K Koos van Berkel

Fast and Smooth Clutch Engagement Control for a Mechanical Hybrid Powertrain

Automatically controlled clutches are widely used in advanced automotive powertrains to transmit a demanded torque while synchronizing the rotational speeds of the shafts. The two objectives of the clutch engagement controller are a fast clutch engagement to reduce the frictional losses and thermal load, and a smooth clutch engagement to accurately track the demanded torque without a noticeable torque dip. Meanwhile, the controller is subjected to standard constraints such as model uncertainty and limited sensor information. This paper presents a new controller design that explicitly separates the control laws for each objective by introducing three clutch engagement phases. The time instants to switch between the subsequent phases are chosen such that the desired slip acceleration is achieved at the time of clutch engagement. The latter can be interpreted as a single calibration parameter that determines the tradeoff between fast and smooth clutch engagement. The controller is elaborated for a mechanical hybrid powertrain that uses a flywheel as a secondary power source and a continuously variable transmission. Simulations and experiments on a test rig show that the control objectives are realized with a robust and relatively simple controller.

Optimal Control of a Mechanical Hybrid Powertrain With Cold-Start Conditions

This paper investigates the impact of cold-start conditions on the fuel-saving potential and the associated optimal energy controller of a mechanical hybrid powertrain. The mechanical hybrid powertrain uses a flywheel system to add fuel-saving functionalities to a conventional powertrain, which consists of an internal combustion engine and a continuously variable transmission (CVT). The cold-start conditions refer to a low powertrain temperature, which increases the frictional power dissipation in the engine and transmission, and a stationary (or energyless) flywheel system, which must be energized to a minimum energy level before it can be effectively utilized. The heating of the powertrain and the initialization of the flywheel system can be influenced by the energy controller, which controls the power distribution between the engine, the flywheel, and the vehicle. The energy controller aims at minimizing the overall fuel consumption for a given driving cycle. The optimal energy controller is found analytically for a simplified model to gain qualitative insights in the controller and numerically using dynamic programming for a detailed model to quantify the impact on the fuel consumption. The results show that the cold-start conditions have a significant impact on the fuel-saving potential, yet a negligible impact on the optimal energy controller. The latter result implies that the temperature state can be eliminated from the state space of the energy controller, which is an important step toward the design of an effective yet simple energy controller suitable for real-time implementation.

From Optimal to Real-Time Control of a Mechanical Hybrid Powertrain

This brief presents the design of an energy controller for a mechanical hybrid powertrain, which is suitable for implementation in real-time hardware. The mechanical hybrid powertrain uses a compact flywheel module to add hybrid functionalities to a conventional powertrain that consists of an internal combustion engine and a continuously variable transmission. The control objective is to minimize the overall fuel consumption for a given driving cycle. The design approach follows a generic framework to: 1) solve the optimization problem using optimal control; 2) make the optimal controller causal using a prediction of the future driving conditions; and 3) make the causal controller robust by tuning of one key calibration parameter. The highly constrained optimization problem is solved with dynamic programming. The future driving conditions are predicted using a model that smoothly approximates statistical data, and implemented in the receding model predictive control framework. The controller is made tunable by rule extraction from the model predictive controller, based on physical understanding of the system. The resulting real-time controller is transparent, causal, and robust, where the latter is shown by simulations for various driving cycles and start conditions.

Implementation of Dynamic Programming for Optimal Control Problems With Continuous States

Dynamic programming is a numerical method to solve a dynamic optimal control problem. Due to its numerical framework, it is very suitable to describe discrete dynamics, nonlinear characteristics, and nonconvex constraints. The implementation of continuous states in the discrete framework, however, may lead to optimization inaccuracies. This brief addresses implementation methods with fundamentally different utilizations of the nodes in the quantized time-state space. A new implementation method is presented, which combines the advantages of numerical and analytical optimization techniques to substantially improve the optimization accuracy for a given quantization of the continuous state. If desired, the computation time can be substantially reduced for a given accuracy by lowering the quantization resolution. As a case study, the optimal energy controller is computed for a mechanical hybrid powertrain, which is characterized by switched dynamics, active state constraints, and nonconvex control constraints. Results show that the optimization accuracy of the new method is superior to that of the conventional method based on nearest neighbor rounding. For a given desired accuracy, the computation time is reduced by an order of magnitude.

Topology and Flywheel Size Optimization for Mechanical Hybrid Powertrains

Mechanical hybrid powertrains have the potential to improve the fuel economy of passenger vehicles at a relatively low cost, by adding a flywheel and only mechanical transmission components to a conventional powertrain. This paper presents a systematic approach to optimizing the topology and flywheel size, which are the key design parameters of a mechanical hybrid powertrain. The topology is optimized from a limited set of over 20 existing mechanical hybrid powertrains described in the literature. After a systematic classification of the topologies, a set of four competitive powertrains is selected for further investigation. The fuel-saving potential of each hybrid powertrain is computed using an optimal energy controller and modular component models, for various flywheel sizes and for three certified driving cycles. The hybridization cost is estimated based on the type and size of the components. Other criteria, such as control complexity, clutch wear, and driving comfort are qualitatively evaluated to put the fuel-saving potential and the hybridization cost into a wider perspective. Results show that, for each of the four investigated hybrid powertrains, the fuel-saving benefit returns the hybridization investment well within (about 50%) the service life of passenger vehicles. The optimal topology follows from a discussion that considers all the optimization criteria. The associated optimal flywheel size has an energy storage capacity that is approximately equivalent to the kinetic energy of the vehicle during urban driving (50 km/h).

Analysis of optimal mechanical-hybrid powertrain topologies

This study presents an overview of mechanical-hybrid vehicle concepts found in the literature, and compares the fuel saving potential and the estimated cost of a selection of four competitive powertrain topologies. To make a fair comparison, the fuel saving of each concept is computed using the same reference vehicle, a selection of the same set of components, the same driving cycles, and an optimal energy management strategy. The flywheel size is left as a design parameter. Dynamic programming is used to find the optimal energy management strategy, by minimizing the fuel consumption of a given hybrid powertrain model for a given driving cycle. The production cost is estimated using weight-specific parameters, so that components such as the flywheel size can be scaled. Results show that these competitive topologies represent different trade-offs between fuel saving, cost, and control complexity. In general, the investment of the additional mechanical-hybrid components is returned after approximately 50,000 km, which is makes it a very competitive technology.

Semi-empirical power dissipation modelling of mechanical hybrid powertrain components:

Accurate modelling is of key importance for the model-based design of controlled systems. The overall system complexity can be limited by using simple component models that represent only the main characteristics, where smooth characteristics are preferred to avoid unnecessary irregularities in the design optimization and in the controlled signals. This paper presents the design of such control-oriented models to describe the power dissipation in a mechanical hybrid powertrain. The two key powertrain components are the continuously variable transmission for mechanical power transmission and a flywheel system for kinetic energy storage. The power dissipation in these components is modelled by parametric functions, which are suitable for describing smooth characteristics in a relatively simple format with only a few coefficients. The functions are selected on the basis of the physical understanding of the systems, whereas the coefficients are identified from dedicated test rig experiments. The results show that the power dissipations are modelled very accurately for both the continuously variable transmission and the flywheel system, with a modelling error of less than 75 W for 80% of the operating conditions in a wide operating range between −25 kW and 38 kW. The continuously variable transmission model is also validated under dynamic driving conditions, showing an overall error for the transmission efficiency of less than 1%.

Performance simulations of a low-cost hybrid powertrain with large fuel savings

This paper presents a new design of a low-cost mechanicalhybrid powertrain with large fuel savings. The hybrid powertrain contains only low-cost mechanical components, such as a compact flywheel module and a Continuously Variable Transmission (CVT). No electrical motor/generator or battery is used. On the basis of the characteristics of typical driving cycles, the energy storage capacity of the flywheel module is derived accordingly. The fuel-saving potential of the new powertrain is simulated for a compact passenger vehicle, which represents the aimed vehicle segment in emerging markets. Simulations show that the fuel-saving potential, with respect to the same vehicle without flywheel module, ranges in between 15% and 29%, depending on the considered driving cycle.

Optimal energy management for a flywheel-based hybrid vehicle

This paper presents the modeling and design of an optimal Energy Management Strategy (EMS) for a flywheel-based hybrid vehicle, that does not use any electrical motor/generator, or a battery, for its hybrid functionalities. The hybrid drive train consists of only low-cost components, such as a flywheel module and a continuously variable transmission. This hybrid drive train is characterized by a relatively small energy capacity (flywheel) and discrete shifts between operation modes, due to the use of clutches. The main design criterion of the optimized EMS is the minimization of the overall fuel consumption, over a pre-defined driving cycle. In addition, comfort criteria are formulated as constraints, e.g., to avoid high-frequent shifting between driving modes. The criteria are used to find the optimal sequence of driving modes and the generated engine torque. Simulations show a fuel saving potential of 20% to 39%, dependent on the chosen driving cycle.

Reading of cracked optical discs using Iterative Learning Control

Optical discs, including Compact Discs (CDs), Digital Versatile Discs (DVDs), and Blu-ray Discs (BDs), can get cracked during storage and usage. Such cracks commonly lead to discontinuities in the data track, potentially preventing reading of the data on the disc. The aim of the present paper is to improve tracking performance of the optical disc drive in the presence of cracks. A Hankel Iterative Learning Control (ILC) algorithm is presented that can perfectly steer the lens during the crack towards the beginning of the track immediately after the crack, i.e., the actuator is steered appropriately during the crack crossing to compensate for the discontinuity in the data track. Experimental results confirm improved reading capabilities of cracked discs. The presented approach potentially enables the recovery of data from cracked discs that were previously considered as unreadable.