Thermal Management of Electrified Vehicle by Means of System Simulation

Shah, S. B.; Vijay, Dig; Lehocky, Marek

doi:10.4271/2020-28-0033

Cited by 6 publications

(1 citation statement)

References 2 publications

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“…References [8][9][10][11][12][13] developed integrated EVTMSs that could not only effectively predict system parameters but also match the most efficient operating modes under different conditions to save energy. Sanket Shah et al [14] integrated a battery electrochemical model into a battery TMS to predict the battery pack's cycling and aging. References [15][16][17] developed a TMS for plug-in hybrid electric vehicles (PHEVs) based on environmental and target temperature, which ensured system performance and achieved energy savings.…”

Section: Introductionmentioning

confidence: 99%

Application of Neural Network Feedforward in Fuzzy PI Controller for Electric Vehicle Thermal Management System: Modeling and Simulation Studies

Fei,

Wang

2023

Energies

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The electric vehicle thermal management system (EVTMS) plays a crucial role in ensuring battery efficiency, driving range, and passenger comfort. However, EVTMSs still face unresolved challenges, such as accurate modeling, compensating for temperature variations, and achieving efficient control strategies. Addressing these issues is crucial for enhancing the performance, reliability, and energy efficiency of electric vehicles. Therefore, this study presents a cooling EVTMS model, considering both the battery pack temperature and the cabin comfort, and utilizes the prediction of neural network as a feedforward in a fuzzy PI controller to compensate for the model temperature variations. The simulation results reveal that, compared with PI controller and MPC, the neural network fuzzy PI (NN-Fuzzy PI) controller can well predict and compensate for the system’s nonlinear characteristics as well as the time-delay caused by heat transfer, achieving superior control performance and reducing energy consumption. The battery pack temperature and PMV fluctuations are effectively constrained within [−0.5, 0.5] and [−0.1, 0.1], reducing up to 150% and 164%, and the energy consumption of the pump and compressor are reduced by up to 0.23 and 100.1 KJ, with ranges of 18% and 2.68%. Meanwhile, the neural network feedforward also works effectively in different controllers. The findings of this research can provide valuable insights for TMS engineers to select advanced control strategies.

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Section: Introductionmentioning

confidence: 99%

Application of Neural Network Feedforward in Fuzzy PI Controller for Electric Vehicle Thermal Management System: Modeling and Simulation Studies

Fei,

Wang

2023

Energies

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Development of an electrochemically approximated simulation model and a hardware substitution cell approach for thermal management battery system tests

Lorbeck,

Fruehwirth

2024

Automot. Engine Technol.

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Battery tests require a high degree of safety-related preparation and constant monitoring of the operating parameters to guarantee the smooth running of tests without incidents or exceptional events such as a thermal runaway. As the handling before, during, and especially after the tests is relatively complex and sometimes just as costly, it is important to reduce these risks and costs as well as to find an alternative to conventional battery tests. Such an approach is being developed by the T-cell project of the Institute of Thermodynamics and Sustainable Propulsion Systems at Graz University of Technology, founded by the Austrian Research Promotion Agency (FFG). A thermal substitution cell, which resembles a battery cell on the outside, is to reflect the surface temperature distribution of a chemical cell without there being any cell chemistry inside the substitution cell. Rather, the interior should not be part of the observation and the thermal requirements should be provided by an internal heating option. Such a measurement approach requires, among other things, a control and regulation unit, without which it would not be possible to transfer the thermal behavior of a battery cell to the substitution cell. Together with the electronic structure of the substitution cell and a circuit environment including a battery simulation model established at a later date, this control/regulation module forms an overall package that considerably facilitates investigations at cell, module, and pack level by substitution of a certain amount of cells using system symmetry advantages. A suitable simulation model was constructed and parameterized for this purpose from an electrochemical and a thermal network. As a first step, the data, which were partly determined empirically but also derived from real cell measurements in the literature, were adapted to the requirements of the simulation environment, so that the real cell used could be simulated thermally in principle. The simulation observations represent the state of the art of the model and are continuously improved by measurements carried out at the institute, thus building up the overall system in more detail.

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Assessing the influence of glass properties on cabin solar heating and range of an electric vehicle using a comprehensive system model

Penning

Weibel

2023

Applied Energy

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Thermal Management of Electrified Vehicle by Means of System Simulation

Cited by 6 publications

References 2 publications

Application of Neural Network Feedforward in Fuzzy PI Controller for Electric Vehicle Thermal Management System: Modeling and Simulation Studies

Application of Neural Network Feedforward in Fuzzy PI Controller for Electric Vehicle Thermal Management System: Modeling and Simulation Studies

Development of an electrochemically approximated simulation model and a hardware substitution cell approach for thermal management battery system tests

Assessing the influence of glass properties on cabin solar heating and range of an electric vehicle using a comprehensive system model

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