Theo Theo Hofman

Review of optimal design strategies for hybrid electric vehicles

In this last decade, the industry headed in multiple transportation sectors towards hybridization and electrification of powertrains. This trend can be particularly observed in the automotive industry (passenger vehicles, commercial and construction vehicles), as well as, in water and air transportation systems. This change was a clear result of multiple environmental or market driven objectives as high fuel economy, pollution or limited resources. The electrification of transportation has brought an increase in the design complexity of the powertrain; and, in the same time a challenge for the research institutes and original equipment manufactures (OEMs). Multiple hybrid electric architectures have been developed under a continuous struggle to find the best solution with respect to various objectives and constraints. To find the optimal design of, for example, a hybrid electric vehicle (HEV), is a complex optimization problem that can be addressed through various methods. Prior to the choice of a suitable algorithm for the optimization of this design problem, there is a need of in-depth understanding of the current state of knowledge in architecture choices and optimization algorithms. This paper presents an overview of the existing approaches and algorithms used for optimal design of hybrid electric vehicles (HEV). It also includes an introduction in various hybrid topologies and examples from different transportation sectors.

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.

Review of Optimization Strategies for System-Level Design in Hybrid Electric Vehicles

The optimal design of a hybrid electric vehicle (HEV) can be formulated as a multiobjective optimization problem that spreads over multiple levels (technology, topology, size, and control). In the last decade, studies have shown that by integrating these optimization levels, fuel benefits are obtained, which go beyond the results achieved with solely optimal control for a given topology. Due to the large number of variables for optimization, their diversity, and the nonlinear and multiobjective nature of the problem, a variety of methodologies have been developed. This paper presents a comprehensive analysis of the various methodologies developed and identifies challenges for future research. Starting from a general description of the problem, with examples found in the literature, we categorize the types of optimization problems and methods used. To offer a complete analysis, we broaden the scope of the search to several sectors of transport, such as naval or ground.

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.

Contrôle optimal d’échauffement du moteur dans les véhicules hybrides

An Internal Combustion Engine (ICE) under cold conditions experiences increased friction losses due to a high viscosity of the lubricant. With the additional control freedom present in hybrid electric vehicles, the losses during warmup can be minimized and fuel can be saved. In this paper, firstly, a control-oriented model of the ICE, describing the warmup behavior, is developed and validated on measured vehicle data. Secondly, the two-state, non-autonomous fuel optimization, for a parallel hybrid electric vehicle with stop-start functionality, is solved using optimal control theory. The principal behavior of the Lagrange multipliers is explicitly derived, including the discontinuities (jumps) that are caused by the constraints on the lubricant temperature and the energy in the battery system. The minimization of the Hamiltonian for this two-state problem is also explicitly solved, resulting in a computationally efficient algorithm. The optimal controller shows the fuel benefit, as a function of the initial temperature, for a long-haul truck simulated on the FTP-75. Résumé— Contrôle optimal d’échauffement du moteur dans les véhicules hybrides — Un moteur à combustion interne (ICE, Internal Combustion Engine) en condition froide provoque une croissance de pertes de frottement suite à la viscosité élevée du lubrifiant. Dues à la commande libre supplémentaire appliquée dans les véhicules électriques hybrides, les pertes d’échauffement peuvent être réduites au maximum tout en économisant du carburant. Dans cet article, premièrement, un modèle de contrôle de l’ICE, décrivant le comportement d’échauffement, a été développé et validé par les données mesurées sur le moteur. Deuxièmement, dans un véhicule électrique hybride parallèle avec la fonctionnalité d’arrêt-démarrage, l’optimisation du double-état, non-autonome, du carburant est résolue en appliquant la théorie du contrôle optimal. Le comportement principal des multiplicateurs de Lagrange est explicitement dérivé, y compris les discontinuités (sauts) qui sont causées par les contraintes de la température du lubrifiant et par la capacité dans le système de batterie. La minimisation de l’Hamiltonien de ce problème de doubleétat est aussi explicitement résolue, résultant en un algorithme de calcul efficace. Le régulateur optimal indique l’économie du carburant en fonction de la température initiale, pour un grand routier simulé sur le FTP-75. Oil & Gas Science and Technology – Rev. IFP Energies nouvelles Copyright 2014, IFP Energies nouvelles DOI: 10.2516/ogst/2014042

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.

Functional and Cost-Based Automatic Generator for Hybrid Vehicles Topologies

The energy efficiency of a hybrid electric vehicle is dictated by the topology (coupling option of power sources/sinks), choice (technology), and control of components. The first design area among these, the topology, has the biggest flexibility of them all, yet, so far in the literature, the topology design is limited investigated due to its high complexity. In practice, a predefined small set of topologies is used to optimize their energy efficiency by varying the power specifications of the main components (sizing). By doing so, the complete design of the vehicle is, inherently and to a certain extent, suboptimal. Moreover, various complex topologies appear on the automotive market and no tool exists to optimally choose or evaluate them. To overcome this design limitation, in this paper, a novel framework is presented that deals with the automatic generation of possible topologies given a set of components (e.g., engine, electric machine, batteries, or transmission elements). This paper uses a platform (library of components) and a hybrid knowledge base (functional and cost-based principles) to set up a constraint logic programming problem, and outputs a set of feasible topologies for hybrid electric vehicles. These are all possible topologies that could be built considering a fixed, yet large, set of components. Then, by using these results, insights are given on what construction principles are mostly critical for simulations time, and what topologies could be selected as candidate topologies for sizing and control studies. Such a framework can be used for any powertrain application; it can offer the topologies to be investigated in the design phase and can provide insightful results for optimal design analyses.

Comparison of Bi-Level Optimization Frameworks for Sizing and Control of a Hybrid Electric Vehicle

This paper discusses the integrated design problem related to determining the power specifications of the main subsystems (sizing) and the supervisory control (energy management). Different bi-level optimization methods, with the outer loop using algorithms as Genetic Algorithms, Sequential Quadratic Programming, Particle Swarm Optimization or Pattern Search (DIRECT) and the inner loop using Dynamic Programming, are benchmarked to optimally size a parallel topology of a heavy duty vehicle. Since the sizing and control of a hybrid vehicle is inherently a mixed-integer multi-objective optimization problem, the Pareto analyses are also addressed. The results shows significant fuel reduction by hybridization and engine downsizing and offer insights in the usability of these nested optimization approaches.

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.