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

Michigan Technological University-Darrell Robinette, Eric Kostreva, Alexandra Krisztian, Anthony Lackey, Christopher Morgan, Joshua Orlando, Neeraj Rama
Published 2019-04-02 by SAE International in United States
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 (CAV) control. The investigation began by establishing baseline and repeatability of energy consumption at various battery states of charge. It was determined that drive cycle energy consumption under a randomized set of boundary conditions varied within 3.6% of mean energy consumption regardless of initial battery state of charge. After completing 30 baseline drive cycles, a design for six sigma (DFSS) L18 array was designed to look at sensitivity of a range of parameters to energy consumption as related to connected and automated vehicles to target highest return on engineering development effort. The parameters explored in the DFSS array that showed the most sensitivity, in order of…
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Route-Optimized Energy Management of Connected and Automated Multi-Mode Plug-In Hybrid Electric Vehicle Using Dynamic Programming

Michigan Technological University-Neeraj Rama, Huanqing Wang, Joshua Orlando, Darrell Robinette, Bo Chen
Published 2019-04-02 by SAE International in United States
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 completely driven in all-electric mode. 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 used is dynamic programming (DP) paired with a reduced-order powertrain model to enable onboard embedded controller compatibility and computational efficiency in optimally blending CD, CS modes over the entire drive route. The objective of the optimizer is to enable future Connected and Automated Vehicles (CAVs) to best utilize onboard energy for minimum overall energy consumption based on speed and elevation profile information from Intelligent Transportation Systems (ITS), Internet of Things (IoT), High-definition Mapping, and onboard sensing technologies. Emphasis is placed on runtime minimization to quickly react and plan an optimal mode scheme in highly dynamic road conditions with minimal computational resources. On-road performance of the…
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Computationally Efficient Reduced-Order Powertrain Model of a Multi-Mode Plug-In Hybrid Electric Vehicle for Connected and Automated Vehicles

Michigan Technological University-Neeraj Rama, Darrell Robinette
Published 2019-04-02 by SAE International in United States
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 using only vehicle speed profile and route elevation as inputs. Such a model is intended to overcome the computational inefficiencies of higher fidelity powertrain and vehicle models in short and long horizon energy optimization efforts such as Coordinated Adaptive Cruise Control (CACC), Eco Approach and Departure (EcoAND), 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 CAVs without a need for computationally expensive server-based models. Performance of the model is evaluated on a fleet of second-generation Chevrolet Volts in a variety of driving scenarios and drive cycle durations. On-road testing indicates that the model can estimate actual vehicle…
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