Alparslan Emrah Bayrak

DESIGN OF HYBRID-ELECTRIC VEHICLE ARCHITECTURES USING AUTO-GENERATION OF FEASIBLE DRIVING MODES

Several hybrid-electric vehicle architectures have been commercialized to serve different categories of vehicles and driving conditions. Such architectures can be optimally controlled by switching among driving modes, namely, the power distribution schemes in their planetary gear (PG) transmissions, in order to operate the vehicle in the most efficient regions of engine and motor maps. This paper proposes a systematic way to identify the optimal architecture for a given vehicle drive cycle, rather than parametrically optimizing one or more pre-selected architectures. An automatic generator of feasible driving modes for a given number of PGs is developed. For a powertrain consisting of one engine, two motors and two PGs, this generator results in 1116 modes. A heuristic search is then proposed to find a near-optimal pair of modes for a given driving cycle and vehicle specification. In a study this process identifies a dual-mode architecture with an 8% improvement in fuel economy compared to a commercially available architecture over a standard drive cycle.

EcoRacer: Game-Based Optimal Electric Vehicle Design and Driver Control Using Human Players

We investigate the cost and benefit of crowdsourcing solutions to an NP-complete powertrain design and control problem. Specifically, we cast this optimization problem as an online competition, and received 2391 game plays by 124 anonymous players during the first week from the launch. We compare the performance of human players against that of the Efficient Global Optimization (EGO) algorithm. We show that while only a small portion of human players can outperform the algorithm in long term, players tend to formulate good heuristics early on, from where good solutions can be extracted and used to constrain the solution space. Incorporating this constraint into the search enhances the efficiency of the algorithm, even for problem settings different from the game. These findings indicate that human computation is promising in solving comprehensible and computationally hard optimal design and control problems.

Electric Vehicle Design Optimization: Integration of a High-Fidelity Interior-Permanent-Magnet Motor Model

The simulation-based design optimization of an electric-vehicle (EV) propulsion system requires integration of a system model with detailed models of the components. In particular, a high-fidelity interior-permanent-magnet (IPM) motor model is necessary to capture important physical effects, such as magnetic saturation. The system optimization challenge is to maintain adequate model fidelity with acceptable computational cost. This paper proposes a design method that incorporates a high-fidelity motor, high-voltage power electronics, and vehicle propulsion simulation models in a system design optimization formulation that maximizes energy efficiency of a compact EV on a given drive cycle. The resulting optimal design and associated energy efficiency for a variety of drive cycles and performance requirements are presented and discussed.

Decomposition-Based Design Optimization of Hybrid Electric Powertrain Architectures: Simultaneous Configuration and Sizing Design

Effective electrification of automotive vehicles requires designing the powertrain’s configuration along with sizing its components for a particular vehicle type. Employing planetary gear systems in hybrid electric vehicle powertrain architectures allows various architecture alternatives to be explored, including singlemode architectures that are based on a fixed configuration and multi-mode architectures that allow switching power flow configuration during vehicle operation. Previous studies have addressed the configuration and sizing problems separately. However, the two problems are coupled and must be optimized together to achieve system optimality. An all-in-one system solution approach to the combined problem is not viable due to the high complexity of the resulting optimization problem. In this paper we propose a partitioning and coordination strategy based on Analytical Target Cascading for simultaneous design of powertrain configuration and sizing for given vehicle applications. The capability of the proposed design framework is demonstrated by designing powertrains with one and two planetary gears for a mid-size passenger vehicle.

Optimal dual-mode hybrid electric vehicle powertrain architecture design for a variety of loading scenarios

A hybrid-electric vehicle powertrain architecture consists of single or multiple driving modes, i.e., connection arrangements among engine, motors and vehicle output shaft that determine distribution of power. While most architecture development work to date has focused primarily on passenger cars, interest has been growing in exploring architectures for special-purpose vehicles such as vans or trucks for civilian and military applications, whose weights or payloads can vary significantly during operations. Previous findings show that the optimal architecture can be sensitive to vehicle weight. In this paper we investigate architecture design under a distribution of vehicle weights, using a simulation-based design optimization strategy with nested supervisory optimal control and accounting for powertrain complexity. Results show that an architecture under a single load has significant differences and lower fuel efficiency than an architecture designed to work under a variety of loading scenarios.

Topology Generation for Hybrid Electric Vehicle Architecture Design

Existing hybrid powertrain architectures, i.e., the connections from engine and motors to the vehicle output shaft, are designed for particular vehicle applications, e.g., passenger cars or city buses, to achieve good fuel economy. For effective electrification of new applications (e.g., heavy-duty trucks or racing cars), new architectures may need to be identified to accommodate the particular vehicle specifications and drive cycles. The exploration of feasible architectures is combinatorial in nature and is conventionally based on human intuition. We propose a mathematically rigorous algorithm to enumerate all feasible powertrain architectures, therefore enabling automated optimal powertrain design. The proposed method is general enough to account for single and multimode architectures as well as different number of planetary gears (PGs) and powertrain components. We demonstrate through case studies that our method can generate the complete sets of feasible designs, including the ones available in the market and in patents. [DOI: 10.1115/1.4033656]

A computational concept generation method for a modular vehicle fleet design

Modularity for ground vehicle systems has been viewed as a potential solution for the military to meet a variety of changing mission demands without keeping a large inventory of vehicles in a fleet. Defining the module concepts is a significant challenge that impacts the effectiveness of the modularity solution. In this paper, we propose a functional synthesis method to design a set of modules comprising a modular ground vehicle fleet according to overall fleet-level objectives. We start with a functional decomposition of a baseline conventional fleet capable of performing a given fleet mission. We then formulate a functional synthesis problem to draw the boundaries that define modules for a modular fleet having the same mission capability with maximum fleet performance defined by average weight of the fleet performing the required operations and personnel time to reconfigure and maintain the fleet. An example problem is included to illustrate the approach. Results show a trade-off between two fleet-level objectives with respect to the degree of modularity.

An integrated design approach for evaluating the effectiveness and cost of a fleet

This work presents a new method for designing and evaluating different fleet paradigms to determine an effective and cost efficient solution. The method requires the user to define a set of functions which must be carried out by the fleet, as well as a set of candidate vehicles or systems that can carry out these functions. These function and fleet models are then evaluated to determine their performance. All the data is then fed into a stochastic dynamic fleet operation model to identify the amount of vehicles or systems needed to complete each mission defined on a fixed time horizon. The output of the fleet operation model is then used by cost models to determine the cost of completing each fleet mission. The overall approach is demonstrated on a military fleet composed of two different types of vehicle: a conventional fleet and a fleet composed of modules. The method shows the potential for savings using a modular fleet for a hypothetical fleet mission profile; more work in this area is suggested.

Decomposition-based design optimization of hybrid electric powertrain architectures: Simultaneous configuration and sizing design

Effective electrification of automotive vehicles requires designing the powertrain’s configuration along with sizing its components for a particular vehicle type. Employing planetary gear systems in hybrid electric vehicle powertrain architectures allows various architecture alternatives to be explored, including single-mode architectures that are based on a fixed configuration and multi-mode architectures that allow switching power flow configuration during vehicle operation. Previous studies have addressed the configuration and sizing problems separately. However, the two problems are coupled and must be optimized together to achieve system optimality. An all-in-one system solution approach to the combined problem is not viable due to the high complexity of the resulting optimization problem. In this paper we propose a partitioning and coordination strategy based on Analytical Target Cascading for simultaneous design of powertrain configuration and sizing for given vehicle applications. The capability of the proposed design framework is demonstrated by designing powertrains with one and two planetary gears for a mid-size passenger vehicle.Copyright

Robustness and Real Options for Vehicle Design and Investment Decisions Under Gas Price and Regulatory Uncertainties

Manufacturers must decide when to invest and launch a new vehicle segment or how to redesign vehicles existing segment under market uncertainties. We present an optimization framework for redesigning or investing in future vehicles using real options to address uncertainty in gas price and regulatory standards like the U.S. Corporate Average Fuel Economy (CAFE) standard. In a specific study involving a product of gasoline, hybrid electric, and electric vehicles (EV), we examine the relationship between gas price and CAFE uncertainties to support decisions by manufacturers on product mix and by policy makers on proposing standards. A real options model is used for the time delay on investment, redesign, and pricing, integrated with a robust design formulation to optimize expected net present value (ENPV) and net present value (NPV) robustness. Results for nine different scenarios suggest that policy makers should consider gas price when setting CAFE standards; and manufacturers should consider the trade-off between ENPV and robust NPVs. Results also suggest that change of product mix rather than vehicle redesign better addresses CAFE standards inflation. [DOI: 10.1115/1.4040629]