A Generic Electrothermal Li-ion Battery Model for Rapid Evaluation of Cell Temperature Temporal Evolution

Motapon, Souleman Njoya; Lupien-Bédard, Alexandre; Dessaint, L.-A.; Fortin-Blanchette, Handy; Al‐Haddad, Kamal

doi:10.1109/tie.2016.2618363

Cited by 71 publications

(64 citation statements)

References 27 publications

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“…As already mentioned in the introduction, a mathematical model is suitable for the rapid testing of different battery types and configurations. We used a mathematical model developed by Motapon et al [26]. It is based on the work by Tremblay and Dessaint, who published a modified version of the Shepherd model in 2009 [25].…”

Section: A Mathematical Modelmentioning

confidence: 99%

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Comparison of Parameterization Methods for Real-Time Battery Simulation Used in Mechatronic Powertrain Test Benches

2020

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This paper deals with the real-time simulation of a power tool battery pack on a mechatronic powertrain test bench. The ability of an easy-to-use model for quick and iterative test runs mainly depends on the effort of parameterization. For this purpose, an easily parameterizable battery model is required. The battery model used is based on the current state of research and simulates the battery's behavior with an adequate precision. The suggested parameterization allows building the model without the necessity of experimental investigation. Three different procedures for model parameterization were used and compared with the real battery behavior. In conclusion, this paper shows a good tradeoff between precision and an easy way to handle a battery model for testing mechatronic powertrains. INDEX TERMS Batteries, modeling, parameter extraction, system analysis and design, system testing, mechanical engineering, mechanical systems, virtual prototyping, hardware-in-the-loop, mechatronic powertrain.

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Section: A Mathematical Modelmentioning

confidence: 99%

“…Indeed, the battery model by Tremblay and Dessaint does not consider the thermal behavior of the battery. This disadvantage is considered in the model by Motapon et al [26]. Accordingly, it is an extended version of the model by Tremblay and Dessaint, as it adds the thermal behavior of the battery.…”

Section: A Mathematical Modelmentioning

confidence: 99%

Comparison of Parameterization Methods for Real-Time Battery Simulation Used in Mechatronic Powertrain Test Benches

2020

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“…Liion batteries have higher energy and power density, higher efficiency and lower selfdischarge when compared to other batteries (NiCd, NiMH, and Lead Acid). To ensure the Liion battery is operating at a proper temperature and state of charge (SOC), a battery management supervision system (BMSS) must be applied [6]. to 181 MW in 2014 [7,8].…”

Section: For Example Sopian Et Al (2009) Integrated Amentioning

confidence: 99%

Sizing and Dynamic Modeling of a Power System for the MUN Explorer Autonomous Underwater Vehicle Using a Fuel Cell and Batteries

et al. 2019

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The combination of a fuel cell and batteries has promising potential for powering autonomous vehicles. The MUN Explorer Autonomous Underwater Vehicle (AUV) is built to do mappingtype missions of seabeds as well as survey missions. These missions require a great deal of power to reach underwater depths (i.e. 3000 meters). The MUN Explorer uses 11 rechargeable Lithiumion (Li-ion) batteries as the main power source with a total capacity of 14.6 kWh to 17.952 kWh, and the vehicle can run for 10 hours. The draw-backs of operating the existing power system of the MUN Explorer, which was done by the researcher at the Holyrood management facility, include mobilization costs, logistics and transport, and facility access, all of which should be taken into consideration. Recharging the batteries for at least 8 hours is also very challenging and time consuming. To overcome these challenges and run the MUN Explorer for a long time, it is essential to integrate a fuel cell into an existing power system (i.e. battery bank). The integration of the fuel cell not only will increase the system power, but it will also reduce the number of batteries needed as suggested by HOMER software. In this paper, an integrated fuel cell is designed to be added into the MUN Explorer AUV along with a battery bank system to increase its power system. The system sizing is performed using HOMER software. The results from HOMER software show that a 1-kW fuel cell and 8 Li-ion batteries can increase the power system capacity to 68 kWh. The dynamic model is then built in MATLAB / Simulink environment to provide a better understanding of the system behavior. The 1-kW fuel cell is connected to a DC / DC Boost Converter to increase the output voltage from 24 V to 48 V as required by the battery and DC motor. A hydrogen gas tank is also included in the model. The advantage of installing the hydrogen and oxygen tanks beside the batteries is that it helps the buoyancy force in underwater depths. The design of this system is based on MUN Explorer data sheets and system dynamic simulation results.

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“…Moreover, an online update process for ECM parameters is suggested based on the analysis of dynamic characteristics of the proposed ECM. To validate the developed SOF estimation algorithm, V t0 is associated with the polarization voltage that makes the relaxation voltage change although there is no current applied on the battery [8][9][10]. This polarization voltage causes SOF to vary according to when the driver attempts to crank the engine.…”

Section: Introductionmentioning

confidence: 99%

Cranking Capability Estimation Algorithm Based on Modeling and Online Update of Model Parameters for Li-Ion SLI Batteries

2019

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The terminal voltage of a starting-lighting-ignition (SLI) battery can decrease to a value lower than the allowable voltage range because of the high discharge current required to crank the engine of a vehicle. To avoid the safety problems generated by this voltage drop, this paper proposes a cranking capability estimation algorithm. The proposed algorithm includes an equivalent circuit model for describing the instantaneous voltage response to the cranking current profile. This algorithm predicts the minimum value of the terminal voltage for the cranking transient period by analyzing the polarization voltage and dynamic characteristic of the equivalent circuit model. The estimation accuracy is adjusted by an online update for the parameters of the equivalent circuit model, which varies with temperature, aging, and other factors. The proposed algorithm was validated by experiments with a 60Ah LiFePO4-type SLI battery.

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A Generic Electrothermal Li-ion Battery Model for Rapid Evaluation of Cell Temperature Temporal Evolution

Cited by 71 publications

References 27 publications

Comparison of Parameterization Methods for Real-Time Battery Simulation Used in Mechatronic Powertrain Test Benches

Comparison of Parameterization Methods for Real-Time Battery Simulation Used in Mechatronic Powertrain Test Benches

Sizing and Dynamic Modeling of a Power System for the MUN Explorer Autonomous Underwater Vehicle Using a Fuel Cell and Batteries

Cranking Capability Estimation Algorithm Based on Modeling and Online Update of Model Parameters for Li-Ion SLI Batteries

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