E Emilia Silvas

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.

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.

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.

Synthesis of Realistic Driving Cycles With High Accuracy and Computational Speed, Including Slope Information

This paper describes a new method to synthesize driving cycles, where not only the velocity is considered but the road slope information of the real-world measured driving cycle as well. Driven by strict emission regulations and tight fuel targets, hybrid or electric vehicle manufacturers aim to develop new and more energy- and cost-efficient powertrains. To enable and facilitate this development, short, yet realistic, driving cycles need to be synthesized. The developed driving cycle should give a good representation of measured driving cycles in terms of velocity, slope, acceleration, and so on. Current methods use only velocity and acceleration and assume a zero road slope. The heavier the vehicle is, the more important the road slope becomes in powertrain prototyping (as with component sizing or control design); hence, neglecting it leads to unrealistic or limited designs. To include the slope, we extend existing methods and propose an approach based on multidimensional Markov chains. The validation of the synthesized driving cycle is based on a statistical analysis (as the average acceleration or maximum velocity) and a frequency analysis. This new method demonstrates the ability of capturing the measured road slope information in the synthesized driving cycle. Furthermore, results show that the proposed method outperforms current methods in terms of accuracy and speed.

Design of Power Steering Systems for Heavy-Duty Long-Haul Vehicles

Conventionally, all auxiliaries present in a heavy-duty vehicle (e.g., power-steering pump, air-conditioning compressor) are engine-driven systems, which put high constraints on their performance. Outputs (e.g., speed, temperature) and energy consumption are dictated by engine speed, while most auxiliary demands are not proportional to the engine speed. Dealing with worst-case scenarios leads to highly oversized components that further, dramatically reduce the overall efficiency. How to choose, in a simultaneous design step, a topology, component sizes and a control algorithm for auxiliaries is still unknown. This becomes, especially, important when an integrated general optimal design is desired for the vehicle rather than an optimal system or sub-system design. To overcome the drawbacks of a sequential design approach, this paper shows the precise combination of technology, topology, size and control for the power steering system used in a heavy-duty vehicle. Modeling of six possible topologies and optimal sizing of components, as the gear ratio between combustion engine and power steering pump, are shown. Next, a sensitivity analysis is done for control parameters and a view is presented on a suitable topology for a power steering system used in a heavy-duty long-haul vehicle.

Modeling for Control and Optimal Design of a Power Steering Pump and an Air Conditioning Compressor Used in Heavy Duty Trucks

The hybridization and electrification of power-trains has brought increased flexibility and, therefore, new challenges, in the design of the hybrid vehicles. Beside the main components that are used for vehicles propulsion, important energy consumers are the auxiliaries such as the power steering pump or others. The influence of these components on the fuel consumption can be defined in terms of the topology, technology and control algorithm choices. This paper presents the influence on fuel consumption of two auxiliaries for two different topologies. For this purpose models are developed for the power steering pump and for the air conditioning compressor, and validated using experimental data from components used in long-haul heavy duty trucks. A case study on the control for the power steering pump is also presented. The results show a significant fuel reduction for each component (for the power steering pump approximately 50% fuel reduction and for the ACC approximately 40% fuel reduction).

Optimal Design of All-Wheel-Drive Hybrid Pick-Up Trucks

Both fuel economy and launching/towing performance are important for pick-up trucks. All-wheel-drive (AWD) and multimode operations are important to ensure efficient operations in a wide range of speeds and road loads. In this paper, a systematic methodology is developed and applied to the screening and evaluation of AWD multi-mode hybrid trucks. It is a four-step design process that uses automated modeling techniques, exhaustive search, and a near-optimal control strategy to obtain optimal designs. A case study was conducted to identify superior designs for an imagined hybrid electric Ford F-150 light truck.Copyright