A Mobility Performance Assessment on Plug-in EV Battery

This paper deals with mobility prediction of LiFeMnPO4 batteries for an emission-free Electric Vehicle. The data-driven model has been developed based on empirical data from two different road types –highway and local streets –and two different driving modes – aggressive and moderate. Battery State of Charge (SoC) can be predicted on any new roads based on the trained model by selecting the drving mode. In this paper, the performance of Adaptive Recurrent Neural Network (ARNN) and regression is evaluated using two benchmark data sets. The ARNN model at first estimates the speed profile of the new road based on slope and then both slope and speed is going to be used as the input to estimate battery current and SoC. Through comparison it is found that if ARNN system is appropriately trained, it performs with better accuracy than Regression in both two road types and driving modes. The results show that prediction SoC model follows the Columb-counting SoC according to the road slope.

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Diagnostics and Prognostics of Lithium-Ion Batteries

Volume 2A: 41st Design Automation Conference ◽

10.1115/detc2015-46935 ◽

2015 ◽

Author(s):

Zhimin Xi ◽

Rong Jing ◽

Cheol Lee

Keyword(s):

Lithium Ion Batteries ◽

Lithium Ion ◽

Remaining Useful Life ◽

State Of Charge ◽

Data Driven ◽

State Of Health ◽

Li Ion Batteries ◽

Battery State Of Charge ◽

Li Ion ◽

Useful Life

This paper investigates recent research on battery diagnostics and prognostics especially for Lithium-ion (Li-ion) batteries. Battery diagnostics focuses on battery models and diagnosis algorithms for battery state of charge (SOC) and state of health (SOH) estimation. Battery prognostics elaborates data-driven prognosis algorithms for predicting the remaining useful life (RUL) of battery SOC and SOH. Readers will learn not only basics but also very recent research developments on battery diagnostics and prognostics.

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Data driven estimation of electric vehicle battery state-of-charge informed by automotive simulations and multi-physics modeling

Journal of Power Sources ◽

10.1016/j.jpowsour.2020.229108 ◽

2021 ◽

Vol 483 ◽

pp. 229108

Author(s):

Marco Ragone ◽

Vitaliy Yurkiv ◽

Ajaykrishna Ramasubramanian ◽

Babak Kashir ◽

Farzad Mashayek

Keyword(s):

Electric Vehicle ◽

State Of Charge ◽

Data Driven ◽

Battery State Of Charge ◽

Electric Vehicle Battery ◽

Physics Modeling

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Agricultural Robotics

Annual Review of Control Robotics and Autonomous Systems ◽

10.1146/annurev-control-053018-023617 ◽

2019 ◽

Vol 2 (1) ◽

pp. 365-392 ◽

Cited By ~ 12

Author(s):

Stavros G. Vougioukas

Keyword(s):

Data Driven ◽

Farm Labor ◽

Data Sets ◽

Future Directions ◽

Agricultural Robots ◽

Benchmark Data ◽

Labor Inputs ◽

Sustainable Fashion ◽

And Performance ◽

Physical And Chemical

A key goal of contemporary agriculture is to dramatically increase production of food, feed, fiber, and biofuel products in a sustainable fashion, facing the pressure of diminishing farm labor supply. Agricultural robots can accelerate plant breeding and advance data-driven precision farming with significantly reduced labor inputs by providing task-appropriate sensing and actuation at fine spatiotemporal resolutions. This article highlights the distinctive challenges imposed on ground robots by agricultural environments, which are characterized by wide variations in environmental conditions, diversity and complexity of plant canopy structures, and intraspecies biological variation of physical and chemical characteristics and responses to the environment. Existing approaches to address these challenges are presented, along with their limitations; possible future directions are also discussed. Two key observations are that biology (breeding) and horticultural practices can reduce variabilities at the source and that publicly available benchmark data sets are needed to increase perception robustness and performance despite variability.

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