Increasing the Fuel Economy of Connected and Autonomous Lithium-Ion Electrified Vehicles

Asher, Zachary D.; Trinko, David; Bradley, Thomas H.

doi:10.1007/978-3-319-69950-9_6

Cited by 13 publications

(4 citation statements)

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“…The implementation of the fleet management system and properly presented results (the fluctuation of critical values) have a direct positive effect; stabilizing fuel consumption at lower levels [11].…”

Section: Fuel Consumption In Transport Companymentioning

confidence: 94%

“…Wang Q. et al [10] discuss ways of producing energy, with the aim of the accurate thermal analysis of batteries. Maximum profits can be achieved by monitoring systems [11] and managing resources for optimal utilization. Energy efficiency models have proven to play an important role in reducing transport costs and negative environmental impact.…”

Section: Introductionmentioning

confidence: 99%

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Managing Fuel Consumption and Emissions in the Renewed Fleet of a Transport Company

et al. 2020

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This research shows the relationship between the energy emission parameters and CO2 equivalents for conventional fossil fuel-powered vehicles (ICEV, Internal Combustion Engine Vehicles) and hybrid electric vehicles (HEV) to determine the life cycle costs of the vehicles. The combination of transport policy and alternative fuels has the purpose of creating a sustainable transport system. Transport policy focuses on increasing energy efficiency and reducing the price of electric vehicles as technology advances. The profitability for each vehicle type was also observed through current vehicle purchase prices. The main objective of this paper is to study the environmental impact of diesel vans, taking into account lifelong energy use, fuel consumption and CO2 equivalents through air pollution. Although the purchase price of the ICEV is less than the HEV, all electric vehicles are determined to have the lowest overall environmental impact during the operational phase. The goal of transport companies and logistics operators that own a fleet is to achieve quality service with maximum cost and negative environmental impact reduction.

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Section: Fuel Consumption In Transport Companymentioning

confidence: 94%

Section: Introductionmentioning

confidence: 99%

Managing Fuel Consumption and Emissions in the Renewed Fleet of a Transport Company

et al. 2020

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“…For a fixed route, this can be accomplished through driver feedback to promote efficient driving habits (Eco-driving) [18,19] as well as through optimal powertrain control to improve the powertrain efficiency [20]. Recent research suggests that the highest FE improvements are possible when Eco-driving is combined with optimal powertrain control [21]. The research in this paper is focused on improving FE through optimal powertrain control, which is typically discussed as an Optimal Energy Management Strategy (Optimal EMS).…”

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confidence: 99%

Enabling Prediction for Optimal Fuel Economy Vehicle Control

Asher

Tunnell

Baker

et al. 2018

SAE Technical Paper Series

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V ehicle control using prediction based optimal energy management has been demonstrated to achieve better fuel economy resulting in economic, environmental, and societal benefits. However, research focusing on prediction derivation for use in optimal energy management is limited despite the existence of hundreds of optimal energy management research papers published in the last decade. In this work, multiple data sources are used as inputs to derive a prediction for use in optimal energy management. Data sources include previous drive cycle information, current vehicle state, the global positioning system, travel time data, and an advanced driver assistance system (ADAS) that can identify vehicles, signs, and traffic lights. To derive the prediction, the data inputs are used in a nonlinear autoregressive artificial neural network with external inputs (NARX). Two real world drive cycles were developed for analysis in the Denver, Colorado region: a city-focused drive cycle that passes through downtown as well as a highway-focused drive cycle that transitions across multiple interstates. A validated model of a 2010 Toyota Prius in Autonomie is used to determine the vehicle control fuel economy improvements that are possible from the NARX prediction. The optimal energy management control strategy is determined using dynamic programming due to its ease of use and that the solution produced is the globally optimal solution. The control strategies compared include the existing 2010 Toyota Prius control strategy as a baseline, the neural network prediction optimal energy management control strategy, and a 100% accurate prediction optimal energy management control strategy. Results show that inclusion of various sensors and signals enables a significant amount of the fuel economy improvement with respect to 100% accurate prediction. The conclusion is that prediction based optimal energy management enabled fuel economy improvements can be realized with currently available sensors and signals.

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“…For a known Optimal EMS, recent research demonstrated real-world results by including various aspects of physical vehicles [9,10,11,12,13,14]. Note that research has also begun to show that the largest FE improvements are possible when eco-driving and an Optimal EMS are combined [15]. But, typically these FE improvement strategies require significant prediction of future events in the drive cycle, which is not straightforward.…”

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confidence: 99%

Towards Improving Vehicle Fuel Economy with ADAS

Tunnell

Asher

Pasricha

et al. 2018

SAE Technical Paper Series

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Modern vehicles have incorporated numerous safety-focused advanced driver-assistance systems (ADAS) in the last decade including smart cruise control and object avoidance. In this article, we aim to go beyond using ADAS for safety and propose to use ADAS technology to enable predictive optimal energy management and improve vehicle fuel economy (FE). We combine ADAS sensor data with a previously developed prediction model, dynamic programming (DP) optimal energy management control, and a validated model of a 2010 Toyota Prius to explore FE. First, a unique ADAS detection scope is defined based on optimal vehicle control prediction aspects demonstrated to be relevant from the literature. Next, during real-world city and highway drive cycles in Denver, Colorado, a camera is used to record video footage of the vehicle environment and define ADAS detection ground truth. Then, various ADAS algorithms are combined, modified, and compared to the ground truth results. Lastly, the impact of four vehicle control strategies on FE is evaluated: (1) the existing vehicle control, (2) actual ADAS detection for prediction and optimal energy management (we consider two variants ADAS1 and ADAS2 for this strategy), (3) ground truth ADAS detection for prediction and optimal energy management, and (4) 100% accurate prediction and optimal energy management. Results show that the defined ADAS scope and algorithms provide close correlation with ADAS ground truth and can enable FE improvements as part of a prediction-based optimal energy management strategy (EMS). Our proposed approach can leverage existing ADAS technology in modern vehicles to realize prediction-based optimal energy management, thus obtaining FE improvements with minor modifications.

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Increasing the Fuel Economy of Connected and Autonomous Lithium-Ion Electrified Vehicles

Cited by 13 publications

References 50 publications

Managing Fuel Consumption and Emissions in the Renewed Fleet of a Transport Company

Managing Fuel Consumption and Emissions in the Renewed Fleet of a Transport Company

Enabling Prediction for Optimal Fuel Economy Vehicle Control

Towards Improving Vehicle Fuel Economy with ADAS

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