Mathematical models for assessing the role of airflow on the risk of airborne infection

Mathematical models for assessing the role of air?ow on the risk of airborne infection in hospital wards

Catherine J.Noakes*and P.Andrew Sleigh

Pathogen Control Engineering Institute,School of Civil Engineering,University of Leeds,

Woodhouse Lane,Leeds LS29JT,UK

Understanding the risk of airborne transmission can provide important information for designing safe healthcare environments with an appropriate level of environmental control for mitigating risks.The most common approach for assessing risk is to use the Wells–Riley equation to relate infectious cases to human and environmental parameters.While it is a simple model that can yield valuable information,the model used as in its original pres-entation has a number of limitations.This paper reviews recent developments addressing some of the limitations including coupling with epidemic models to evaluate the wider impact of control measures on disease progression,linking with zonal ventilation or compu-tational?uid dynamics simulations to deal with imperfect mixing in real environments and recent work on dose–response modelling to simulate the interaction between pathogens and the host.A stochastic version of the Wells–Riley model is presented that allows consider-ation of the effects of small populations relevant in healthcare settings and it is demonstrated how this can be linked to a simple zonal ventilation model to simulate the in?uence of proxi-mity to an infector.The results show how neglecting the stochastic effects present in a real situation could underestimate the risk by15per cent or more and that the number and rate of new infections between connected spaces is strongly dependent on the air?ow.Results also indicate the potential danger of using fully mixed models for future risk assessments, with quanta values derived from such cases less than half the actual source value.

Keywords:airborne infection;ventilation;Wells–Riley;stochastic;hospital

1.INTRODUCTION

Airborne transmission of infectious diseases is a subject of increasing interest driven by a wide range of factors including:greater understanding of the role played by indoor air and ventilation provision in the dispersal and transport mechanisms of a wide range of patho-gens;changing expectations of hospital patients, particularly in developed countries;and the emergence of new or drug-resistant disease strains with the poten-tial to spread on a global scale.Tuberculosis(TB)is an archetypal example of a disease that is transmitted by a true airborne route;primary infection occurs when dro-plet nuclei containing Mycobacterium tuberculosis bacilli are inhaled.These tiny particles(typically ,5m m in diameter)can remain suspended in the air for long periods of time with local air?ow pathways inside a building determining their fate.TB is a particu-lar concern as it is once again a worldwide health problem,compounded by the increased susceptibility to M.tuberculosis in HIV/AIDS patients,ease of world travel and the increased prevalence of multidrug-resistant tuberculosis(MDR-TB).Specialist ventilation and isolation facilities are recommended to control nosocomial(hospital)spread(Siegel et al.2007)and those on the front line advocate secondary environ-mental control measures such as ultraviolet germicidal irradiation to further minimize risk(Nardell et al. 1991;Escombe et al.2009).Although excluded from the medical de?nition of airborne infection,the trans-mission of disease by pathogen-contaminated droplets also involves transport through the air.The emergence of severe acute respiratory syndrome(SARS)in2002–2003caused a global health scare(Riley et al.2003), with the causative agent,a highly infectious corona-virus(Lipsitch et al.2003),thought to be primarily spread through localized contact with contaminated droplets.However,there is evidence that individuals were apparently infected without suf?ciently close con-tact with a known case(Scales et al.2003),and retrospective studies of building air?ow patterns suggested that airborne dispersal may play a signi?cant role(Li et al.2005).In recent months,the potential for a global in?uenza pandemic has created similar anxieties for those tasked with controlling wide-scale disease spread.Again the infection is linked to droplet transmission and the time scale for production of a vac-cine and limitations of drug treatment mean that

*Author for correspondence(c.j.noakes@https://www.wendangku.net/doc/286984316.html,).

One contribution of10to a Theme Supplement‘Airborne transmission of disease in hospitals’.J.R.Soc.Interface(2009)6,S791–S800 doi:10.1098/rsif.2009.0305.focus

Published online7October2009

Received20July2009

Accepted7September2009S791This journal is q2009The Royal Society

physical and procedural control strategies are the pri-mary defence against widespread transmission(Morse et al.2006).

Although many nosocomial infections are primarily associated with direct person-to-person contact,there is considerable evidence that aerial dissemination of pathogens may play an important role in many hospital-acquired infections.In recent years airborne transmission has been implicated in nosocomial outbreaks of Staphylococcus aureus(Farrington et al. 1990;Mertens1996)and Acinetobacter spp.(Allen& Green1987;Kumari et al.1998)as well as many viral outbreaks.The high secondary attack rates seen in nor-ovirus outbreaks have also been attributed to the dispersion droplets,released when patients vomit,that rapidly evaporate to form airborne droplet nuclei and are distributed by air currents around hospitals.With hospital design and operation in the developed world now driven by infection control targets and increasingly the energy use agenda,better understanding of the relationships between the design of the physical environ-ment and the risk of infection is becoming increasingly essential in establishing robust guidance for those charged with developing and managing healthcare facilities.This paper reviews the application of the Wells–Riley model for relating the risk of airborne infec-tion to parameters in the indoor environment and the developments applied to address some of the limitations in the original model.A stochastic formulation is pre-sented which is coupled with a simple zonal ventilation model to demonstrate the role of air?ow and population size on the risk of infection and the implications for design,risk assessment and future research.

2.MODELLING AIRBORNE INFECTION Transmission of infection is a complex process at the best of times with the risk of disease determined by numerous factors that have considerable and uncer-tain variability including:the characteristics of the pathogen concerned,the infectiousness of the host, the media in which it is passed from source to new host and the immune response of the new host.Trans-mission through airborne routes complicates this further by adding the in?uence of building air?ows to the process.Despite this,researchers in epidemiology have developed a range of approaches for modelling disease dynamics from the classic models such as Susceptible-Infector-Susceptible(SIS)and Susceptible-Infector-Removed(SIR)models,which make use of average rate coef?cients to describe progression of a disease in a population(Bailey1957)to more recent studies based on dose–response data(Jones et al. 2009)or that incorporate the pathogen–host biological interaction(Chen et al.2009).Much of the previous research quantifying airborne infection rates in con?ned spaces has stemmed from the work of Wells(1955)and Riley et al.(1978),using the analytical expression known as the Wells–Riley equation.This relates the number of infective(I)and susceptible(S)people in a space,the room ventilation rate(Q,m3s21)and the quantity of infectious material in the air to predict the number of new cases infected,N C,over a period of time t(s):

N C?S1àeàI qpt=Q

:e2:1THere p(m3s21)is the pulmonary ventilation rate of sus-ceptible individuals,while q represents a unit of infection termed as‘quantum’,introduced by Wells (1955),to express the response of susceptible individ-uals to inhaling infectious droplet nuclei.He postulated that not all inhaled droplet nuclei will result in infection and de?ned a quantum of infection as the number of infectious droplet nuclei required to infect121/e susceptible people.The term quantum or quanta of infection is widely used in evaluating airborne infections and is usually interpreted as a measure that effectively indicates both the quantity and virulence of infectious material present in the air.

Numerous researchers have carried out risk-analysis studies based on this model including the evaluation of personal protective equipment(Gammaitoni& Nucci1997),tuberculosis risk in buildings(Nardell et al.1991)and the dispersion of Bacillus anthracis from envelopes(Fennelly et al.2004).The study con-ducted by Gammaitoni&Nucci(1997)also showed a fundamental formulation of the Wells–Riley equation that enables transient ventilation effects to be included. An earlier study reviewing Wells–Riley type models (Beggs et al.2003)highlighted that although the models give useful indications of expected transmission in a wide range of circumstances,their simple nature results in several limitations described here.

2.1.Disease dynamics

The original Wells–Riley formulation is con?ned to only predicting new cases of a disease,an assumption that is valid where the incubation period(or time for a new case to become infective)is longer than the time scale of the model.With the model most commonly used to evaluate TB transmission,this is generally justi?ed as the incubation is typically weeks or even years,and (with the exception of long-term con?nement such as prisons),occupants are generally not in contact longer than the incubation period.The assumption is also valid for short incubation period diseases if the model is applied over very short time scales,such as trans-mission of in?uenza on an aircraft as considered by Rudnick&Milton(2003).However,in the case of trans-mission of diseases such as in?uenza,SARS or norovirus in hospitals,which may have an airborne component to the transmission,the time scale of contact is comparable to the incubation period and therefore the dynamics of the disease must be considered.It is straightforward to extend the model to include the long-term dynamics of an infection by coupling with classic epidemic models as described in Noakes et al.(2006a).Such an approach enables both the disease and environmental parameters to be explored,allowing the combined role of nursing behaviour with controls such as ventilation,personal protective equipment or vaccination(Chen&Liao 2008)to be assessed through a single model.Interestingly, the original paper?rst describing the Wells–Riley

S792Models to assess airborne infection risk C.J.Noakes and P.A.Sleigh J.R.Soc.Interface(2009)

equation(Riley et al.1978)applied it to a measles out-break in a school,a disease and setting that do not meet the above criteria.To accommodate this the authors applied the model over discrete time periods, using the cases and susceptibles at the end of each period as the initial conditions for the next period rather than coupling with an epidemic model.

2.2.Population size

One of the key limitations with the Wells–Riley model concerns the small size of populations in hospital environments and the role that chance effects play in determining infection risk.Equation(2.1)is based on the Poisson law of small chances,which assumes that in a small enough time period only one new infec-tion is likely.This is suitable for most airborne infections where it is easy to de?ne a time period that approximates to this criterion.However,although the Wells–Riley model is derived from this probabilistic approach,it is more commonly used in deterministic simulations,with equation(2.1)used to predict average infection risk in different scenarios.In particular, the model has been used successfully in studies to exam-ine both the impact of interventions on the progression of an infection,as well as retrospectively to?nd the average quanta production rate from outbreak data, particularly relating to TB transmission.Treating the model as one describing a deterministic process is only strictly suitable for large populations,and to under-stand the variability in risk for small numbers,such as hospital patients,it is necessary to apply the model in a stochastic simulation.

2.3.Proximity

The Wells–Riley model assumes that the air is well mixed leading to a uniform concentration of bioaerosols throughout the space.This is rarely true even in spaces with the best designed ventilation systems and therefore does not account for the in?uence of proximity between infective and susceptible people.In particular this is an issue when analysing the risk of infection in a space consisting of connected rooms,such as hospital wards.This can be partially addressed by using zonal ventilation or computational?uid dynamics(CFD) modelling techniques to simulate the air?ow and dis-persion of contaminants,revealing regions of good and poor mixing and areas of high contaminant con-centrations that would constitute a higher risk to occupants.Zonal or network ventilation models are well used in evaluating ventilation?ows in large multi-connected spaces such as whole buildings.While they are limited in that they are not capable of resolving local details of air?ows and are less well suited to large spaces such as atria(Mora et al.2003),they have been shown to give good prediction of bulk air movement and contaminant transport in a range of applications including natural ventilation(Asfour&Gadi2007) and particle dispersion(Hu et al.2007).Two of the most widely used models,COMIS and CONTAM, were developed by national laboratories in the USA and are used in both research and design applications (Chen2009).Zonal modelling has previously been applied to airborne infection risk,including simulation of ultraviolet disinfection(Noakes et al.2004a)showing good comparison to CFD models and studies by Ko et al.(2004)and Jones et al.(2009)considering TB transmission on an airliner.Ko et al.’s study used both a fully mixed model as well as approximating the spatial variation by dividing the airliner cabin into four zones with incomplete mixing between zones. Combining this with the Wells–Riley model and spatial distribution data from a real outbreak enabled them to show that compartmentalization of air?ow in cabins acts to limit transmission of any infection throughout the entire aircraft.Jones et al.(2009)also adopted a zonal approach,dividing the aircraft into34zones with the ventilation and interzonal?ows based on measured data with results indicating spatial trans-mission patterns dependent on the turbulent mixing between zones.CFD offers a strategy for modelling the detailed spatial distribution of pathogens in indoor environments.A number of recent studies have considered hospital applications(Chow&Yang2004; Noakes et al.2006b)or bioaerosol dispersal(Noakes et al.2004b),and the2003SARS outbreak generated a lot of interest using CFD to model the spread of con-tagion within and between buildings(Yu et al.2004;Li et al.2005).A recent paper(Qian et al.2009)has linked CFD simulations and the Wells–Riley model with results showing correlation between predicted and observed spatial infection risk.Despite the details available from CFD modelling,using the technique to simulate air?ow in large multi-connected buildings requires signi?cant computational resources that are unavailable or inappropriate in many cases.A recent review by Chen(2009)highlights a move towards the use of‘coarse grid’CFD and coupling CFD models to zonal ventilation models to provide higher levels of accuracy without excessive computational effort.

2.4.Infectious dose

Perhaps the biggest limitation with the Wells–Riley model is the representation of the infectious dose through the expression‘quantum’of infection. While this is a simple approach that is easily analogous to the concentration of a pathogen in the air,the single parameter cannot fully capture the complex interaction between infectors,pathogens and potential hosts that occurs in reality.As highlighted in Pujol et al.(2009), the Wells–Riley model is only appropriate for infections that can be modelled with an exponential dose–response where a single large dose can be considered to be the same as the equivalent in smaller doses over a longer time period.As such the model cannot incor-porate the immune system response that may act to control pathogens arriving at low doses over a long time period and is likely to be inappropriate for estimat-ing risk at low doses(Haas1983).Nicas&Hubbard (2002)also recognize this limitation and go on to suggest that the Wells–Riley model is only strictly valid where infection is initiated by a single micro-organism and the quanta represents the risk of this being inhaled and initiating infection.The model has

Models to assess airborne infection risk C.J.Noakes and P.A.Sleigh S793 J.R.Soc.Interface(2009)

been most widely applied to TB,which is believed to satisfy these criteria(Escombe et al.2007);however, it may be less appropriate for many other infections, especially where the infectious dose is low(Nicas& Hubbard2002).Recent research is starting to develop strategies to address these weaknesses through the application of disease-speci?c characteristics and dose–response data,much of which has developed through risk assessment of pathogens in water and wastewater(Haas1983;Mara et al.2007).Studies focusing on airborne transmission include Armstrong& Haas(2007a,b)who outline a framework for using quantitative microbial risk assessment(QMRA)in modelling the risk of legionnaire’s disease,using dose–response data from animal studies.Bartrand et al. (2008)consider a similar approach in the transmission of B.anthracis,again through?tting distribution models to published non-human dose–response data, while Jones et al.’s(2009)study also uses a QMRA approach in evaluating M.tuberculosis transmission. Chen et al.(2009)adopt a slightly different approach, using a Wells–Riley framework to describe global parameters,but linking both viral kinetics and the characteristics of exhaled bioaerosols to incorporate the disease characteristics in the transmission of in?u-enza.The most recent studies in this area(Huang& Haas2009;Pujol et al.2009)are building on these dose–response model developments to consider the risk over time from single or multiple doses,enabling the immune response seen in reality to be incorporated into analyses.Although the primary interest in this paper is on the environmental parameters rather than the disease characteristics,these recent developments clearly offer a valuable strategy for understanding the role of pathogen–human interaction in disease trans-mission and are likely to play a key role in future model developments.

3.STOCHASTIC ZONAL MODEL

By considering equation(2.1)an infection rate l can be written as

l?Iqp

Q

:e3:1T

A stochastic formulation of the Wells–Riley equation is based on the probability that there are S uninfected susceptibles at time t,p S(t)?Pr(S susceptibles at time t).In a small time interval,d t,such that the probability of more than one infection is negligible, two outcomes are possible:one new infection with probability l d tS or no new infection with probability 12l d tS.Therefore,the process can be expressed as p Settd tT?p SetT1àl d tS

eT

tp St1etTl d t St1

eT:e3:2TAs d t tends to zero,this yields the differential equation

d p SetTd t ?àl Sp SetTtleSt1Tp St1etT:e3:3T

This can be solved using a numerical approach in

which the process is considered to consist of a series of

infection events where the susceptible population

decreases by one in each case.As shown by Renshaw

(1991),for a population of S susceptibles and a disease

that can be approximated by an exponential dose–

response,the time T to the next event is an exponentially

distributed random variable with

PreT!tT?expeàl StT:e3:4T

This can be used to simulate the time to the next event,t,

using a random number0Y1by the equation

t?à

lneYT

el ST

:e3:5T

With l de?ned by equation(3.1),the result in equation

(3.5)can be easily applied to derive a series of inter-

event times corresponding to the new cases of infection

among the susceptible population in a ventilated

indoor environment.

To account for the proximity of an infector to suscep-

tibles and the incomplete mixing in interconnected

ward spaces,the above model is applied within a

zonal ventilation model.Here the air within each zone

is treated as uniformly mixed;however,the mixing

between the zones is limited.The infectious quanta

is treated as a deterministic variable leading to a con-

centration distribution throughout the ward space.

A simpli?ed approach is applied which represents a

realistic spatial arrangement of a ward but uses?xed

interzonal ventilation rates to model transfer into and

out of zones rather than environment-speci?c pressure

coef?cients.It must be highlighted that this approach

is used only to demonstrate the behaviour of the

stochastic risk model in a multi-zone space and

the results are a considerable simpli?cation of reality.

However,it is straightforward to apply the approach

described here using any ventilation network model or

CFD simulation to assess the spatial distribution of

infectious material in a real situation.

For the general case shown schematically in?gure1,

the concentration of infectious material in the i th zone

C i can be approximated by considering the generation,

ventilation removal and interzonal transfers for each

case to give

V i

d C i

d t

?q i I iàQ oi C ià

X

k

b ik C it

X

k

b ki C k:e3:6T

Here,the term q i I i represents the generation rate in the

zone,Q oi is the extract ventilation rate in zone i and b ik

and b ki represent the volume?ow rate of air to and from

adjacent zones k,respectively.These interzonal?ow

rate terms consist of two components:a global mixing

rate b o which is a constant value in both directions

across all zonal boundaries in the model plus an

additional component b Qik which expresses the net

?ow across a boundary owing to a ventilation imbalance

between the two zones(Brouns&Waters1991).This

component is speci?c to the ventilation system and is

de?ned for each boundary in the model to give the

S794Models to assess airborne infection risk C.J.Noakes and P.A.Sleigh J.R.Soc.Interface(2009)

total interzonal ?ow rate as

b ik ?b o tb Qik :

e3:7T

Under steady-state conditions,equation (3.6)is equal to zero for each zone and yields a set of equations that can be represented in matrix form and solved through a Gaussian elimination technique.This is shown partially below for the simple schematic case in ?gure 1:àQ o 1tb 12eT

b 21b 12àQ o 2tb 21tb 23eT0b 23..

(2)

66664

0áááb 32áááàQ o 3tb 32tb 3k eTááá......377775C 1

C 2C 3...266664377775?q 1I 1q 2I 2q 3I 3 (2666643)

77775

:e3:8TThe infection risk model is made zone dependent by

replacing the term qI /Q with the zone concentration C i from the solution of equation (3.8),giving

l i ?C i p :e3:9T

As the new infection may now occur in any one of the occupied zones within the model,it is necessary to examine the relative probability of infection in each to determine in which zone each infection event occurs.At each time step,the probability that the next infection event will be in zone i is given by

Pr einfection in zone i T?

l i S i

R ek T

;where

R ek T?

X 9k ?1

l k S k ;e3:10T

with the inter-event time now given by

t ?à

ln eY T

R ek T

:e3:11T

The numerical simulation of this process again follows the methodology described by Renshaw (1991)(i)Calculation of l i S i /R (k )for each zone at the

current time step.

(ii)Generation of a ?rst random number 0 Y 1

to ?nd the inter-event time.

(iii)Generation of a second random number 0 X 1

to establish which zone is infected based on infection in zone 1if 0 X l 1S 1/R (k ),zone 2if l 1S 1/R (k ) X l 2S 2/R (k ),etc.

(iv)Change S i to S i 21in infected zone i .

The model was implemented using E XCEL and VBA (Microsoft)incorporating a Monte Carlo approach to enable each model to run up to 100times to calculate mean behaviour and the s.d.As the equations are de?ned in terms of inter-event times,which are differ-ent in every simulation owing to the random number in the event time de?nition,it was necessary to map each result onto a regular time scale in order to be able to ?nd average data across more than one simu-lation.The simulations were mapped onto a 170h time period divided into hourly steps,then plotted every 3h to enable the data to be seen clearly.

4.RESULTS

The models described above were used to investigate the in?uence of population and air?ows on the risk of infection through a parametric study approach.The model was based on a hypothetical hospital ward layout as shown in ?gure 2comprising three iden-tical six-bedded bays that open out onto a common corridor.To investigate a range of possible ventilation scenarios,each bay is divided into two equal zones

Figure 1.Schematic representation of simple zonal model for three adjacent zones.Solid black arrows indicate ventilation extract,

solid grey arrows indicate interzonal ?ows,dashed black arrows indicate infection source within the

zone.

connecting corridor

Figure 2.Hypothetical ward layout used in the study show-ing possible ventilation supply /extract (black arrows)and interzonal mixing (grey arrows).

Models to assess airborne infection risk

C.J.Noakes and P.A.Sleigh

S795

J.R.Soc.Interface (2009)

(each containing three occupants)and the corridor split into three equal zones corresponding to the adja-cent ward.The model assumes that ventilation air can be supplied and/or extracted from each zone and there is some degree of mixing between adjacent zones that is in?uenced by the ventilation regime as described above.All cases simulated a ward occupancy of18patients(six per bay)of which one located in zone1a was assumed to be infectious.All patients were equally susceptible and breathed the ward air at a constant rate of0.01m3min21(10l min21).Six different ventilation regimes were investigated as detailed in table1to explore the effect of directional air?ow.Although these speci?ed different supply and extract volumes to the various zones,the total venti-lation rate over the whole ward was27m3min21in all cases,equivalent to an average air change rate of 3AC h21.

The interzone mixing parameter b o was constant across all zone boundaries with a value between9 and27m3min21depending on the simulation.The ventilation-dependent component of the interzone mixing b Qik was de?ned to simulate directional air?ow induced by a ventilation regime.

The?nal parameter is the value of quanta gener-ation,which is particularly dif?cult to de?ne for most infections.Previous researchers have estimated the values from outbreak data using equation(2.1)and the actual number of new cases.Most of the values given in the literature relate to TB outbreaks and the data collated in Beggs et al.(2003)indicate that for most pulmonary TB cases,a generation rate of between 1.25and60quanta h21can be assumed.Higher values of hundreds or even thousands of quanta per hour are associated with medical procedures,such as broncho-scopy or abscess irrigation where the generation rate of infectious aerosols is increased.Riley et al.(1978) calculated a value of570quanta h21for a school measles outbreak,while Rudnick&Milton(2003) estimated quanta production rates for rhinovirus as 1–10quanta h21and in?uenza as15–128quanta h21. For the purposes of this study,a quanta production rate of0.5quanta min21(30quanta h21)is used.As the aim of this study is to examine the relative impact of the occupant and air?ow parameters on the risk of infection,the actual quanta production rate is not criti-cal.However,we will return to the de?nition and calculation of quanta in§5,as the model results raise some important questions about estimating quanta, and hence risk,from equation(2.1).

4.1.Stochastic effects

Prior to considering the effect of ventilation par-ameters,?gures3and4compare the zonal and stochastic behaviour with a fully mixed deterministic simulation using equation(2.1)for a single infector generating30quanta h21.Figure3compares both approaches for the fully mixed case,presented in terms of a mean value with error bars indicating1s.d. In the stochastic model this is based on the data from 100simulations,while in the deterministic solution, mean and s.d.are based on the Poisson assumption used in the derivation of equation(2.1).As such,the number of cases is taken as the Poisson mean and s.d. as the square root of the mean.As expected,the mean values from both the models are almost identical and both show considerable variability in the mean result. However,the expected variance differs between approaches,with a similar range predicted after short time duration,but a greater deviation from the mean indicated by the deterministic solution over a longer time period.This difference is probably apparent because basing the variability on the mean value from the deterministic solution inherently assumes variabil-ity in all parameters of the model,while the variation in the stochastic solution is due solely to the small population.

In?gure4,the deterministic fully mixed mean is compared with the zonal model results for ventilation regime A and the infector located in zone1a.In this case all zones have an equal supply and extract volume?ow rate;therefore,the interzonal mixing is solely due to the value of b o,with no additional transfer through ventilation imbalance(b Qik?0).Results pre-sented show the effect of air mixing on the total number of new cases across the whole ward.With a value of b o?9m3s21,the overall infection rate is much slower than the fully mixed model,with less than two-thirds of the predicted total number of cases after the170h time period.Increasing the mixing to b o?27m3s21increases the rate at which the infection spreads with now around85per cent of the fully mixed model.The?gure again shows the considerable variabil-ity in a small population with considerable overlap between the range of results for the two mixing

Table1.Volume?ow rate in and out of each zone for the six ventilation regimes.

regime zones1a,2a,3a zones1b,2b,3b zones c1,c2,c3

supply

(m3min21)

extract

(m3min21)

supply

(m3min21)

extract

(m3min21)

supply

(m3min21)

extract

(m3min21)

A333333 B900009 C090090 D660033 E600633 F066033 S796Models to assess airborne infection risk C.J.Noakes and P.A.Sleigh

J.R.Soc.Interface(2009)

parameters and a deviation of approximately +15per cent from the mean value in either stochastic simulation.

4.2.Effect of air?ow paths

Although the results in ?gure 4provide some initial insight into the potential in?uence of ventilation,the air mixing between the rooms is not in?uenced by the ventilation regime in this case.To understand the potential impact of this,simulations are run for all six ventilation regimes in table 1using a ?xed value of b o ?9m 3s 21.In all cases,Monte Carlo simulations are performed with 100simulation runs to yield mean infection rates for each of the three ward bays.The results from these simulations are presented in ?gure 5in terms of infection risk,where a risk of one is equivalent to all six patients in a bay being infected.The results in ?gure 5demonstrate both the in?u-ence of proximity and ventilation ?ows on the risk

of infection for patients on the ward over time.As expected,the risk of infection in bay 1(?gure 5a ),where the infector is located,is much higher than the other two bays,with the ventilation regime having little impact on the risk.Although ventilation regime C suggests a slightly lower infection rate compared with the other ?ve regimes,the risk is still over 90per cent over the 170h period.The results for the other two bays (?gure 5b ,c ),however,clearly demonstrate the potential impact of the ventilation system on the risk of airborne pathogen transfer throughout the space.In both cases,even with the sto-chastic variability in the data,the risk of infection is highest with ventilation regime D and lowest with regime C,with the risk around 50per cent lower in bay 2and 60per cent lower in bay 3.

5.DISCUSSION

The results presented above give some initial insight into both the variability of infection risk likely to

be

24681012141618n u m b e r o f c a s e s time (h)

Figure https://www.wendangku.net/doc/286984316.html,parison of variability from mean results in stochastic and deterministic fully mixed models.Error bars show 1s.d.from the mean,with grey capped error bars for the stochastic model and black uncapped error bars for the deterministic

model.

24681012141618n u m b e r o f c a s e s time (h)

Figure 4.Effect of air mixing on the total rate of infection.Error bars show 1s.d.from the mean value.Solid line denotes b o ?27m 3min 21;open triangle denotes b o ?9m 3min 21;?lled diamond denotes fully

mixed.

(a )(b )(c )r i s k o f i n f e c t i o

n

r i s k o f i n f e c t i o

n r i s k o f i n f e c t i o n

time (h)

Figure 5.Effect of ventilation regime on the risk of infection over a 170h period.Mean data obtained from 100simulation runs.(a )Infections in bay 1.(b )Infections in bay 2.(c )Infec-tions in bay 3.Filled diamonds,case A;open squares,case B;?lled triangles,case C;crosses,case D;open triangles,case E;open diamonds,case F.

Models to assess airborne infection risk

C.J.Noakes and P.A.Sleigh S797

J.R.Soc.Interface (2009)

present in real situations as well as the role that venti-lation?ows may play in the transmission of infection.

The results in?gures3and4clearly show that considering the stochastic variation produces a con-siderably wider range of predicted cases than the mean result typically derived from deterministic simu-lations.The model presented here indicates that the actual number of new infections could deviate from the mean by up to two cases owing to chance effects in a small population alone.As the results in?gure3 indicate,if there is uncertainty in other parameters, this could result in an even wider deviation.While the Wells–Riley model is a very straightforward approach for carrying out assessments as part of outbreak plan-ning,the deterministic mean has the potential to signi?cantly underestimate the bed numbers,staf?ng and resources needed to respond to an outbreak.As such some level of stochastic variability should be taken into account when using Wells–Riley type models in this way.

Hospital ventilation is typically designed on a mixing ventilation approach with little consideration beyond provision of adequate comfort except in certain applications such as isolation rooms,units for immuno-suppressed patients or operating theatres.Although the zonal model presented here is a very simple rep-resentation of ventilation?ows and is limited as a model of a real situation,the results do give some quali-tative indication of the importance of air?ow paths between zones in the transmission of infection.Many of the results are intuitive as can be seen by presenting the worst(D)and best(C)cases schematically in ?gure6.In case C the air pathways are from the corri-dor to the ward,reducing the risk of airborne pathogens generated within a particular bay being transferred to other bays by extracting from the source location.How-ever,in case D(and also cases A and E),the ventilation provides little or no additional movement of potential pathogens within the space.Although this does not actively promote the transfer between spaces,at the same time it does nothing to restrict it with little direc-tional?ow to limit transfer into other areas.These ?ndings suggest that some approaches could be inadver-tently contributing to the spread of infection and that careful design of a system could potentially provide greater protection for patients within a hospital ward.

The results presented in?gure5a suggest that case C also has some advantage in reducing within-bay transmission;however,this result should be treated with a good deal of caution.The results presented are the mean results from100stochastic simulations.The variability in the data plus the uncertainty over the exact location of the infector in the ward implies that,in reality,it is dif?cult to say from this model how the ventilation system impacts on the risk within a single bay.To understand the level of risk in this case more detailed simulations of the air?ow,such as CFD analysis,are essential to show how the location of ventilation supply and extract vents in?uences the risk of cross-infection between patients(Noakes et al. 2006b).

Apart from giving some insight into the role of the ventilation system,the model applied above raises some important issues relating to the assessment of risk in indoor environments and use of quanta values in such activities.Regardless of the ventilation regime and layout,these results show a clear dependence of risk on the proximity to the infector.As shown by ?gure5,with the values used in this hypothetical study patients in the same space as the infector have over a90per cent risk of infection over the170h period,while those two bays away(bay3)have less than a35per cent risk over the same period.However, most quanta values quoted in the literature are calcu-lated from outbreak data and do not consider the in?uence of proximity.The assumed value of 30quanta h21with ventilation case A in the zonal sto-chastic model resulted in a mean number of infections across the whole ward of10.2in the170h period.

Quanta values presented in the literature take the total number of infections over a period of time, assume complete mixing and manipulate equation (2.1)to?nd the value for quanta production.In this case,using10.2new cases,17susceptibles in a fully mixed space with a total ventilation rate of 27m3min21over170h,this yields a quanta production rate of14.5quanta h21,less than half the actual value. This suggests that using a fully mixed model to deter-mine quanta production rates from outbreak data may signi?cantly underestimate the quanta values in environments such as multi-zoned hospital wards or of?ce buildings where the air will be far from fully mixed.In addition,using such values derived from out-breaks to estimate risk and design control procedures may signi?cantly underestimate the actual risk,par-ticularly for susceptible people in closer proximity to

case D case C

Figure6.Schematic of ventilation?ows in regimes D and C.Location of infector indicated by star.Black arrows indicate

ventilation?ow;grey arrows indicate interzonal mixing?ows.

S798Models to assess airborne infection risk C.J.Noakes and P.A.Sleigh

J.R.Soc.Interface(2009)

the index case.Although shown here from a simple rep-resentation of the ventilation,the results concur with the?ndings of Qian et al.(2009)who showed differ-ences between quanta values determined from mixed and spatially varying CFD models.

6.CONCLUSIONS

The Wells–Riley model has been used to examine air-borne infectious disease transmission since the1970s and remains a simple and valuable approach for under-standing the role of various parameters to inform research,design and risk assessment.Linking the model with ventilation?ows is a straightforward and practical option for those involved in the design and risk assessment of healthcare buildings.Provided users appreciate the limitations of the Wells–Riley model and their ventilation model,the approach enables a much greater understanding of the possible spatial transmission of infection and allows design and operational control strategies to be explored.The importance of stochastic effects,especially in small populations,should not be underestimated and users should seek to incorporate this into any model to evaluate the potential range of risk.

Coupling the model with disease dynamics,vacci-nation and environmental control strategies have also been tackled in previous studies and shown to give greater insight into the role of environmental and management strategies,particularly for the trans-mission of short incubation period diseases.The greatest uncertainty in the Wells–Riley model remains the disease parameters,with the concept of quanta suit-able for parametric studies but severely limited in real risk assessments owing to the necessity to derive expected values from prior outbreaks.However,recent developments are showing considerable promise for establishing new methodologies for evaluating airborne disease transmission based on the dose–response characteristics of real pathogens.While this is currently limited by available time–dose data relevant to human subjects(Pujol et al.2009),the right collaboration between those conducting experimental dosing studies and the infection risk modelling community could sig-ni?cantly enhance knowledge of disease characteristics and the pathogen–host interaction.Linking such knowledge to models incorporating environmental par-ameters offers a very effective framework for future assessment of airborne disease transmission in indoor environments.

The authors would like to acknowledge the support of the Department of Health,Estates and Facilities Division Research and Development Fund in funding this study. REFERENCES

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Tesla Model S底盘全透视..

水平对置、后置后驱、低重心、前双横臂后多连杆、全铝合金车架、5门5座，你以为笔者说的是保时捷新车型吗？那笔者再补充多几个关键词好了，后置的水平对置双电刷电动机、0油耗、藏在地板下的笔记本电池组，同时拥有这些标签的，便是Tesla第二款车型Model S。Model S是五门五座纯电动豪华轿车，布局设计及车身体积与保时捷Panamera相当，并且是目前电动车续航里程的纪录保持者（480公里）。虽然现在纯电动在我国远未至于普及，但是在香港地区却是已经有Tesla的展厅，在该展厅内更是摆放了一台没有车身和内饰，只有整个底盘部分的Model S供人直观了解Model S的技术核心。 图：Tesla Model S。

图：拆除车壳之后，Model S的骨架一目了然。

图：这套是Model S的个性化定制系统，可以让买家选择自己喜爱的车身颜色、内饰配色和轮圈款式，然后预览一下效果。可以看到Model S共分为普通版、Sign at ure版和Performance版，后面两个型号标配的是中间的21寸轮圈，而普通版则是两边的19寸款式。Signature版是限量型号，在美国已全部售罄，香港也只有少量配额。 图：笔者也尝试一下拼出自己心目中的Model S，碳纤维饰条当然是最爱啦。

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特斯拉电动汽车动力电池管理系统解析(苍松书屋)

特斯拉电动汽车动力电池管理系统解析 1. Tesla目前推出了两款电动汽车，Roadster和Model S，目前我收集到的Roadster 的资料较多，因此本回答重点分析的是Roadster的电池管理系统。 2. 电池管理系统（Battery Management System, BMS）的主要任务是保证电池组工作在安全区间内，提供车辆控制所需的必需信息，在出现异常时及时响应处理，并根据环境温度、电池状态及车辆需求等决定电池的充放电功率等。BMS的主要功能有电池参数监测、电池状态估计、在线故障诊断、充电控制、自动均衡、热管理等。我的主要研究方向是电池的热管理系统，因此本回答分析的是电池热管理系统 (Battery Thermal Management System, BTMS). 1. 热管理系统的重要性 电池的热相关问题是决定其使用性能、安全性、寿命及使用成本的关键因素。首先，锂离子电池的温度水平直接影响其使用中的能量与功率性能。温度较低时，电池的可用容量将迅速发生衰减，在过低温度下（如低于0°C）对电池进行充电，则可能引发瞬间的电压过充现象，造成内部析锂并进而引发短路。其次，锂离子电池的热相关问题直接影响电池的安全性。生产制造环节的缺陷或使用过程中的不当操作等可能造成电池局部过热，并进而引起连锁放热反应，最终造成冒烟、起火甚至爆炸等严重的热失控事件，威胁到车辆驾乘人员的生命安全。另外，锂离子电池的工作或存放温度影响其使用寿命。电池的适宜温度约在10~30°C之间，过高或过低的温度都将引起电池寿命的较快衰减。动力电池的大型化使得其表面积与体积之比相对减小，电池内部热量不易散出，更可能出现内部温度不均、局部温升过高等问题，从而进一步加速电池衰减，缩短电池寿命，增加用户的总拥有成本。 电池热管理系统是应对电池的热相关问题，保证动力电池使用性能、安全性和寿命的关键技术之一。热管理系统的主要功能包括：1）在电池温度较高时进行有效散热，防止产生热失控事故；2）在电池温度较低时进行预热，提升电池温度，确保低温下的充电、放电性能和安全性；3）减小电池组内的温度差异，抑制局部热区的形成，防止高温位置处电池过快衰减，降低电池组整体寿命。 2. Tesla Roadster的电池热管理系统 Tesla Motors公司的Roadster纯电动汽车采用了液冷式电池热管理系统。车载电池组由6831节18650型锂离子电池组成，其中每69节并联为一组（brick），再将9组串联为一层（sheet），最后串联堆叠11层构成。电池热管理系统的冷却液为50%水与50%乙二醇混合物。

特斯拉整体介绍

Tesla Model S 特斯拉Model S是一款纯电动车型，外观造型方面，该车定位一款四门Coupe车型，动感的车身线条使人过目不忘。此外在前脸造型方面，该车也采用了自己的设计语言。另值得一提的是，特斯拉Model S的镀铬门把手在触摸之后可以自动弹出，充满科技感的设计从拉开车门时便开始体现。该车在2011年年中正式进入量产阶段，预计在2012年年内将有5000台量产车投放市场。 目录 1概述 2售价 3内饰 4动力 5车型 6技术规格 7性能表现 8荣誉 9对比测试 10车型参数 1概述

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由于马丁·艾伯哈德毫无这方面的制造经验，最终找到AC Propulsion公司。当时，对AC Propulsion公司电动汽车技术产生兴趣的还有艾龙·穆思科（Elon Musk）。在AC Propulsion公司CEO汤姆·盖奇（Tom Gage）的引见下，穆思科认识了艾伯哈德的团队。2004年2月会面之后，穆思科向TESLA投资630万美元，但条件是出任公司董事长、拥有所有事务的最终决定权，而艾伯哈德作为创始人任TESLA的CEO。 在有了技术方案、启动资金后，TESLA开始开发高端电动汽车，他们选择英国莲花汽车的Elise作为开发的基础。没有别的原因，只是因为莲花是唯一一家把TESLA放在眼里的跑车生产商。

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FIG3 如专利图所示，Model S的电池组位于车辆的底盘，与轮距同宽，长度略短于轴距。电池组的实际物理尺寸是：长2.7m，宽1.5m，厚度为0.1 m至0.1 8m。其中0.1 8m较厚的部分是由于2个电池模块叠加而成。这个物理尺寸指的是电池组整体的大小，包括上下、左右、前后的包裹面板。这个电池组的结构是一个通用设计，除了18650电池外，其他符合条件的电池也可以安装。此外，电池组采用密封设计，与空气隔绝，大部分用料为铝或铝合金。可以说，电池不仅是一个能源中心，同时也是Model S底盘的一部分，其坚固的外壳能对车辆起到很好的支撑作用。 由于与轮距等宽，电池组的两侧分别与车辆两侧的车门槛板对接，用螺丝固定。电池组的横断面低于车门槛板。从正面看，相当于车门槛板"挂着。电池组。其连接部分如下图所示。 FIG, 4

特斯拉Model S电动汽车性能介绍

特斯拉Model S 特斯拉Model S并非小尺寸、动力不足的短程汽车——这是某些人对电动车的预期。作为特斯拉三款电动车中体积最大的车型，根据美国环保署认证，这款快捷、迷人的运动型轿车一次充电能够行驶265英里(426公里)，不过特斯拉声称可以达到300英里。不管哪种情况，这肯定是电动车行业的新高。Model S Performance版本的入门级价格为94,900美元，我测试的版本价格为101,600美元(按照美国联邦税收抵免，可以在此基础上扣减7,500美元)。 在一次开放驾驶上，这款特斯拉汽车硕大的85千瓦时电池的确可以至少行驶426公里。电流来自于车底的电池组，里面有大约7,000颗松下锂电池，重量约为590公斤(1,300磅). 试驾的第二天是前往威斯康辛州，在行驶了320公里后电几乎用光，不过其中包括了在芝加哥的一场交通拥堵中无奈爬行的两个小时。这天的测试充满野心，更多是针对性能而非行驶里程，包括这款特斯拉汽车迅速地用4.4秒时间从0加速到时速97公里(0至60英里每小时)，此外测试达到的最高时速为210公里。 我有没有提到，在0到时速100英里的加速时间方面，这款310千瓦(416马力)的特斯拉汽车将击败威力巨大、使用汽油的413千瓦(554马力)宝马M5？部分原因在于这款特斯拉汽车的同步交流电发动机能够即时提供600牛·米(443英尺磅)的扭矩。像电灯开关一样轻点特斯拉的油门，最大的扭矩已经准备就绪，一分钟内能够实现从0到5,100转。后悬挂、液冷式发动机可以保持1.6万转每分钟，通过一个单速变速箱将动力传导至后轮。 它就像一头冷酷的猛兽，在出奇安静之中让内燃机这个猎物消失于无形——安静到何种程度呢？来自轮胎和风阻的声音比在其他大部分豪华车中感受到的更加明显。安装于车底的电池让特斯拉获得与很多超级车相当的重心，这非常有利于稳定操控。Model S经过弯道的时候也能很好地保持贴地感。 尽管这款特斯拉汽车看起来并不笨重，但其重量达到2,108公斤；随着速度和重力的提升，这些多余的重量表露无遗。加大油门后，沉重的尾部会产生震动。在操控手感的愉悦性方面，特斯拉无法与宝马相提并论，甚至连马自达都赶不上。 美妙的试驾体验在你进入车内之前就开始了，你靠近汽车时，可伸缩的车门把手自动弹出。接着看到的是特斯拉标志性的驾驶室特色内容，一个43厘米(17英寸)电容触摸屏，看起来就像一对相互配合的iPad. 在其用铝合金加强的底盘和车身内，Model S可以容纳5人。一个可爱但是奇怪的按钮可以在车门位置增加脸朝车后的儿童座椅，从而实现最多承载7人。将第二排座椅向下折，可以扩展后座载货空间，可用于家得宝(Home Depot)采购之旅。由于引擎盖下面没有发动机，这些空间可以作为有用的前置行李箱，特斯拉将其称为“前备箱”(“frunk”)，就像保时捷911一样。

特斯拉纯电动车

目录 一、特斯拉简介 (3) 二、特斯拉纯电动车主要功能特点 (3) （一）Model S 主要特点 (3) （二）Model X 主要特点 (9) （三）Model 3 主要特点 (12) 三、特斯拉的电池技术 (13) （一）特斯拉动力电池简介 (13) （二）85kwh电池板的拆解分析 (14) （三）单体电池的能量密度 (20) （四）电量的衰减性能 (22) （五）电池检测实验室：从源头保证锂电池单体一致性 (24) （六）动力电池系统PACK技术 (25) （七）电池管理系统（BMS） (27) 四、特斯拉的充电技术 (35) （一）家用充电桩 (35) （二）超级充电桩 (37) （三）目的地充电桩 (38) （四）计划使用太阳能为超级充电站供电 (38) 五、电机及电控的主要技术 (38) （一）感应电机与永磁电机的对比 (39) （二）Model S采用三相交流感应电机 (40)

（三）双电机可以有效减少高速时的效率降低，并延长续航能力 (41) （四）电机的结构改进提效并易于自动化 (41) （五）逆变器采用分散塑封IGBT，实现低散热要求 (43) 六、车身的主要技术 (46) （一）全铝车身 (46) （二）Model X的双铰链鹰翼门 (47) 七、安全方面的主要技术 (48) （一）车身的安全设计 (49) （二）电池的安全性 (50) （三）信息安全技术 (51) 八、智能化技术 (51) （一）空中升级 (51) （二）远程诊断 (52) （三）自动求助 (52) （四）交互关系 (52)

特斯拉纯电动车的核心技术分析 一、特斯拉简介 特斯拉(Tesla)，是一家美国电动车及能源公司，产销电动车、太阳能板、及储能设备。总部位于美国加利福尼亚州硅谷帕洛阿尔托（Palo Alto）。 特斯拉第一款汽车产品Roadster发布于2008年，为一款两门运动型跑车。2012年，特斯拉发布了其第二款汽车产品——Model S，一款四门纯电动豪华轿跑车；第三款汽车产品为Model X，豪华纯电动SUV ，于2015年9月开始交付。特斯拉的下一款汽车为Model 3，首次公开于2016年3月，并将于2017年末开始交付。 2016年11月17日特斯拉电动车收购美国太阳能发电系统供应商SolarCity，使得特斯拉转型成为全球唯一垂直整合的能源公司，向客户提供包括Powerwall能源墙、太阳能屋顶等端到端的清洁能源产品。2017年2月1日，特斯拉汽车公司（Tesla Motors Inc．）正式改名为特斯拉（Tesla Inc．）。这意味着汽车不再是特斯拉的唯一业务。 二、特斯拉纯电动车主要功能特点 （一）Model S 主要特点 得益于特斯拉独特的纯电动动力总成，Model S 的性能表现十分出色，0-100公里/小时加速最快仅需2.7 秒。通过Autopilot 自动辅助驾驶（选装），Model S 还可以使高速公路驾驶更为安全且轻松，让你更好的享受驾驶乐趣。

深度揭秘特斯拉Model S底盘：电池组电机四驱

深度揭秘特斯拉Model S底盘：电池组/电机/四驱 特斯拉的第一代产品Roadster，用的是莲花Elise的底盘。这台车当时卖了2000多台。现在，这个经典的跑车底盘又被底特律电动车（Detroit Electric）拿来做另外一款“Roadster”了。 2012年，特斯拉发布Model S。底盘结构由特斯拉自主研发，并为其今后的车系奠定了基础。与燃油汽车不同，特斯拉一个底盘就可以涵盖所有级别的车型。比如将于2017年上市的Model 3，其底盘是在Model S的基础上缩短了轴距而已。 本期，我们来彻底解构下特斯拉Model S的底盘结构。共分为三部分来讲：电池组、电机，以及四驱。先从电池组说起。 特斯拉的电池，是特斯拉的核心专利技术之一，可以说是整台Model S最核心的一个零件。特斯拉一共拥有249项专利，其中有104项是跟电池有关的。与很多采用几个大的电池单元成电池组的布局不同，特斯拉采用的是与笔记本一样的电池。整台Model S的整备质量为2108kg（2.1吨），其中电池组的重量就占了600kg（0.6吨）。作为一辆D级豪华车，特斯拉Model S并没有超重。这在很大程度上得益于Model S的全铝车身。

由于电池组横贯于位于车辆底部，这使得Model S的重心得以降低，平衡了配重，从而提升了操控性。根据官方数据，Model S的前后配重比为48：52。 在Model S刚上市时，按照电池划分共有3款型号，分别是85kWh、60kWh，以及40kWh。2013年，由于40kWh车型销量惨淡，特斯拉决定停止销售。不久前，特斯拉又推出了70Kwh车型，来取代之前的60kWh版本。 值得一提的是，当年60kWh的车型与40kWh的车型，电池组其实是一样的；两者的区别在于，特斯拉将40kWh的电池进行了软件限制，从而在一个可容纳60kWh电量的电池组中，只有40kWh的电量可用。 而85kWh电池与60kWh电池的区别，主要是电池组中装配的电池单元数量。85kWh的电池组电压为400V，由一共16个电池包组成，每个电池包装配了444颗电池单元，所以这个电池组一共有7104颗电池组成。60kWh，则是由14个电池包，共计6216颗电池单元组成。这里所说的电池单元，是由松下提供的 NCR-18650A型电池。 18650是可充电锂离子电池的一种型号，它的命名来源于这种电池的尺寸 --18mm*65mm，但由于还要加入保护电路，所以电池的实际尺寸要略微大几零点几毫米。18650电池的主要用途，是笔记本电脑的电池，它有很多生产厂商；而特斯拉则选用了松下提供的18650电池，但要注意特斯拉使用的电池与笔记本中的电池还是有差别。18650只是一个统称。

特斯拉电动车2013全球销量

特斯拉电动车2013全球销量

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特斯拉的2013年：利润超1亿美元售车2.25万辆 2014年02月20日来源：第一电动网 特斯拉（NASDAQ:TSLA）将2013年一季度创出的盈利“奇迹”延续到了全年。按照特斯拉一贯采纳的非通用会计准则（Non-GAAP），特斯拉2013年赚得超过1亿美元的利润。特斯拉的电动汽车销量也大为增长，达到了约2.25万辆。 近日，特斯拉发布财务数据称，根据GAAP准则，即不计入股权奖励支出及其他一次性项目，特斯拉2013年营业收入为20.13496亿美元，对比2012年的4.13256亿美元，同比增长387.2%。而按照非GAAP准则，特斯拉2013年营业收入为24.77662亿美元，对比2012年的4.13256亿美元，同比增长499.5%。 根据GAAP准则，特斯拉去年亏损额为7401.4万美元，2012年则为39621.3万美元，同比削减81.3%。按照非GAAP准则，特斯拉去年实现利润10356.3万美元，2012年亏损34421.4万美元。 特斯拉汽车于美国时间本周三下午发布了2013年的致股东邮件。邮件显示，第四季度，特斯拉创纪录地销售了6892辆电动汽车，全年销量22477辆。 未来，特斯拉还计划在美国发展超级充电站网络和服务中心，推动汽车销售。此外特斯拉还预计，欧洲和中国市场将带来巨大销量。2014年的汽车总销量将达到3.5万辆，比今年的22477辆高55%。

特斯拉分析报告

特斯拉分析报告 Revised as of 23 November 2020

目录 特斯拉电动汽车国际发展分析报告 综合经营教育 组织：市场策划1301 班 指导老师：胡子娟 组长：符美丹 组员：徐宝怡、李嘉尊、张家梦、杨伟怡 华南农业大学珠江学院 电话： 乐享科技 2016-4-6

一、背景 (一)公司概况 2003年7月1日，马丁艾伯哈德与长期商业伙伴马克塔彭宁合伙成立特斯拉（TESLA）汽车公司，并将总部设在美国加州的硅谷地区2004年2月，埃隆马斯克向特斯拉投资630万美元，但条件是出任公司董事长、拥有所有事务的最终决定权，而马丁艾伯哈德作为特斯拉之父任公司的CEO。不可忽视的是，特斯拉的背后，站着众多超级投资人。其中包括谷歌创始人拉里佩奇、谢尔盖布林等人，还包括丰田、戴姆勒奔驰的子公司和松下等传统汽车巨头。松下是特斯拉的锂电池电芯供应商，而特斯拉汽车的部分设计也受益于奔驰的启发特斯拉刷新了世界对电动汽车的认知，从这一点出发，特斯拉可以称得上是一个改变了世界的公司。特斯拉当前的创新应该更多在商业模式以及对电动汽车的发展的推动上，是一个令人充满期待，并且值得让人敬佩的公司。从诞生之日起，特斯拉的品牌一直都与“环保”、“高科技”等标签贴在一起，时时闪现出高冷的明星气质。这的确在品牌初期为其吸引了众多支持者，并获得了意想不到的营销效果。而借助这层光环加持，特斯拉开始了自己的故事。在本土市场较为稳定之后特斯拉开始开拓中国市场。 (二)公司产品 1.T esla Roadster 2.T esla Model S 3.T esla Model X

(完整版)特斯拉汽车案例介绍

特斯拉汽车案例介绍 一、 1、发展背景 2003年在美国硅谷成立了一家汽车公司，这个在选址上独具一格的传统汽车公司名为特斯拉，在企业一开始发展的阶段就将公司的选址放在美国西部的科技圣地——硅谷，这个二十一世纪电子和计算机业的王国，突然诞生了一家汽车公司，于周围的企业显得格格不入，但就在这样的环境下，从硅谷走出了一辆通向未来的汽车。 在众多传统巨头坚持不住的时候，特斯拉默默无闻的坚持了下来，并且发展的如火如荼，目前特斯拉的股票突破了100美元大关，直追日本丰田，成为了美国股市之中为数不多的超过100美元的汽车公司，超越了众多的汽车行业巨头。这个不太出名的小汽车企业是如何发展起来的呢？ 在1990年，由美国通用汽车研发并制造了第一款现代化电动汽车EV-1，这款低风阻、双门双座的电动汽车却采用租赁的方式对外进行，大多数租户第一次接触到现代电动汽车，对EV-1表现得尤为满意，但EV-1的结局却让人感慨万千，由于这款车的投入和产量不大，在生产一千多台后停止生产，1999年通用回收销毁EV-1，让租户们很不理解，大多数租户都愿意对租的车进行购买，最终全部被通用回收，分批销毁，最后只有几台放置在博物馆。参于EV-1的工程师不甘心失败，于是创建研究铅酸电池的AC Propulsion汽车公司，由于研发铅酸电池一直没大规模突破，马丁·艾伯哈德投资了15万美元，他希望尝试用笔记本电脑的锂电池作为电动汽车电池， 艾伯哈德劝说AC Propulsion公司为他制造一辆电动汽车，就这样科尼在无意成立汽车公司。艾伯哈德于是决定自己来。艾伯哈德在寻找创业项目时发现，停放跑车的私家车道上常有着丰田混合动力汽车的身影。艾伯哈德觉得，所以，有了将跑车和电动汽车结合的主意。2003年7月1日，马丁·艾伯哈德于长期商业伙伴马克·塔彭宁合伙成立特斯拉（TESLA）汽车公司，并将总部设在美国加州的硅谷地区。 2公司现状 特斯拉企旗下现售四款电动汽车，以经营高性能纯电动汽车为主，早在2016年年营业额突破了70亿，说起电动汽车，在这个领域内特斯拉却是行业中的大头，处于翘楚地位，无人驾驶技术较为先进成熟，量产出L3级高度驾驶系统，同样是搭载锂电池的特斯拉续航能力远远超越其他同类电动汽车，是目前新能源汽车领域的佼佼者，更是当前新能源行业的领头羊。在2018全球电动汽车销量排行中特斯拉汽车占据了前五位中的三个，市场份额就占据了整个市场中的11%，可以彰显出特斯拉在电动汽车行业的领导地位。

Tesla motor特斯拉电动汽车分析

Tesla Motors Norbert Binkiewicz Justin Chen Matt Czubakowski June 4, 2008

1SWOT Analysis Strengths ?Good engineering and technology research capability ?Able to raise large amounts of capital ?First mover advantage; the first company to offer a relatively practical fully electric car, customers include high-profile figures like Arnold Schwarzenegger, George Clooney, and Jay Leno ?Designs and builds many of the components in its cars, including the power electronics, motor and battery packs Weaknesses ?Doesn’t have much brand recognition among the general public ? A very small company with small sales volume, so no economies of scale ?Possible supply problems with components, especially if demand increases ?The Tesla Roadster hasn’t been on the market for very long, the longevity of fully electric cars remains to be proven Opportunities ?Moving towards the family sedan market and making a product that is meant for more of the automotive market ?Price of oil and gasoline skyrocketing, making the price premium for an electric car less of an issue ?Expanding into developing lithium-ion batteries and other energy technologies, partnering with a battery company to improve battery technology Threats ?Wrightspeed X1, a prototype high performance electric car that caters to the same market; the only direct competitor to Tesla that offers a similar product ?Large automobile companies entering the market with full and hybrid electric cars, the GM Volt and Toyota Prius ?The price of oil falling dramatically in the short run ? A competitor having a breakthrough in related energy technologies, like hydrogen powered cars, natural gas, or ethanol

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