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

Optimized design of multi-speed transmissions for parallel hybrid electric vehicles

In this paper, the optimal design of a multi-speed transmission system in terms of gear ratio, number of gears and gear shifting strategy is investigated for a parallel hybrid electric vehicle. The design procedure starts with the optimization of the transmission configuration to identify the optimal gear ratios for a specified number of gears. In order to avoid solving a complex co-optimization problem that involves numerous control variables for hybrid powertrain energy management (EM), gear ratios and gear shifting, the gear ratio optimization is properly decoupled from the co-optimization problem, while the optimal gear shifting strategy for the optimized gear ratios is determined jointly with the powertrain EM. The separation of the co-optimization makes it possible to solve individual problems by dynamic programming (DP), which guarantees global optimality. To show the impact of optimally designed and controlled transmission on fuel savings, the fuel economy solution of the proposed scheme is compared with the traditional EM and gear shifting optimization method that applies non-optimized gear ratios. Simulation examples verify the effectiveness of the proposed methodology and show the fuel savings incurred by the configuration optimization of the multi-speed transmission system

Numerical Strategies for Mixed-Integer Optimization of Power-Split and Gear Selection in Hybrid Electric Vehicles

This paper presents numerical strategies for a computationally efficient energy management system that co-optimizes the power split and gear selection of a hybrid electric vehicle (HEV). We formulate a mixed-integer optimal control problem (MIOCP) that is transcribed using multiple-shooting into a mixed-integer nonlinear program (MINLP) and then solved by nonlinear model predictive control. We present two different numerical strategies, a Selective Relaxation Approach (SRA), which decomposes the MINLP into several subproblems, and a Round-n-Search Approach (RSA), which is an enhancement of the known ‘relax-n-round’ strategy. Subsequently, the resulting algorithmic performance and optimality of the solution of the proposed strategies are analyzed against two benchmark strategies; one using rule-based gear selection, which is typically used in production vehicles, and the other using dynamic programming (DP), which provides a global optimum of a quantized version of the MINLP. The results show that both SRA and RSA enable about\ua03.6%\ua0cost reduction compared to the rule-based strategy, while still being within\ua01%\ua0of the DP solution. Moreover, for the case studied RSA takes about\ua035%\ua0less mean computation time compared to SRA, while both SRA and RSA being about\ua099\ua0times faster than DP. Furthermore, both SRA and RSA were able to overcome the infeasibilities encountered by a typical rounding strategy under different drive cycles. The results show the computational benefit of the proposed strategies, as well as the energy saving possibility of co-optimization strategies in which actuator dynamics are explicitly included

Energy management in plug-in hybrid electric vehicles: recent progress and a connected vehicles perspective

Plug-in hybrid electric vehicles (PHEVs) offer an immediate solution for emissions reduction and fuel displacement within the current infrastructure. Targeting PHEV powertrain optimization, a plethora of energy management strategies (EMSs) have been proposed. Although these algorithms present various levels of complexity and accuracy, they find a limitation in terms of availability of future trip information, which generally prevents exploitation of the full PHEV potential in real-life cycles. This paper presents a comprehensive analysis of EMS evolution toward blended mode (BM) and optimal control, providing a thorough survey of the latest progress in optimization-based algorithms. This is performed in the context of connected vehicles and highlights certain contributions that intelligent transportation systems (ITSs), traffic information, and cloud computing can provide to enhance PHEV energy management. The study is culminated with an analysis of future trends in terms of optimization algorithm development, optimization criteria, PHEV integration in the smart grid, and vehicles as part of the fleet

Optimal Supervisory Control of Hybrid Vehicles

Hybrid vehicles (HV), comprising a conventional ICE-based powertrain and a secondary energy source, to be converted into mechanical power as well, represent a well-established alternative to substantially reduce both fuel consumption and tailpipe emissions of passenger cars. Several HV architectures are either being studied or already available on market, e.g. Mechanical, Electric, Hydraulic and Pneumatic Hybrid Vehicles. Among the others, Electric (HEV) and Mechanical (HSF-HV) parallel Hybrid configurations are examined throughout this Thesis. To fully exploit the HVs potential, an optimal choice of the hybrid components to be installed must be properly designed, while an effective Supervisory Control must be adopted to coordinate the way the different power sources are managed and how they interact. Real-time controllers can be derived starting from the obtained optimal benchmark results. However, the application of these powerful instruments require a simplified and yet reliable and accurate model of the hybrid vehicle system. This can be a complex task, especially when the complexity of the system grows, i.e. a HSF-HV system assessed in this Thesis. The first task of the following dissertation is to establish the optimal modeling approach for an innovative and promising mechanical hybrid vehicle architecture. It will be shown how the chosen modeling paradigm can affect the goodness and the amount of computational effort of the solution, using an optimization technique based on Dynamic Programming. The second goal concerns the control of pollutant emissions in a parallel Diesel-HEV. The emissions level obtained under real world driving conditions is substantially higher than the usual result obtained in a homologation cycle. For this reason, an on-line control strategy capable of guaranteeing the respect of the desired emissions level, while minimizing fuel consumption and avoiding excessive battery depletion is the target of the corresponding section of the Thesis

Integrated Modeling and Hardware-in-the-Loop Study for Systematic Evaluation of Hydraulic Hybrid Propulsion Options.

The fuel economy benefits of any given hybrid technology depend greatly on the vehicle type, size, supervisory control and driving schedule. The main goal of this work is to develop a comprehensive methodology for up-front strategic assessments of the best hybrid system for a given vehicle platform, and to explore the impact of vehicle driving schedules on the final decision. Several other objectives enabled achieving the main goal, including modeling, optimization of design and power management of several hydraulic hybrid systems developed for a 4x4 light truck. The parallel, series and power-split hybrid configurations are modeled and analyzed. The unique issues related to matching of components and interactions in the system with a high-power density of pump/motors and the energy storage (accumulator), but relatively low energy density of the storage and limited motor speed range are investigated. The design optimization is carried out to maximize the fuel economy while satisfying vehicle performance constraints. An Engine-in-the-Loop capability is developed for each of the hybrid architectures, integration issues are resolved and the EIL is subsequently used for validation of simulation predictions and studies of the impact of hybrid system configuration and control on diesel emissions. For the power management optimization, the deterministic dynamic programming technique provides the fuel economy benchmark. Stochastic dynamic programming technique is explored next, in order to develop an implementable sub-optimal supervisory control policy based on the vehicle power demand probability distribution sampled from various driving schedules. The simulation results obtained over the wide range of driving schedules from aggressive city cycles to mild highway cycles provided fuel economy trends and comparison of hybrid propulsion options. Fuel economy improvements of ~80% (up to 150% with engine shutdowns) are shown for aggressive city-cycles, while the gains diminish for high-speed highway driving. Verification of the emission reduction potential is enabled by synergistic experiments using a newly developed engine-in-the-loop capability. The results provide insight into the effects of the hybrid power management on transient emissions of soot and nitric oxides from a diesel, and provide guidance for the development of strategies for achieving both clean and efficient hybrid propulsion.Ph.D.Mechanical EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/58396/1/dadnsoo_1.pd

Comparison on Energy Economy and Vibration Characteristics of Electric and Hydraulic in-Wheel Drive Vehicles

This paper compares the energy economy and vertical vibration characteristics of in-wheel drive electric vehicles (IEVs), in-wheel drive electric hydraulic hybrid vehicles (IHVs) and centralized drive electric vehicles (CEVs). The dynamic programming (DP) algorithm is used to explore the optimal energy consumption of each vehicle. The energy economy analysis shows that the IEV consumes more energy than the CEV due to its relatively lower electric motor efficiency, even with fewer driveline components. The IHV consumes much more energy than the IEV and CEV because of the energy loss in the hydraulic driveline. The vertical vibration analysis demonstrates that both IEV and IHV degrade the vehicle driving comfort due to increased unsprung mass. Taking the advantage of high power density of the hydraulic motor, IHV have less unsprung mass when compared with the IEV, which helps to mitigate the vibration problems caused by increased unsprung mas