A Connected Controls and Optimization System for Vehicle Dynamics and Powertrain Operation on a Light-Duty Plug-In Multi-Mode Hybrid Electric Vehicle

Oncken, Joseph; Orlando, J.A.; Bhat, Pradeep; Narodzonek, Brandon; Morgan, Christopher; Robinette, Darrell; Chen, Bin; Naber, Jeffrey

doi:10.4271/2020-01-0591

Cited by 10 publications

(5 citation statements)

References 16 publications

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“…The distance profile based on average deceleration is calculated as described in Equation (19). The velocity profile based on the deceleration computed as per Equation ( 15) is presented in Equation ( 16):…”

Section: Case 2: Eco-stopping Algorithmmentioning

confidence: 99%

“…Velocity profile generation for the real-time predictive control of a hybrid powertrain was discussed in [18]. An MPC-based energy management framework assuming a cloud-based traffic information distribution can be found in article [19]. Article [20] shares the framework and experimental results of leveraging smart connectivity and intelligence to achieve energy savings.…”

Section: Introduction and Literature Reviewmentioning

confidence: 99%

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Real-Time Suitable Predictive Control Using SPaT Information from Automated Traffic Lights

Bhat

Chen

2023

Future Transportation

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Traffic intersections throughout the United States combine fixed, semi-actuated, and fully actuated intersections. In the case of the semi-actuated and actuated intersections, uncertainties are considered in phase duration. These uncertainties are due to car waiting queues and pedestrian crossing. Intelligent transportation systems deployed in traffic infrastructure can communicate Signal and Phase Timing messages (SPaT) to vehicles approaching intersections. In the connected and automated vehicle ecosystem, the fuel savings potential has been explored. Prior studies have predominantly focused on fixed time control for the driver. However, in the case of actuated signals, there is a different and significant challenge due to the randomness caused by uncertainties. We have developed a predictive control using the SPaT information communicated from the actuated traffic intersections. The developed MPC-based algorithm was validated using model-based design platforms such as AMBER®, Autonomie®, MATLAB®, and SIMULINK®. It was observed that the proposed algorithm can save energy in a single phase, in multiple phase scenarios, and in compelled stopping at stop signs when employed considering communications.

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Section: Case 2: Eco-stopping Algorithmmentioning

confidence: 99%

Section: Introduction and Literature Reviewmentioning

confidence: 99%

Real-Time Suitable Predictive Control Using SPaT Information from Automated Traffic Lights

Bhat

Chen

2023

Future Transportation

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“…Equation (4 in [56][57][58]. The result of this process will be a set of cumulative costs 1: , , the minimum of which is the lowest path cost for the problem.…”

Section: Algorithm Overviewmentioning

confidence: 99%

“…As outlined in [55,56], the Optimal Mode Path Planning Algorithm and NMPC powertrain controller are the two MTU NEXTCAR-developed technologies that are designed to provide optimal powertrain control using a short, seconds length horizon prediction of future vehicle operating condition. While capable of being implemented without the other, these two technologies were designed to be implemented as one integrated predictive powertrain controller.…”

Section: Integrated Predictive Powertrain Controllermentioning

confidence: 99%

“…According to [3], this information can be utilized to make accurate predictions of future vehicle behavior that can serve as an input to real-time, in-vehicle predictive PHEV control. Using reliable forecasts obtained through CAV technology, the effectiveness of predictive energy management strategies can be improved when compared to estimated forecasts developed using historical data as is done in [23,38] [56]. In addition to the powertrain controller presented in this paper, components of this system, which is outlined in Figure 3…”

Section: Introductionmentioning

confidence: 99%

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Real-Time Predictive Control of Connected Vehicle Powertrains for Improved Energy Efficiency

Oncken¹

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MultiCruise: Eco-Lane Selection Strategy with Eco-Cruise Control for Connected and Automated Vehicles

Aoki

Jan

Zhao³

et al. 2021

2021 IEEE Intelligent Vehicles Symposium (IV)

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A Connected Controls and Optimization System for Vehicle Dynamics and Powertrain Operation on a Light-Duty Plug-In Multi-Mode Hybrid Electric Vehicle

Cited by 10 publications

References 16 publications

Real-Time Suitable Predictive Control Using SPaT Information from Automated Traffic Lights

Real-Time Suitable Predictive Control Using SPaT Information from Automated Traffic Lights

Real-Time Predictive Control of Connected Vehicle Powertrains for Improved Energy Efficiency

MultiCruise: Eco-Lane Selection Strategy with Eco-Cruise Control for Connected and Automated Vehicles

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