This paper establishes an accurate and reliable study for estimating the lithium-ion battery’s State of Charge (SoC). An accurate state space model is used to determine the parameters of the battery’s nonlinear model. African Vultures Optimizers (AVOA) are used to solve the issue of identifying the battery parameters to accurately estimate SoC. A hybrid approach consists of the Coulomb Counting Method (CCM) with an Adaptive Unscented Kalman Filter (AUKF) to estimate the SoC of the battery. At different temperatures, four approaches are applied to the battery, varying between including load and battery fading or not. Numerical simulations are applied to a 2.6 Ahr Panasonic Li-ion battery to demonstrate the hybrid method’s effectiveness for the State of Charge estimate. In comparison to existing hybrid approaches, the suggested method is very accurate. Compared to other strategies, the proposed hybrid method achieves the least error of different methods.

The mobility industry has experienced a fast evolution towards electric-based transport in recent years. Recently, hybrid electric vehicles, which combine electric and conventional combustion systems, have become the most popular alternative by far. This is due to longer autonomy and more extended refueling networks in comparison with the recharging points system, which is still quite limited in some countries. This paper aims to conduct a literature review on thermodynamic models of heat engines used in hybrid electric vehicles and their respective configurations for series, parallel and mixed powertrain. It will discuss the most important models of thermal energy in combustion engines such as the Otto, Atkinson and Miller cycles which are widely used in commercial hybrid electric vehicle models. In short, this work aims at serving as an illustrative but descriptive document, which may be valuable for multiple research and academic purposes.

En la presente investigación se explica la metodología para la creación de un sistema de diagnóstico aplicado a la detección de fallas mecánicas en vehículos con motores a gasolina mediante redes neuronales artificiales, el sistema se basa en el estudio de la fase de admisión del ciclo Otto, el cual es registrado a través de la implementación física de un sensor MAP (Manifold Absolute Pressure). Se emplea un estricto protocolo de muestreo y su correspondiente análisis estadístico. Los valores estadísticos de la señal del sensor MAP: área, energía, entropía, máximo, media, mínimo, potencia y RMS se seleccionaron en función al mayor aporte de información y diferencia significativa. Los datos se obtuvieron con la aplicación de 3 métodos estadísticos (ANOVA, matriz de correlación y Random Forest) para tener una base de datos que permita el entrenamiento de una red neuronal feed-forward backpropagation, con la cual se obtiene un error de clasificación de 1.89e-11. La validación del sistema de diagnóstico se llevó a cabo mediante la provocación de fallas supervisadas en diferentes motores de encendido provocado.

En el presente trabajo se explica la aplicación de RNA (redes neuronales artificiales) para la predicción de emisiones contaminantes generadas por fallas mecánicas en motores de encendido provocado, de la cual se puede cuantificar el porcentaje de CO (% monóxido de carbono) y el particulado por millón HC (ppm hidrocarburos sin quemar), a través del estudio de la fase de admisión del ciclo Otto, la cual es registrada por medio de la implementación física de un sensor MAP (Manifold Absolute Pressure). Se aplica un riguroso protocolo de muestreo y consecuente análisis estadístico. La selección y reducción de atributos de la señal del sensor MAP se realiza en función del mayor aporte de información y diferencia significativa con la aplicación de tres métodos estadísticos (ANOVA, matriz de correlación y Random Forest), de la cual se obtiene una base de datos que permite el entrenamiento de dos redes neuronales feed-forward backpropagation, con las cuales se obtiene un error de clasificación de 5.4061e-9 y de 9.7587e-5 para la red neuronal de CO y HC respectivamente.

This study applies a machine learning technique, specifically Convolutional Neural Networks (CNNs), to predict pollutant emissions resulting from failures in actuators and components of Otto engines. The work addresses the current lack of non-intrusive methods that exploit signals already available in the vehicle to estimate, with high accuracy, emissions associated with failures in the injection, ignition, and air intake systems. Concentrations of CO (% carbon monoxide), CO2 (% carbon dioxide), HC (unburned hydrocarbons, ppm), and O2 (% oxygen) are quantified by analyzing the Manifold Absolute Pressure (MAP) sensor signal under a rigorous sampling and signal-processing protocol. Optimal features are extracted from the MAP signal based on their informational relevance and discriminative capacity. These features are obtained through spectrographic transformation, enabling the construction of a robust database. The resulting dataset serves as an effective input for CNN training, achieving emission prediction errors below 1%.