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Technical Paper

PHEV Real World Driving Cycle and Energy and Fuel Consumption Reduction Potential for Connected and Automated Vehicles

This paper presents real world driving energy and fuel consumption results for the second-generation Chevrolet Volt plug-in hybrid electric vehicle (PHEV). A drive cycle, local to Michigan Technological University, was designed to mimic urban and highway driving test cycles in terms of distance, transients and average velocity, but with significant elevation changes to establish an energy intensive real world driving cycle for assessing potential energy savings for connected and automated vehicle control. The investigation began by establishing baseline and repeatability of energy consumption at various battery states of charges. It was determined that drive cycle energy consumption under a randomized set of boundary conditions varied within 3.4% of mean energy consumption regardless of initial battery state of charge.
Technical Paper

Computationally Efficient Reduced-order Powertrain Model of a Multi-mode Plug-in Hybrid Electric Vehicle for Connected and Automated Vehicles

This paper presents the development of a reduced-order powertrain model for energy and SOC estimation of a multi-mode plug-in hybrid electric vehicle with velocity and elevation inputs. Such a model is intended to overcome the computational inefficiencies of higher fidelity powertrain and vehicle models in short and long horizon optimization efforts such as Coordinated Adaptive Cruise Control (CACC), Eco Approach and Departure (Eco AND), Eco Routing, and PHEV mode blending. The reduced-order powertrain model enables Connected and Automated Vehicles (CAVs) to utilize the onboard sensor and connected data to quickly react and plan their maneuvers to highly dynamic road conditions with minimal computational resources. Although overall estimation accuracy is less than neural network and high-fidelity models, emphasis on runtime minimization with reasonable estimation accuracy enables energy optimization of CAV without a need for computationally expensive server-based models.
Technical Paper

Control Strategy and Energy Recovery Potential for P2 Parallel Hybrid Step Gear Automatic Transmissions

The purpose of this investigation is to present a control strategy and energy recovery potential for P2 parallel hybrid step gear automatic transmissions. The automatic transmission types considered for the investigation are rear wheel drive a dual clutch transmission and a 10 speed planetary automatic equipped each equipped with a significant electric motor between the engine and transmission. The governing equations of clutch-to-clutch upshift controls are presented for each transmission type and various strategies are explored for executing the upshift under various input torque levels, shift times and engine torque management approaches. A lumped parameter dynamic model including the P2 electrification system is utilized for each transmission type to represent dynamic behavior representative of that expected in vehicle for achieving various levels of shift quality assessed with vibration dose value.
Technical Paper

Route Optimized Energy Management of a Connected and Automated Multi-mode Hybrid Electric Vehicle using Dynamic Programming

This paper presents a methodology to optimize the blending of Charge Depleting (CD) and Charge Sustaining (CS) modes in a multi-mode plug-in hybrid electric vehicle (PHEV) that reduces overall energy consumption when the selected route cannot be drive purely electric. The PHEV used in this investigation is the second generation Chevrolet Volt and as many as four instrumented vehicles were utilized simultaneously on road to acquire validation data. The optimization method utilized is dynamic programming (DP) and is paired with a reduced fidelity propulsion system and vehicle dynamics model to enable compatibility with embedded controllers and be computationally efficient of the optimal blended operating scheme over an entire drive route.
Technical Paper

A Comparative Analysis for Optimal Control of Power Split in a Fuel Cell Hybrid Electric Vehicle

Power split in Fuel Cell Hybrid Electric Vehicles (FCHEVs) has been controlled using different strategies ranging from rule-based to optimal control. Dynamic Programming (DP) and Model Predictive Control (MPC) are two common optimal control strategies used in optimization of the power split in FCHEVs with a trade-off between global optimality of the solution and online implementation of the controller. In this paper, both control strategies are developed and tested on a FC/battery vehicle model, and the results are compared in terms of total energy consumption. In addition, the effects of the MPC prediction horizon length on the controller performance are studied. Results show that by using the DP strategy, up to 12% less total energy consumption is achieved compared to MPC for a charge sustaining mode in the Urban Dynamometer Driving Schedule (UDDS) drive cycle.
Technical Paper

A Comparison Between Power Injection and Impulse Response Decay Methods for Estimating Frequency Averaged Loss Factors for SEA

Damping measurements on vehicle subsystems are rarely straightforward due to the complexity of the dynamic interaction of system joints, trim, and geometry. Various experimental techniques can be used for damping estimation, such as frequency domain modal analysis curve-fitting methods, time domain decay-rate methods, and other methods based on energy and wave propagation. Each method has its own set of advantages and drawbacks. This paper describes an analytical and an experimental comparison between two, widely used loss factor estimation techniques frequently used in Statistical Energy Analysis (SEA). The single subsystem Power Injection Method (PIM) and the Impulse Response Decay Method (IRDM) were compared using analytical models of a variety of simulated simple spring-mass-damper systems. Frequency averaged loss factor values were estimated from both methods for comparison.