Ajosyklin epävarmuus ja kaupunkibussien energiankulutus: analyysi ja optimointi

The research presented here studied the effect of driving cycle variation and passenger load uncertainty on the energy consumption of city buses. Furthermore, different methods for reducing the energy consumption were analyzed and compared. The research was conducted with simulation studies. In order to generate a large quantity of varying realistic cycles for a single bus route, a novel driving cycle synthetization algorithm was developed. The algorithm is capable of synthesizing a large number of cycles based on only a handful of measurements by exploring different combinations of events. Cycles generated with the algorithm were employed to compare the energy consumption of different city bus powertrain topologies under uncertainty in the driving cycle and passenger load. Simulated bus powertrain topologies included: compressed natural gas, diesel, parallel hybrid, series hybrid, hydrogen fuel cell hybrid, and battery electric bus. Synthetic driving cycles generated with the novel cycle synthesis algorithm were shown to maintain the statistical properties of the original measured cycles with good accuracy. The presented algorithm could be utilized to further optimize city buses for the routes they will be operated on. Energy consumption results acquired from the simulation studies indicated that battery electric buses are the most robust option against driving cycle uncertainty. Diesel buses appeared to be the most sensitive to the driving aggressiveness. However, the results displayed a strong correlation between energy consumption and driving aggressiveness with all types of powertrains. This suggests that steps should be taken to limit high-speed accelerations of city buses regardless of powertrain type. Battery electric buses were further studied by comparing component-choice-related methods for reducing the energy consumption. The methods included using an aluminum chassis instead of a steel chassis, employing a low-height body for reduced aerodynamic drag, using low-rolling-resistance class C tires, and utilizing an electric heat pump instead of a more conventional electric heater. A novel problem formulation for driving optimization was devised for a nonlinear model predictive controller. The driving optimization algorithm was used to compare the energy savings achievable with predictive driving to those achieved with the component-choice-related methods. Out of all the considered consumption reduction methods, the heat pump produced the highest energy savings in cold conditions. However, the relative effectiveness of the heat pump was significantly influenced by the ambient temperature and driving cycle. The aluminum chassis provided higher consumption reductions than the low-rolling-resistance tires, but the influence of the lighter chassis was highly dependent on the aggressiveness of the driving. On average, the predictive driving achieved higher energy savings than the aluminum chassis. Applying all of the methods simultaneously resulted in an average consumption reduction of more than 30 %.Tutkimuksessa selvitettiin perusteellisesti ajosyklin vaihteluiden ja matkustajakuorman epävarmuuden vaikutusta kaupunkibussien energiankulutukseen. Lisäksi tutkittiin metodeja energiankulutuksen vähentämiseksi. Tutkimus toteutettiin simulaatioiden avulla. Uusi ajosyklien syntetisointialgoritmi kehitettiin, jotta pystyttäisiin generoimaan suuri määrä vaihtelevia realistisia syklejä tietylle bussireitille. Algoritmi kykenee syntetisoimaan suuren määrän syklejä pienen mittadatajoukon perusteella käyttämällä erilaisia tapahtumien kombinaatioita. Synteesialgoritmin avulla vertailtiin, kuinka ajosykli- ja matkustajakuormaepävarmuus vaikuttavat energiankulutukseen erilaisilla käyttövoimajärjestelmän topologioilla. Seuraavia kaupunkibussien käyttövoimajärjestelmiä vertailtiin simulaatioissa: paineistettu maakaasu, diesel, rinnakkais- ja sarjahybridi, vetypolttokennohybridi sekä täyssähkö. Tulokset osoittivat, että syklisynteesialgoritmi kykenee tuottamissaan sykleissä säilyttämään al-kuperäisten mitattujen syklien tilastolliset ominaisuudet tarkasti. Tällaista syklisynteesiä olisi mah-dollista käyttää esimerkiksi optimoimaan kaupunkibusseja paremmin niille reiteille, joilla niitä tiedetään tultavan käyttämään. Energiankulutustutkimusten tulokset osoittivat, että sähköbussit ovat robusteimpia ajosykliepävarmuutta vastaan. Dieselbussit puolestaan osoittautuivat kaikista herkimmiksi ajotyylin aggressiivisuudelle. Tuloksista kuitenkin myös havaittiin energiankulutuksen korreloivan voimakkaasti ajotyylin aggressiivisuuden kanssa kaikenlaisilla käyttövoimajärjestelmän konfiguraatioilla. Tämä tulos kertoo, että olisi erityisen tärkeää rajoittaa kaupunkibussien kiihtyvyyksiä korkeissa nopeuksissa riippumatta bussin käyttövoimajärjestelmästä. Bussien komponentteihin liittyviä energiankulutuksen vähentämisen metodeja vertailtiin sähköbussin tapauksessa. Metodeihin kuului kevyen alumiinirungon käyttäminen teräksisen rungon sijaan, matalan koriprofiilin käyttö ilmanvastuksen pienentämiseksi, pienemmän vierimisvastuksen omaavien C-luokan renkaiden käyttäminen ja sähköisen lämpöpumpun käyttö perinteisen sähkölämmittimen sijaan. Epälineaariselle mallipohjaiselle ennakoivalle kontrollerille luotiin uudenlainen ajo-optimoinnin ongelman muotoilu. Ajo-optimointialgoritmia käytettiin vertailemaan ennakoivalla ajolla saavutettavia energiasäästöjä aiemmin mainittujen metodien tuottamiin säästöihin. Lämpöpumppu tuotti suurimmat energiasäästöt vertailluista metodeista kylmissä ulkoilmaolosuhteissa. Lämpöpumpun kyky vähentää energiankulutusta suhteessa muihin metodeihin riippui kuitenkin voimakkaasti ulkoilman lämpötilasta sekä ajosyklin tyypistä. Alumiinirunko vähensi kulutusta enemmän kuin C-luokan renkaiden käyttäminen, mutta kevyemmän rungon vaikutus riippui merkittävästi ajotyylin aggressiivisuudesta. Ennakoiva ajo tuotti keskimäärin suuremmat energiasäästöt kuin alumiininen runko. Kun kaikkia metodeja käytettiin yhtä aikaa, väheni bussin energiankulutus keskimäärin yli 30 %

Tien ja renkaan välisen kitkapotentiaalin arviointi inertia-anturin mittausten perusteella alhaisen kitkan olosuhteissa

Electronic driver aids have become commonplace in passenger cars in the last two decades. These systems improve safety by attempting to prevent the vehicle from exceeding the limits of its handling and becoming unstable. Those limits are largely defined by the tire-road friction potential. Consequently, the friction potential is one of the variables used in the control logics of these systems. Thus, by estimating the potential, the effectiveness of electronic driver aids can be significantly improved. The purpose of this thesis is to develop and test the accuracy of a novel friction estimation algorithm that uses the accelerations and yaw, pitch, and roll rates of the vehicle measured with an inertial sensor as its basis. The algorithm was designed to account for the effects of inclination and banking, as they influence the acceleration capabilities of the vehicle and the acceleration measurements. Three different versions of the algorithm were created so that the effects of compensating for inclination and bank angle could be assessed. Additionally, the algorithm was designed in such a way that it should be able to estimate the friction potential accurately in start maneuvers where the steering angle is high. The single-track model was incorporated into the algorithm for this purpose. The algorithm must also detect when the vehicle is on the limits of its handling, as it is only then that the measured friction coefficient is equal to the friction potential. The algorithm accomplishes this by monitoring the states of the driver aids. The algorithm was tested with simulations and experimental tests. The research vehicle was modelled in simulation software, including the most significant electronic driver aids. A variety of acceleration, braking, and cornering maneuvers were performed in order to test the capabilities of the algorithm on roads with varying inclinations and bank angles. The tests focused on low-friction conditions, as friction estimation is at its most beneficial in such circumstances. The results show that this novel algorithm is capable of estimating the friction potential accurately in most acceleration, braking, and cornering situations on inclined, banked, and level roads. However, the results also indicate that accounting for the inclination and the bank angle makes little difference in the friction estimation. The algorithm calculates the tire-road forces largely based on the longitudinal and lateral acceleration measurements of the inertial sensor, which contain a component of gravitational acceleration if the road is not level. Thus, the effects of inclination and bank angle get mostly compensated even in the versions that were not specifically designed to account for them. The results also show that the friction potential estimation produced by the single-track model in high steering angle start maneuvers contains significant error due to the two front tires producing forces in different directions in such situations.Elektronisista ajoavuista on tullut yleisiä henkilöautoissa viimeisten kahden vuosikymmenen aikana. Nämä järjestelmät parantavat turvallisuutta yrittämällä estää autoa ylittämästä suorituskykyrajojaan, jolloin auto muuttuu epästabiiliksi. Kyseiset rajat perustuvat laajalti renkaan ja tien väliseen kitkapotentiaaliin. Kitkapotentiaali on siksi yksi niistä muuttujista, joita nämä järjestelmät käyttävät ohjauslogiikoissaan. Täten ajoapujen toimintaa voidaan tehostaa merkittävästi estimoimalla kitkapotentiaalia. Tämän opinnäytetyön tarkoituksena on luoda uudenlainen kitkaestimointialgoritmi, jonka toiminta perustuu inertia-anturilla mitattaviin auton kiihtyvyyksiin ja kallistumis-, nyökkimis- ja pystykiertymänopeuksiin, ja tutkia sen tarkkuutta. Algoritmi suunniteltiin huomioimaan tien nousu- ja sivuttaiskulmien vaikutus, sillä ne vaikuttavat auton kiihtyvyysrajoihin ja mitattuihin kiihtyvyyslukemiin. Algoritmista luotiin kolme eri versiota, jotta tien kulmien kompensoinnin vaikutusta voitaisiin arvioida. Lisäksi algoritmi suunniteltiin siten, että sen pitäisi kyetä arvioimaan kitkapotentiaalia tarkasti myös sellaisissa liikkeellelähtötilanteissa, joissa ohjauskulma on suuri. Kaksipyörämalli sisällytettiin algoritmiin tätä tarkoitusta varten. Algoritmin on myös kyettävä havaitsemaan, milloin auto on lähellä suorituskykyrajojaan, koska arvioitu kitkakerroin on lähellä kitkapotentiaalia vain silloin. Algoritmi toteuttaa tämän tarkkailemalla ajoapujen tiloja. Algoritmia testattiin simulaatioiden ja koeautolla tehtävien testien avulla. Koeauto ja sen merkittävimmät ajoavut mallinnettiin simulaatio-ohjelmistossa. Monenlaisia kiihdytys-, jarrutus- ja kaarreajoliikkeitä suoritettiin algoritmin kykyjen tutkimiseksi erilaisia kallistuksia sisältävillä teillä. Testit keskittyivät alhaisen kitkan olosuhteisiin, sillä kitkaestimoinnista on eniten hyötyä juuri sellaisissa oloissa. Tulokset näyttävät, että luotu algoritmi kykenee arvioimaan kitkapotentiaalia tarkasti useimmissa kiihdytys-, jarrutus- ja kaarreajotilanteissa mäkisillä, kallistetuilla ja tasaisilla teillä. Tulokset kuitenkin myös osoittavat, että nousu- ja sivuttaiskulman huomiointi algoritmissa tuottaa vain pienen eron kitkaestimoinnissa. Algoritmi laskee rengasvoimat perustuen enimmäkseen inertia-anturin pitkittäis- ja sivuttaiskiihtyvyysmittauksiin, jotka sisältävät putoamiskiihtyvyyskomponentin, mikäli tie ei ole tasainen. Täten nousu- ja sivuttaiskulmien vaikutus kompensoituu enimmäkseen pois niissäkin algoritmiversioissa, joita ei erityisesti suunniteltu huomioimaan kyseisiä kulmia. Tulokset näyttävät myös, että kaksipyörämallin tuottama kitkapotentiaaliarvio suuren ohjauskulman liikkeellelähtötilanteissa sisältää merkittävästi virhettä johtuen siitä, että etupyörät tuottavat tällöin voimaa eri suuntiin

Predictive Braking With Brake Light Detection-Field Test

Driver assistance systems, such as adaptive cruise control, are an increasing commodity in modern vehicles. Our earlier experience of radar-based adaptive cruise control has indicated repeatable abrupt behavior when approaching a stopped vehicle at high speed, which is typical for extra-urban roads. Abrupt behavior in assisted driving not only decreases the passenger trust but also reduces the comfort levels of such systems. We present a design and proof-of-concept of a machine vision-enhanced adaptive cruise controller. A machine vision-based brake light detection system was implemented and tested in order to smoothen the transition from coasting to braking and ensure speed reduction early enough. The machine vision system detects the brake lights in front, then transmits a command to the cruise controller to reduce speed. The current paper reports the speed control system design and experiments carried out to validate the system. The experiments showed the system works as designed by reducing abrupt behavior. Measurements show that brake light-assisted cruise control was able to start deceleration about three seconds earlier than a cruise controller without brake light detection. Measurements also showed increased ride comfort with the maximum deceleration and minimum jerk levels improving from 5% to 31%.Peer reviewe

Reducing the energy consumption of electric buses with design choices and predictive driving

Peer reviewe

Cost-Benefit Analysis of Electric Bus Fleet with Various Operation Intervals

Electric buses are particularly suitable for city and suburban routes due to zero local exhaust and noise emissions. The operation schedule interval defines the charging power, bus fleet size and total cost of ownership of a bus. We propose a novel cost-benefit method for the scheduling of an electric city bus fleet on a single route. Three different charging infrastructure scenarios were considered. In the first scenario, only one charging station was used. The second scenario considered two charging stations that were located at the same terminus. In the third scenario, two charging stations were located at opposite terminuses. The costs and utilization rates of the buses were analyzed with operation intervals up to 40 minutes. The first scenario with a single charging station had the lowest costs for the entire bus fleet system when the utilization rate was considered. Furthermore, the results show that certain schedule intervals are more cost-beneficial in terms of vehicle specific life-cycle costs than others. In the future, the proposed method is expanded to aid the design of bus network scheduling under energy demand uncertainty.Peer reviewe

Energy Uncertainty Analysis of Electric Buses

Uncertainty in operation factors, such as the weather and driving behavior, makes it difficult to accurately predict the energy consumption of electric buses. As the consumption varies, the dimensioning of the battery capacity and charging systems is challenging and requires a dedicated decision-making process. To investigate the impact of uncertainty, six electric buses were measured in three routes with an Internet of Things (IoT) system from February 2016 to December 2017 in southern Finland in real operation conditions. The measurement results were thoroughly analyzed and the operation factors that caused variation in the energy consumption and internal resistance of the battery were studied in detail. The average energy consumption was 0.78 kWh/km and the consumption varied by more than 1 kWh/km between trips. Furthermore, consumption was 15% lower on a suburban route than on city routes. The energy consumption was mostly influenced by the ambient temperature, driving behavior, and route characteristics. The internal resistance varied mainly as a result of changes in the battery temperature and charging current. The energy consumption was predicted with above 75% accuracy with a linear model. The operation factors were correlated and a novel second-order normalization method was introduced to improve the interpretation of the results. The presented models and analyses can be integrated to powertrain and charging system design, as well as schedule planning.Peer reviewe

Brake Light Detection Algorithm for Predictive Braking

There has recently been a rapid increase in the number of partially automated systems in passenger vehicles. This has necessitated a greater focus on the effect the systems have on the comfort and trust of passengers. One significant issue is the delayed detection of stationary or harshly braking vehicles. This paper proposes a novel brake light detection algorithm in order to improve ride comfort. The system uses a camera and YOLOv3 object detector to detect the bounding boxes of the vehicles ahead of the ego vehicle. The bounding boxes are preprocessed with L*a*b colorspace thresholding. Thereafter, the bounding boxes are resized to a 30 × 30 pixel resolution and fed into a random forest algorithm. The novel detection system was evaluated using a dataset collected in the Helsinki metropolitan area in varying conditions. Carried out experiments revealed that the new algorithm reaches a high accuracy of 81.8%. For comparison, using the random forest algorithm alone produced an accuracy of 73.4%, thus proving the value of the preprocessing stage. Furthermore, a range test was conducted. It was found that with a suitable camera, the algorithm can reliably detect lit brake lights even up to a distance of 150 m

Brake Light Detection Algorithm for Predictive Braking

There has recently been a rapid increase in the number of partially automated systems in passenger vehicles. This has necessitated a greater focus on the effect the systems have on the comfort and trust of passengers. One significant issue is the delayed detection of stationary or harshly braking vehicles. This paper proposes a novel brake light detection algorithm in order to improve ride comfort. The system uses a camera and YOLOv3 object detector to detect the bounding boxes of the vehicles ahead of the ego vehicle. The bounding boxes are preprocessed with L*a*b colorspace thresholding. Thereafter, the bounding boxes are resized to a 30 × 30 pixel resolution and fed into a random forest algorithm. The novel detection system was evaluated using a dataset collected in the Helsinki metropolitan area in varying conditions. Carried out experiments revealed that the new algorithm reaches a high accuracy of 81.8%. For comparison, using the random forest algorithm alone produced an accuracy of 73.4%, thus proving the value of the preprocessing stage. Furthermore, a range test was conducted. It was found that with a suitable camera, the algorithm can reliably detect lit brake lights even up to a distance of 150 m

Prolongation of Battery Lifetime for Electric Buses through Flywheel Integration

Electrification of transportation is an effective way to tackle climate change. Public transportation, such as electric buses, operate on predetermined routes and offer quiet operation, zero local emissions and high energy efficiency. However, the batteries of these buses are expensive and wear out in use. The battery ageing is expedited by fast charging and power spikes during operation. The contribution of this paper is the reduction of the power spikes and thus a prolonged battery lifetime. A novel hybrid energy storage system for electric buses is proposed by introducing a flywheel in addition to the existing battery. A simulation model of the hybrid energy storage system is presented, including a battery ageing model to measure the battery lifetime. The bus was simulated during its daily driving operation on different routes with different energy management strategies and flywheel configurations. These different flywheels as well as the driving cycle had a significant impact on the battery life increase. The proposed hybrid battery/flywheel storage system resulted in a battery lifetime increase of 20% on average