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

Shifting strategy for step change transmission vehicle : a comparative study and design method

In this paper, gear shifting characteristics of vehicles equipped with an Automated Manual Transmission (AMT) and PowerShift-AMT (PS-AMT) are analyzed and compared in order to study their effects on the fuel economy. A "rapid" upshift strategy proposed for PS-AMT shows that relative fuel saving can be achieved up to 8.5% compared to the case of the AMT with prescribed gear shifting schedule over NEDC. To fully explore potential fuel savings with respect to gear shifting strategy, Dynamic Programming (DP) is utilized for PS-AMT system to derive a globally optimal shifting schedule. Simulation result reveals that relative fuel economy improvement can be reached up 10.7%. A systematic approach is proposed to study a tradeoff between fuel consumption and driveability with respect to gear shifting strategy. The optimization problem of which the cost function consists of fuel consumption, power reserve and shifting cost is formulated and solved for optimal gear shifting strategies by the DP algorithm. In this study, it is found that fuel consumption benefit can be obtained at a certain loss or even benefit of accumulative power reserve by an approximated linear slope factor of -5. This design method is verified through simulations of forward facing dynamic powertrain model. Finally, in order to further improve the fuel saving potential and the driveability, a Hybrid Electric PS-AMT prototype is introduced and design consideration concerning the optimal control strategy is discussed

Optimal control of the gearshift command for hybrid electric vehicles

This paper proposes a design method for the Energy Management Strategy to explore the potential fuel saving of a Hybrid Electric Vehicle equipped with an Automated Manual Transmission. The control algorithm is developed based on the combination of Dynamic Programming and Pontryagins Minimum Principle to optimally control the discrete gear shift command in addition to the continuous power split between the internal combustion engine and the electric machine. The proposed method outperforms Dynamic Programming in terms of computational efficiency with 171 times faster without loss of accuracy. Simulation results for a mid-sized Hybrid Electric Vehicle on the New European Drive Cycle show that by further optimizing the gear shift strategy, an additional fuel saving of 20.3% can be reached. Furthermore, with the start-stop functionality available, it is shown that the two-point boundary value problem following from Pontryagins Minimum Principle can not be solved with sufficient accuracy without loss of optimality. This means that the finding of a constant value for the Lagrange multiplier while satisfying the battery state of energy at the terminal time is not always guaranteed. Therefore an alternative approach of state of energy feedback control to adapt the Lagrange multiplier is adopted. The obtained results are very close to the globally optimal solution from Dynamic Programming. Simulation results, including the start-stop functionality, show the relative fuel saving can be up to 26.8% compared to the case of a standard gear shift strategy

An optimal control-based algorithm for Hybrid Electric Vehicle using preview route information

Control strategies for Hybrid Electric Vehicles (HEVs) are generally aimed at optimally choosing the power distribution between the internal combustion engine and the electric motor in order to minimize the fuel consumption and/or emissions. Using vehicle navigation systems in combination with Global Positioning Systems and Geographical Information Systems allow further optimization of the power distribution by utilizing the route information. In this paper, a new control algorithm based on a combination of dynamic programming and classical optimal control theory is proposed for the Energy Management System in parallel HEVs to improve the fuel economy over a preview route segment. The proposed algorithm optimizes not only the gear position and the engine power yet also the vehicle velocity. The vehicle is controlled to complete this route segment in a predefined time length. Using this method more than 11% fuel saving is computed on an optimized cycle compared to a standard city cycle with equal time length and average speed

Optimal Shifting Strategy for a Parallel Hybrid Electric Vehicle

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Modelling dispersion in laminar and turbulent flows in an open channel based on centre manifolds using 1D-IRBFN method

Centre manifold method is an accurate approach for analytically constructing an advection-diffusion equation (and even more accurate equations involving higher-order derivatives) for the depth-averaged concentration of substances in channels. This paper presents a direct numerical verification of this method with examples of the dispersion in laminar and turbulent flows in an open channel with a smooth bottom. The one-dimensional integrated radial basis function network (1D-IRBFN) method is used as a numerical approach to obtain a numerical solution for the original two-dimensional (2-D) advection-diffusion equation. The 2-D solution is depth-averaged and compared with the solution of the 1-D equation derived using the centre manifolds. The numerical results show that the 2-D and 1-D solutions are in good agreement both for the laminar flow and turbulent flow. The maximum depth-averaged concentrations for the 1-D and 2-D models gradually converge to each other, with their velocities becoming practically equal. The obtained numerical results also demonstrate that the longitudinal diffusion can be neglected compared to the advection

Optimal gear shift strategies for fuel economy and driveability

This paper aims at designing optimal gear shift strategies for conventional passenger vehicles equipped with discrete ratio transmissions. In order to study quantitatively an optimal trade-off between the fuel economy and the driveability, the vehicle driveability is addressed in a fuel-optimal gear shift algorithm based on dynamic programming by three methods: method 1, weighted inverse of power reserve; method 2, constant power reserve; method 3, variable power reserve. Furthermore, another method based on stochastic dynamic programming is proposed to derive an optimal gear shift strategy over a number of driving cycles in an average sense, hence taking into account the vehicle driveability. In contrast with the dynamic-programming-based strategy, the obtained gear shift strategy based on stochastic dynamic programming is real time implementable. A comparative analysis of all proposed gear shift methods is given in terms of the improvements in the fuel economy and the driveability. The variable-power-reserve method achieves the highest fuel economy without sacrificing the driveability

From a Hard to Soft Approach for Flood Management in the Vietnamese Mekong Delta: Integrating Ecological Engineering for Urban Sustainability in My Tho City

Flooding is one of the leading challenges faced by delta cities in the world. Flood risk management using flood control infrastructure (FCI) is a popular solution to prevent flood damage; however, this is receiving enormous criticism due to its negative impacts on urban ecosystems. Recently, there have been new approaches to flood risk management that gradually shifted the focus away from FCI, such as ecological infrastructure (EI) based approaches. However, the conventional thinking that cities cannot be safe without FCI seems an immutable one, especially in developing countries. This study firstly assessed human–river interaction in direct relation to FCI and outlined the limitations of FCI. Then, an urban ecology research model was used to conduct a case study in the Vietnamese Mekong Delta (VMD), in which the interaction between factors, including riverine urbanization, FCI formation dynamics, the changing hydrological regime, flood risk, and riverine ecosystem degradation were evaluated. Due to the dynamism and complexity of the interactions between humans and rivers at the VMD, this study attempts to demonstrate that building the ability to adapt to flood risks based on EI will have a crucial role in enhancing the sustainability of delta cities. Through a case study in My Tho City (MTC) a flood resilience management scenario for a riverine urban area along the Mekong River was developed to discuss the role of EI in flood risk reduction and the restoration of riverine native ecosystems. The findings from this study suggests that EI should be considered as an effective and indispensable design tool for the conservation of riparian ecological corridors and public open spaces—which is a major challenge for urban areas in the context of increasing climate change impacts in the VMD

Extragalactic Submillimetric Surveys with BLAST

The Balloon-borne Large Aperture Submillimeter Telescope (BLAST) has recently conducted an extragalactic submillimetric survey of the Chandra Deep Field South region of unprecedented size, depth, and angular resolution in three wavebands centered at 250, 350, and 500 µm. BLAST wavelengths are chosen to study the Cosmic Infrared Background near its peak at 200 µm. We find that most of the CIB at these wavelengths is contributed by galaxies detected at 24 µm by the MIPS instrument on Spitzer, and that the source counts distribution shows a population with strongly evolving density and luminosity. These results anticipate what can be expected from the surveys that will be conducted with the SPIRE instrument on the Herschel space observatory