A Virtual Sensor for Electric Vehicles’ State of Charge Estimation

Gruosso, Giambattista; Gajani, G. Storti; Ruiz, Fredy; Valladolid, Juan D.; Patiño, Diego

doi:10.3390/electronics9020278

Cited by 19 publications

(10 citation statements)

References 26 publications

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“…The first model is trained using only the Starting SoC and the time of the day in which the charge is starting. The starting SOC, if properly evaluated by the Battery Management System (BMS) [14], [15] is a crucial feature in the prediction of the duration of the charge assuming that it is brought to completion. The time of the day gives the model information about the habits of the user.…”

Section: Data Processingmentioning

confidence: 99%

Electric Vehicles charging sessions classification technique for optimized battery charge based on machine learning

Matrone,

Ogliari,

Nespoli

et al. 2023

IEEE Access

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The fast increase in electric vehicle (EV) usage in the last 10 years has raised the need to properly forecast their energy consumption during charge. Lithium-ion batteries have become the major storage component for electric vehicles, avoiding their overcharge can preserve their health and prolong their lifetime. This paper proposes a Machine Learning model based on the K-Nearest Neighbors classification algorithm for EV charging session duration forecast. The model forecasts the duration of the charge by assigning the event to its correct class. Each class contains the charging events whose duration is comprised of a certain interval. The only information used by the algorithm is the one available at the beginning of the charging event (arrival time, starting SOC, calendar data). The model is validated on a real-world dataset containing records of charging sessions from more than 100 users, a sensitivity analysis is performed to assess the impact of different information given as input. The effectiveness of the model with respect to the benchmark models is demonstrated with an increase in performance.

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Section: Data Processingmentioning

confidence: 99%

Electric Vehicles charging sessions classification technique for optimized battery charge based on machine learning

Matrone,

Ogliari,

Nespoli

et al. 2023

IEEE Access

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“…where, a, b, and c are coefficients of the second-order polynomial found. Considering the equivalent circuit or the Thevenin model, which consists of an array of a Resistor-Capacitor (RC) network in series with a voltage source [29,[32][33][34], the proposal is replacing the RC circuit with a transfer function as shown in Figure 3. Where R refers to the internal resistance of the battery.…”

Section: Battery Modelmentioning

confidence: 99%

A Novel Energy-Efficiency Optimization Approach Based on Driving Patterns Styles and Experimental Tests for Electric Vehicles

et al. 2021

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This article proposes an energy-efficiency strategy based on the optimization of driving patterns for an electric vehicle (EV). The EV studied in this paper is a commercial vehicle only driven by a traction motor. The motor drives the front wheels indirectly through the differential drive. The electrical inverter model and the power-train efficiency are established by lookup tables determined by power tests in a dynamometric bank. The optimization problem is focused on maximizing energy-efficiency between the wheel power and battery pack, not only to maintain but also to improve its value by modifying the state of charge (SOC). The solution is found by means of a Particle Swarm Optimization (PSO) algorithm. The optimizer simulation results validate the increasing efficiency with the speed setpoint variations, and also show that the battery SOC is improved. The best results are obtained when the speed variation is between 5% and 6%.

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“…The key difference between the usage of first principals presented here follows. Classical methods impose the form of the estimation and control (typically negative feedback with gains) and they have very recently been applied to railway vehicles [ 21 ], biomechanical applications [ 22 ], and remotely operated undersea vehicles [ 23 ], electrical vehicles [ 24 ], and even residential heating energy consumption [ 25 ] and multiple access channel usage by wireless sensor networks [ 26 ]. Deterministic artificial intelligence uses first principals and optimization for all quantities but asserts a desired trajectory.…”

Section: Introductionmentioning

confidence: 99%

Virtual Sensoring of Motion Using Pontryagin’s Treatment of Hamiltonian Systems

Sands

2021

Sensors

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To aid the development of future unmanned naval vessels, this manuscript investigates algorithm options for combining physical (noisy) sensors and computational models to provide additional information about system states, inputs, and parameters emphasizing deterministic options rather than stochastic ones. The computational model is formulated using Pontryagin’s treatment of Hamiltonian systems resulting in optimal and near-optimal results dependent upon the algorithm option chosen. Feedback is proposed to re-initialize the initial values of a reformulated two-point boundary value problem rather than using state feedback to form errors that are corrected by tuned estimators. Four algorithm options are proposed with two optional branches, and all of these are compared to three manifestations of classical estimation methods including linear-quadratic optimal. Over ten-thousand simulations were run to evaluate each proposed method’s vulnerability to variations in plant parameters amidst typically noisy state and rate sensors. The proposed methods achieved 69–72% improved state estimation, 29–33% improved rate improvement, while simultaneously achieving mathematically minimal costs of utilization in guidance, navigation, and control decision criteria. The next stage of research is indicated throughout the manuscript: investigation of the proposed methods’ efficacy amidst unknown wave disturbances.

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A Virtual Sensor for Electric Vehicles’ State of Charge Estimation

Cited by 19 publications

References 26 publications

Electric Vehicles charging sessions classification technique for optimized battery charge based on machine learning

Electric Vehicles charging sessions classification technique for optimized battery charge based on machine learning

A Novel Energy-Efficiency Optimization Approach Based on Driving Patterns Styles and Experimental Tests for Electric Vehicles

Virtual Sensoring of Motion Using Pontryagin’s Treatment of Hamiltonian Systems

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