Gear shift strategies for automotive transmissions

The development history of automotive engineering has shown the essential role of transmissions in road vehicles primarily powered by internal combustion engines. The engine with its physical constraints on the torque and speed requires a transmission to have its power converted to the drive power demand at the vehicle wheels. Under dynamic driving conditions, the transmission is required to shift in order to match the engine power with the changing drive power. Furthermore, a gear shift decision is expected to be consistent such that vehicle can remain in the next gear for a period of time without deteriorating the acceleration capability. Therefore, an optimal conversion of the engine power plays a key role in improving the fuel economy and driveability. Moreover, the consequences of the assumptions related to the discrete state variable-dependent losses, e.g. gear shifting, clutch slippage and engine starting, and their e¿ect on the gear shift control strategy are necessary to be analyzed to yield insights into the fuel usage. The ¿rst part of the thesis deals with the design of gear shift strategies for electronically controlled discrete ratio transmissions used in both conventional vehicles and Hybrid Electric Vehicles (HEVs). For conventional vehicles, together with the fuel economy, the driveability is systematically addressed in a Dynamic Programming (DP) based optimal gear shift strategy by three methods: i) the weighted inverse of the power re¬serve, ii) the constant power reserve, and iii) the variable power reserve. In addition, a Stochastic Dynamic Programming (SDP) algorithm is utilized to optimize the gear shift strategy, subject to a stochastic distribution of the power request, in order to minimize the expected fuel consumption over an in¿nite horizon. Hence, the SDP-based gear shift strategy intrinsically respects the driveability and is realtime implementable. By per¬forming a comparative analysis of all proposed gear shift methods, it is shown that the variable power reserve method achieves the highest fuel economy without deteriorating the driveability. Moreover, for HEVs, a novel fuel-optimal control algorithm, consist-ing of the continuous power split and discrete gear shift, engine on-o¿ problems, based on a combination of DP and Pontryagin’s Minimum Principle (PMP) is developed for the corresponding hybrid dynamical system. This so-called DP-PMP gear shift control approach benchmarks the development of an online implementable control strategy in terms of the optimal tradeo¿ between calculation accuracy and computational e¿ciency. Driven by an ultimate goal of realizing an online gear shift strategy, a gear shift map design methodology for discrete ratio transmissions is developed, which is applied for both conventional vehicles and HEVs. The design methodology uses an optimal gear shift algorithm as a basis to derive the optimal gear shift patterns. Accordingly, statis¬tical theory is applied to analyze the optimal gear shift pattern in order to extract the time-invariant shift rules. This alternative two-step design procedure makes the gear shift map: i) respect the fuel economy and driveability, ii) be consistent and robust with respect to shift busyness, and iii) be realtime implementation. The design process is ¿exible and time e¿cient such that an applicability to various powertrain systems con¿gured with discrete ratio transmissions is possible. Furthermore, the study in this thesis addresses the trend of utilizing the route information in the powertrain control system by proposing an integrated predictive gear shift strategy concept, consisting of a velocity algorithm and a predictive algorithm. The velocity algorithm improves the fuel economy in simulation considerably by proposing a fuel-optimal velocity trajectory over a certain driving horizon for the vehicle to follow. The predictive algorithm suc¬cessfully utilizes a prede¿ned velocity pro¿le over a certain horizon in order to realize a fuel economy improvement very close to that of the globally optimal algorithm (DP). In the second part of the thesis, the energetic losses, involved with the gear shift and engine start events in an automated manual transmission-based HEV, are modeled. The e¿ect of these losses on the control strategies and fuel consumption for (non-)powershift transmission technologies is investigated. Regarding the gear shift loss, the study ¿rstly ever discloses a perception of a fuel-e¿cient advantage of the powershift transmissions over the non-powershift ones applied for commercial vehicles. It is also shown that the engine start loss can not be ignored in seeking for a fair evaluation of the fuel economy. Moreover, the sensitivity study of the fuel consumption with respect to the prediction horizon reveals that a predictive energy management strategy can realize the highest achievable fuel economy with a horizon of a few seconds ahead. The last part of the thesis focuses on investigating the sensitivity of an optimal gear shift strategy to the relevant control design objectives, i.e. fuel economy, driveability and comfort. A singu¬lar value decomposition based method is introduced to analyze the possible correlations and interdependencies among the design objectives. This allows that some of the pos¬sible dependent design objective(s) can be removed from the objective function of the corresponding optimal control problem, hence thereby reducing the design complexity

On Optimal Mission Planning for Vehicles over Long-distance Trips

This thesis proposes a mission planner for vehicles over long-distance trips, for finding the optimal trade-off between trip time, energy efficiency, anddriver comfort, subject to road information, traffic situations, and weather conditions. The mission planner consists of three components, i.e. logisticsplanner, eco-driving supervisor, and thermal and charging supervisor. The logistics planner aims at optimising the mission start and/or finish time byminimising energy consumption and trip time. The eco-driving supervisor computes the velocity profile of the driving vehicle, by optimising the energyconsumption and penalising driver discomfort. To do so, an online-capable algorithm has been formulated in a model predictive control framework, subject to road and traffic information, and the pre-optimised mission start and/or finish time. This algorithm is computationally efficient and enables the driving vehicle to adapt and optimally respond to predicted disturbances within a short amount of time. Eco-driving has also been achieved for a vehicleconfronted with wind, by applying stochastic dynamic programming method. The thermal and charging supervisor regulates battery temperature and state of charge by coordinating the energy use of different thermal components. Within the thermal and charging supervisor design, a heat pump has been included for waste heat recovery purposes. Also, the charging stops have been optimally planned, in favour of energy efficiency and trip time. The performance of the proposed algorithms over a road with a hilly terrain is assessed using simulations. According to the simulation results, it is observed that total travel time is reduced up to 5.5 % by optimising the mission start time, when keeping an average cruising speed of about 75 km/h. Also, compared to standard cruise control, the energy savings of using this algorithm is up to 11.6 %. Furthermore, total charging time and energy consumption are reduced by up to 19.4 % and 30.6 %, respectively by developing the thermal and charging supervisor, compared to a case without the heat pump activated and without charge point optimisation

Transportation Mission-Based Optimization of Heavy Combination Road Vehicles and Distributed Propulsion, Including Predictive Energy and Motion Control

This thesis proposes methodologies to improve heavy vehicle design by reducing the total cost of ownership and by increasing energy efficiency and safety.Environmental issues, consumers expectations and the growing demand for freight transport have created a competitive environment in providing better transportation solutions. In this thesis, it is proposed that freight vehicles can be designed in a more cost- and energy-efficient manner if they are customized for narrow ranges of operational domains and transportation use-cases. For this purpose, optimization-based methods were applied to minimize the total cost of ownership and to deliver customized vehicles with tailored propulsion components that best fit the given transportation missions and operational environment. Optimization-based design of the vehicle components was found to be effective due to the simultaneous consideration of the optimization of the transportation mission infrastructure, including charging stations, loading-unloading, routing and fleet composition and size, especially in case of electrified propulsion. Implementing integrated vehicle hardware-transportation optimization could reduce the total cost of ownership by up to 35% in the case of battery electric heavy vehicles. Furthermore, in this thesis, the impacts of two future technological advancements, i.e., heavy vehicle electrification and automation, on road freight transport were discussed. It was shown that automation helps the adoption of battery electric heavy vehicles in freight transport. Moreover, the optimizations and simulations produced a large quantity of data that can help users to select the best vehicle in terms of the size, propulsion system, and driving system for a given transportation assignment. The results of the optimizations revealed that battery electric and hybrid heavy combination vehicles exhibit the lowest total cost of ownership in certain transportation scenarios. In these vehicles, propulsion can be distributed over different axles of different units, thus the front units may be pushed by the rear units. Therefore, online optimal energy management strategies were proposed in this thesis to optimally control the vehicle motion and propulsion in terms of the minimum energy usage and lateral stability. These involved detailed multitrailer vehicle modeling and the design and solution of nonlinear optimal control problems

A Microscopic Simulation Study of Applications of Signal Phasing and Timing Information in a Connected Vehicle Environment

The connected vehicle technology presents an innovative way of sharing information between vehicles and the transportation infrastructure through wireless communications. The technology can potentially solve safety, mobility, and environmental challenges that face the transportation sector. Signal phasing and timing information is one category of information that can be broadcasted through connected vehicle technology. This thesis presents an in-depth study of possible ways signal phasing and timing information can be beneficial as far as safety and mobility are concerned. In total, three studies describing this research are outlined. The first study presented herein focuses on data collection and calibration efforts of the simulation model that was used for the next two studies. The study demonstrated a genetic algorithm procedure for calibrating VISSIM discharge headways based on queue discharge headways measured in the field. Video data was used to first compute intersection discharge headways for individual vehicle queue position and then to develop statistical distributions of discharge headways for each vehicle position. Except for the 4th vehicle position, which was best fitted by the generalized extreme value (GEV) distribution, the Log-logistic distribution was observed to be the best fit distribution for the rest of vehicle positions. Starting with the default values, the VISSIM parameters responsible for determining discharge headways were heuristically adjusted to produce optimal values. The optimal solutions were achieved by minimizing the Root Mean Square Error (RMSE) between the simulated and observed data. Through calibration, for each vehicle position, it was possible to obtain the simulated headways that reflect the means of the observed field headways. However, calibration was unable to replicate the dispersion of the headways observed in the field mainly due to VISSIM limitations. Based on the findings of this study, future work on calibration in VISSIM that would account for the dispersion of mixed traffic flow characteristics is warranted. The second study addresses the potential of connected vehicles in improving safety at the vicinity of signalized intersections. Although traffic signals are installed to reduce the overall number of collisions at intersections, rear-end collisions are increased due to signalization. One dominant factor associated with rear-end crashes is the indecisiveness of the driver, especially in the dilemma zone. An advisory system to help the driver make the stop-or-pass decision would greatly improve intersection safety. This study proposed and evaluated an Advanced Stop Assist System (ASAS) at signalized intersections by using Infrastructure-to-Vehicle (I2V) and Vehicle-to-Vehicle (V2V) communication. The proposed system utilizes communication data, received from Roadside Unit (RSU), to provide drivers in approaching vehicles with vehicle-specific advisory speed messages to prevent vehicle hard-braking upon a yellow and red signal indication. A simulation test bed was modeled using VISSIM to evaluate the effectiveness of the proposed system. The results demonstrate that at full market penetration (100% saturation of vehicles equipped with on-board communication equipment), the proposed system reduces the number of hard-braking vehicles by nearly 50%. Sensitivity analyses of market penetration rates also show a degradation in safety conditions at penetration rates lower than 40%. The results suggest that at least 60% penetration rate is required for the proposed system to minimize rear-end collisions and improve safety at the signalized intersections. The last study addresses the fact that achieving smooth urban traffic flow requires reduction of excessive stop-and-go driving on urban arterials. Smooth traffic flow comes with several benefits including reduction of fuel consumption and emissions. Recently, more research efforts have been directed towards reduction of vehicle emissions. One such effort is the use of Green Light Optimal Speed Advisory (GLOSA) systems which use wireless communications to provide individual drivers with information on the approaching traffic signal phase and advisory speeds to arrive at the intersection on a green phase. Previously developed GLOSA algorithms do not address the impact of time to discharge queues formed at the intersection. Thus, this study investigated the influence of formed intersection queues on the performance of GLOSA systems. A simulation test-bed was modeled inside VISSIM to evaluate the algorithm’s effectiveness. Three simulation scenarios were designed; the baseline with no GLOSA in place, scenario 2 with GLOSA activated and queue discharge time not considered, and scenario 3 with GLOSA activated and where queue dissipation time was used to compute advisory speeds. At confidence level the results show a significant reduction in the time spent in queue when GLOSA is activated (scenarios 2 and 3). The change in the average number of stops along the corridor was found not to be significant when the base scenario was compared against scenario 2. However, a comparison between scenarios 2 and 3 demonstrates a significant reduction in the average number of stops along the corridor, and also in the time spent waiting in queue

3rd Annual Meeting of the Portuguese Association of Energy Economics & 5th Meeting of Environmental and Energy Economics

The 3rd Annual Conference of the Portuguese Association of Energy Economics – APEEN and the International Meeting on Energy and Environmental Economics – ME3 took place on the 18-19 October 2018 in Braga, Portugal. The event was hosted by the Universidade do Minho and gathered the contributions of specialists in Energy and Environmental Economics to enrich the debate about the many issues raised by the management of resources and waste. The main topic was Managing Resources and Waste: challenges for Energy and Environmental Economics beyond 2030. ​Natural resources are the keystone in environmental and energy economics. Nowadays, resource management cannot ignore waste, traditionally seen as a by-product of consumption and production decisions, but increasingly recognized as a source of energy or as new type of resource

Topics in Electromobility and Related Applications

In this thesis, we mainly discuss four topics on Electric Vehicles (EVs) in the context of smart grid and smart transportation systems. The first topic focuses on investigating the impacts of different EV charging strategies on the grid. In Chapter 3, we present a mathematical framework for formulating different EV charging problems and investigate a range of typical EV charging strategies with respect to different actors in the power system. Using this framework, we compare the performances of all charging strategies on a common power system simulation testbed, highlighting in each case positive and negative characteristics. The second topic is concerned with the applications of EVs with Vehicle-to-Grid (V2G) capabilities. In Chapter 4, we apply certain ideas from cooperative control techniques to two V2G applications in different scenarios. In the first scenario, we harness the power of V2G technologies to reduce current imbalance in a three-phase power network. In the second scenario, we design a fair V2G programme to optimally determine the power dispatch from EVs in a microgrid scenario. The effectiveness of the proposed algorithms are verified through a variety of simulation studies. The third topic discusses an optimal distributed energy management strategy for power generation in a microgrid scenario. In Chapter 5, we adapt the synchronised version of the Additive-Increase-Multiplicative-Decrease (AIMD) algorithms to minimise a cost utility function related to the power generation costs of distributed resources. We investigate the AIMD based strategy through simulation studies and we illustrate that the performance of the proposed method is very close to the full communication centralised case. Finally, we show that this idea can be easily extended to another application including thermal balancing requirements. The last topic focuses on a new design of the Speed Advisory System (SAS) for optimising both conventional and electric vehicles networks. In Chapter 6, we demonstrate that, by using simple ideas, one can design an effective SAS for electric vehicles to minimise group energy consumption in a distributed and privacy-aware manner; Matlab simulation are give to illustrate the effectiveness of this approach. Further, we extend this idea to conventional vehicles in Chapter 7 and we show that by using some of the ideas introduced in Chapter 6, group emissions of conventional vehicles can also be minimised under the same SAS framework. SUMO simulation and Hardware-In-the-Loop (HIL) tests involving real vehicles are given to illustrate user acceptability and ease of deployment. Finally, note that many applications in this thesis are based on the theories of a class of nonlinear iterative feedback systems. For completeness, we present a rigorous proof on global convergence of consensus of such systems in Chapter 2