMP-based single shooting without the cranking state, thus demonstrating the effectiveness of the DMIS algorithm. Finally, experimental dynamometer test results with the model predictive powertrain controller solved by DMIS for minimum trip fuel consumption are presented and discussed, demonstrating its real-time execution and the cranking fuel reduction capability.

Novel Energy Management Control Strategy for Improving Efficiency in Hybrid Powertrains

Energy management in electrified vehicles is critical and directly impacts the global operating efficiency, durability, driveability, and safety of the vehicle powertrain. Given the multitude of components of these powertrains, the complexity of the proper control is significantly higher than the conventional internal combustion engine vehicle (ICEV). Hence, several control algorithms and numerical methods have been developed and implemented in order to optimize the operation of the hybrid powertrain while complying with the required boundary conditions. In this work, a model-based method is used for predicting the impacts of a set of possible control actions, choosing the one minimizing the associated costs. In particular, the energy management technique used in the present study is the equivalent consumption minimization strategy (ECMS). The novelty of this work consists of taking into account the thermal state of the ICE for optimization. This feature was implemented by means of an extensive experimental campaign at different coolant temperatures of the ICE to calibrate the additional fuel consumption due to operating the engine outside of its optimum temperature. The results showed significant gains in both WLTC and RDE cycles.

Hierarchical eco‐driving control strategy for hybrid electric vehicle platoon at signalized intersections under partially connected and automated vehicle environment

AbstractIn recent years, eco‐driving for hybrid electric vehicles (HEVs) has been studied with the emerging connected and automated vehicle (CAV) technologies to improve the mobility, fuel economy and safety of HEVs. This paper develops a hierarchical eco‐driving control strategy for HEV platoons consisting of CAVs and human‐driven vehicles (HDVs) to improve fuel economy at signalized intersections. For each platoon, the speed trajectories of CAVs are optimized in the upper layer controller using model predictive control (MPC) to minimize the total fuel consumption of the whole platoon. For each HEV, the optimal power split between the engine and the battery is obtained in the lower layer controller using adaptive equivalent consumption minimization strategy (A‐ECMS). The time‐varying powertrain efficiencies of HEVs are explicitly considered in the speed trajectory optimization. At last, simulation studies are conducted using MATLAB and VISSIM to evaluate the performances of the strategy in mixed traffic scenarios and different CAV penetration rates. Simulation results indicate that compared with the single vehicle control strategy, the proposed strategy can improve the average fuel economy by up to 8.34% and considering the time‐varying powertrain efficiencies of HEVs in the optimization can further reduce the fuel consumption by up to 1.23%.

A Review of Fuel Cell Powertrains for Long-Haul Heavy-Duty Vehicles: Technology, Hydrogen, Energy and Thermal Management Solutions

Long-haul heavy-duty vehicles, including trucks and coaches, contribute to a substantial portion of the modern-day European carbon footprint and pose a major challenge in emissions reduction due to their energy-intensive usage. Depending on the hydrogen fuel source, the use of fuel cell electric vehicles (FCEV) for long-haul applications has shown significant potential in reducing road freight CO2 emissions until the possible maturity of future long-distance battery-electric mobility. Fuel cell heavy-duty (HD) propulsion presents some specific characteristics, advantages and operating constraints, along with the notable possibility of gains in powertrain efficiency and usability through improved system design and intelligent onboard energy and thermal management. This paper provides an overview of the FCEV powertrain topology suited for long-haul HD applications, their operating limitations, cooling requirements, waste heat recovery techniques, state-of-the-art in powertrain control, energy and thermal management strategies and over-the-air route data based predictive powertrain management including V2X connectivity. A case study simulation analysis of an HD 40-tonne FCEV truck is also presented, focusing on the comparison of powertrain losses and energy expenditures in different subsystems while running on VECTO Regional delivery and Longhaul cycles. The importance of hydrogen fuel production pathways, onboard storage approaches, refuelling and safety standards, and fleet management is also discussed. Through a comprehensive review of the H2 fuel cell powertrain technology, intelligent energy management, thermal management requirements and strategies, and challenges in hydrogen production, storage and refuelling, this article aims at helping stakeholders in the promotion and integration of H2 FCEV technology towards road freight decarbonisation.

Analysis of Energy Management for Hybrid Electric Vehicle

This paper describes the methods of improving the efficiency of the powertrain like the energy management system and the control of a Parallel Hybrid Powertrain, in order to address the growing problem of carbon emissions from vehicles on the earth and protect the environment. These developments and improvements are necessary for vehicles. Then further analyzed the previous developments on the powertrain efficiency improving method, predicted and made the future plan for the next step of research by using the growing trend on the global market on vehicles.

Predictive Optimal Control of Mild Hybrid Trucks

Fuel consumption, subsequent emissions and safe operation of class 8 vehicles are of prime importance in recent days. It is imperative that a vehicle operates in its true optimal operating region, given a variety of constraints such as road grade, load, gear shifts, battery state of charge (for hybrid vehicles), etc. In this paper, a research study is conducted to evaluate the fuel economy and subsequent emission benefits when applying predictive control to a mild hybrid line-haul truck. The problem is solved using a combination of dynamic programming with backtracking and model predictive control. The specific fuel-saving features that are studied in this work are dynamic cruise control, gear shifts, vehicle coasting and torque management. These features are evaluated predictively as compared to a reactive behavior. The predictive behavior of these features is a function of road grade. The result and analysis show significant improvement in fuel savings along with NOx benefits. Out of the control features, dynamic cruise (predictive) control and dynamic coasting showed the most benefits, while predictive gear shifts and torque management (by power splitting between battery and engine) for this architecture did not show fuel benefits but provided other benefits in terms of powertrain efficiency.

Modeling and design optimization of a hybrid power generator for full-electric naval propulsion

The purpose of this work is to build a model of diesel engine useful for studying new hybrid solutions for power generation. This kind of study is of significant interest particularly for the marine sector, due to recent limitations on fuel consumption and constraints imposed on the minimum powertrain efficiency. In this paper a variable-speed diesel generator model is presented. The model simulates both the dynamic behavior of the engine and the fuel consumption during the operating cycle. This model was developed using experimental results of bench tests conducted on the VL1716C2-MLL engine by Isotta Fraschini Motori (IFM) SpA. Diesel engine dynamics is based on the formulation found in the technical literature on mean value models and then adjusted through experimental data. Special attention was paid to developing the procedure to find the minimum specific fuel consumption for a given load. Initially, the presented resutls concern the correct response of the model under different load conditions. Then, the improvement in specific fuel consumption (obtained by adjusting the engine speed according to the load), in comparison with fixed speed operation, is shown.

Hybrid powertrains with dedicated internal combustion engines are the perfect basis for future global mobility demands

In order to still reach the 1.5°C or at least 2°C target, a rapid and significant reduction in the CO2 contribution of all sectors must be achieved. The transport sector in particular has a duty here, as it contributes around 20% to global CO2 emissions and this figure has increased rather than decreased in recent decades as a result of a drastic increase in passenger and freight kilometers.The automotive industry is already making a strong contribution to this through an increasing number of electric vehicles in new vehicle registrations. However, this will not be sufficient, because on the one hand the increase of vehicles in the market is due to several reasons happening not fast enough, and on the other hand the electricity available for the vehicles will not be generated fast enough from fully renewable electricity sources.So as long the supply of regenerative electricity is not sufficient, and as long there are above all many places and applications in the world where a switch to pure electromobility will not be possible in the short and medium term, other highly efficient drive systems will have to be offered in parallel. Highly efficient because only in this way can the valuable energy, from whatever source, be used extremely sparingly.In order to evaluate to what extent hybrid powertrains can contribute to a reduction of the CO2 pollution of the transport sector, this paper deals with the virtual investigation of different hybrid powertrains. In a first step, it is deduced why hybrid powertrains are fundamentally interesting and what potential they have if they are consistently optimized for maximum efficiency. It becomes clear that there is considerable potential in them and that, with consistent further development, they can actually come very close to the future CO2 fleet limits. To achieve this, however, all powertrain components must be optimized. For this reason, a further section presents technologies, such as Pre-Chamber Spark Plugs (PCSP) and technology packages for internal combustion engines that have been optimized specifically for use in hybrid powertrains and achieve Break Thermal Efficiency (BTE) levels of more than 45%.Finally, in the last major chapter, various hybrid topologies are combined with the presented high-efficiency internal combustion engines from chapter 2 and are evaluated in a large simulation study. With the aim to achieve a maximum overall powertrain efficiency under real world driving conditions a combination of a parallel and a serial topology seems most promising.Due to their efficiency and conservation of energy resources, hybrid powertrains have the potential to meet a significant share of future global mobility needs.

Battery electric vehicles: Progress, power electronic converters, strength (S), weakness (W), opportunity (O), and threats (T)

• Background about different available EVs is presented.Various power converter electronics used in BEV are discussed. • Strengths (S), weaknesses (W), opportunities (O), and threats (T) of the EV is presented. • SWOT analysis of the BEV and their prospects in the automotive industry are introduced. The rely on internal combustion engines is gradually decreased with the recent evolution of electric vehicles (EVs) in the automotive industry. Electric motors are replacing the energy systems mainly to improve the powertrain's efficiency and ensure they are environmentally friendly. These novel powertrains are designed to operate solely on batteries or supercapacitors. For these types of EVs, the battery is charged using an alternating current supply in connection to the grid in the case of plug-in electric vehicles. Internal combustion engines are equally used for some hybrid vehicles. Charging of the battery can also be carried out via regenerative braking from the traction motor. This study presents a brief background about the different available EVs, detailed information on various power converter electronics used in battery electric vehicles, and a summary of the strengths (S), weaknesses (W), opportunities (O), and threats (T) of the EV is presented. Moreover, SWOT analysis of the battery electric vehicles (BEV) and their prospects in the automotive industry are introduced.

Trip-level energy consumption prediction model for electric bus combining Markov-based speed profile generation and Gaussian processing regression

Electric bus energy consumption prediction is conducive to bus route scheduling and charging optimization management. However, existing prediction models do not comprehensively consider vehicle parameters, auxiliary power, speed profile and external factors. In particular, there is a lack of in-depth research on generating speed profile based on low-resolution data for electric bus. The aim of this study involves predicting bus trip-level energy consumption. Thus, the temperature effect on powertrain efficiency and auxiliary power was first analyzed, and then a Markov-based speed profile generation method considering the speed state transition spatiotemporal characteristics and driving style was developed. Reliable process-related variables extracted from the aforementioned model development in conjunction with external-related variables are used as the input of Markov-based Gaussian processing regression model (M-GPR). Finally, 7919 regular bus driving samples on the same route were extracted from the raw bus operation dataset to verify the model effectiveness in improving the prediction accuracy of trip-level energy consumption. The results indicate that M-GPR exhibits a better prediction performance with an average prediction error of 8.69%, which decreases by 0.62%–5.41% compared with other models. Furthermore, M-GPR presents wide applicability, with average errors between 7.01% and 9.90% in different operating scenarios.