Mathematical Modeling of the State of the Battery of Cargo Electric Vehicles

Abstract

In this paper, a mathematical simulation model of an electric vehicle traction battery has been developed, in which the battery was studied during the dynamic modes of its charge and discharge for heavy electric vehicles in various driving conditions—the conditions of the urban cycle and movement outside the city. The state of a lithium-ion battery is modeled based on operational factors, including changes in battery temperature. The simulation results will be useful for the implementation of real-time systems that take into account the processes of changing the characteristics of traction batteries. The developed mathematical model can be used in battery management systems to monitor the state of charge and battery degradation using the assessment of the state of charge (SOC) and the state of health (SOH). This is especially important when designing and operating a smart battery management system (BMS) in virtually any application of lithium-ion batteries, providing information on how long the device will run before it needs to be charged (SOC value) and when the battery should be replaced due to loss of battery capacity (SOH value). Based on the battery equivalent circuit and the system of equations, a simulation model was created to calculate the electrical and thermal characteristics. The equivalent circuit includes active and reactive elements, each of which imitates the physicochemical parameter of the battery under study or the structural element of the electrochemical battery. The input signals of the mathematical model are the current and ambient temperatures obtained during the tests of the electric vehicle, and the output signals are voltage, electrolyte temperature and degree of charge. The resulting equations make it possible to assign values of internal resistance to a certain temperature value and a certain value of the degree of charge. As a result of simulation modeling, the dependence of battery heating at various ambient temperatures was determined.

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“…A conservative summary of production data by different EOR groups is presented in Table 1. The share distribution of oil production volumes due to different EOR groups differs from the structure of the number of EOR projects; thermal EOR continues to play the first role, although there is a downward trend [173]. The share of production through chemical technologies is growing noticeably, reaching 17-19%, significantly exceeding their weight share in the number of projects.…”

“…The share distribution of oil production volumes due to different EOR groups differs from the structure of the number of EOR projects; thermal EOR continues to play the first role, although there is a downward trend [173]. The share of production through chemical A conservative assessment implies the exclusion of projects with extra-viscous oil (density less than 10 • API, which is directly indicated in the primary sources of publications), considering them as a separate category of hydrocarbons together with bitumen, more strict consideration of additional production in projects of heavy oil and gas methods [170].…”

Mathematical Logic Model for Analysing the Controllability of Mining Equipment

“…Based on the results obtained, it can be said that there is no universal way to connect diagnostic equipment [76][77][78]. Each specific case is unique and requires its own optimal method [79,80].…”

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“…The magnetic azimuth is the angle between the magnetic meridian and the direction to a given object. It is clear that the true and magnetic azimuths differ by the same value by which the magnetic meridian differs from the true meridian [33]. This value is called magnetic declination.…”