ANN modeling of cold cranking test for sealed lead-acid batteries

Karami, Hassan; Karimi, Mohammad Ali; Mahdipour, Maryam

doi:10.1016/j.jpowsour.2005.11.024

Cited by 10 publications

(5 citation statements)

References 22 publications

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“…Before the engine was cranked, the SLI battery was charged to SOC 80% (Experiment 1) and SOC 90% (Experiment 2); for further validation, each experiment was performed at different temperatures (20 • C and −25 • C). As shown in Figure 16a,b, when the SLI battery attempted to crank the engine at a lower temperature, the magnitude of i terminal (t c ) increased from −703.32 to −907.29 A because more output power is required for cold cranking [35,36]. Look-up tables of the ECM parameters in Figures 3 and 5 were identified at 20 °C with the charging and discharging test unit in Figure 15.…”

Section: Methodsmentioning

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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Section: Methodsmentioning

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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“…[32] Generally, ANN is used to solve multiple-input and multiple-output situations without the knowledge of the model internal information. [33] ANN has been extensively used in the field of battery temperature prediction. Shashank Arora et al [34] proposed a computational model based on ANN.…”

Section: Doi: 101002/ente202100258mentioning

confidence: 99%

Temperature‐Field Sparse‐Reconstruction of Lithium‐Ion Battery Pack Based on Artificial Neural Network and Virtual Thermal Sensor Technology

Zheng

Wang

et al. 2021

Energy Tech

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To monitor the temperature of lithium‐ion battery packs more accurately with as few sensors as possible, a temperature‐field sparse‐reconstruction technique based on an artificial neural network (ANN) and a virtual thermal sensor (VTS) is proposed herein. 64 uniformly distributed temperature points of lithium‐ion battery packs in seven discharge cycles are measured by a thermometer, and the 64 sensors are further divided into real thermal sensors (RTS) and VTSs according to a certain number and spatial position relationship. In addition, the sensor compression rate (SCR) is defined, herein, to quantitatively measure the impact of the RTS number on temperature‐field sparse‐reconstruction. ANN is built and compared with linear regression (LR). The results show that the temperature‐field sparse‐reconstruction based on ANN and VTS can provide accurate and robust prediction; the maximum mean absolute error (MAE) of ANN is less than 0.1873 °C (SCR = 1.56%) in the experiment. ANN obtains better accuracy with fewer RTS compared with LR. In addition, the proposed principle of sensor layout design is effective. Herein, the temperature‐field sparse‐reconstruction of battery pack is realized without any knowledge of battery thermal properties, heat generation, or thermal boundary conditions, and the optimal number of RTS under given accuracy requirements are obtained.

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“…Due to the discrete values of working conditions, it is of great difficulty to obtain an analytic formula to describe the relationship between discharge capacities and operating conditions. As to multiinputs and multi-outputs, ANN (artificial neural network) framework has a good applicability in terms of mining their internal nonlinear mapping relationships [36,37]. In this paper, the nonlinear relationship between operating conditions and capacity biases is mined through a three-layered BP (back propagation) neural network which is trained by LevenbergeMarquardt algorithm.…”

Section: Effects Of Operating Conditions On Discharge Capacitymentioning

confidence: 99%

Discharge capacity estimation for Li-ion batteries based on particle filter under multi-operating conditions

Wang

Lyu

et al. 2015

Energy

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ANN modeling of cold cranking test for sealed lead-acid batteries

Cited by 10 publications

References 22 publications

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

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

Temperature‐Field Sparse‐Reconstruction of Lithium‐Ion Battery Pack Based on Artificial Neural Network and Virtual Thermal Sensor Technology

Discharge capacity estimation for Li-ion batteries based on particle filter under multi-operating conditions

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