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Technical Paper

Torsional Vibration Analysis of Six Speed MT Transmission and Driveline from Road to Lab

When a manual transmission (MT) powertrain is subjected to high speeds and high torques, the vehicle driveshaft, and other components experience an increase in stored potential energy. When the engine and driveshaft are decoupled during an up or down shift, the potential energy is released causing clunk during the shift event. The customer desires a smooth shift thus reduction of clunk will improve experience and satisfaction. In this study, a six-speed MT, rear-wheel-drive (RWD) passenger vehicle was used to experimentally capture acoustic and vibration data during the clunk event. To replicate the in-situ results, additional data was collected and analyzed for powertrain component roll and pitch. A lumped parameter model of key powertrain components was created to replicate the clunk event and correlate with test data. The lumped parameter model was used to modify clutch tip-out parameters, which resulted in reduced prop shaft oscillations.
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

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.