battery pulse charge circuit for use with standard charger

It was found that the thermal parameter Cs played a major role in the accuracy of Ts prediction and must have low values to minimize errors.Įstimation of core temperature is one of the crucial functionalities of the lithium-ion Battery Management System (BMS) towards providing effective thermal management, fault detection and operational safety. Hence, an inverse calculation for estimating Ts from known Tc was carried out and found to be reasonably accurate. Most of the battery management system (BMS) applications required Ts as the input to the controller. Tc estimation plays a very important role in designing an effective thermal management and maintaining the state of health (SOH) during fast discharges under limits. For high currents, Tc dominated the surface temperature by about 3 K. It was found that the temperatures closely followed the current patterns. MATLAB/Simulink were used for modelling a Kal-man filter with appropriate process and measurement noise levels. The thermal model did not account for small changes in surface temperature (Ts).

The coupled ODEs were redefined in the discrete state space representation. A set of coupled linear ordinary differential equations (ODEs) were obtained by applying Kirchhoff's current and voltage laws to the thermal model. A second order thermal model was considered for the core temperature (Tc) estimation. The temperatures were measured using thermocouples placed at appropriate locations. This paper deals with the estimation of core temperature (Tc) of a Lithium (Li) ion battery using measured ambient and surface temperatures. A detailed analysis on the errors associated with the algorithms was also carried out It is concluded that the SVM algorithm was best suited. A study on SVM technique was carried out for the simulated and tuned data. Later, these parameters were trained using various Machine Leaning (ML) techniques for regression data analysis using Simulink. A detailed analysis on the errors associated with the algorithms was also carried out. A study on Support Vector Machine (SVM) technique was carried out for the simulated and tuned data. The parameters were trained using various Machine Leaning (ML) techniques for regression data analysis using Simulink. The selection of the best suited algorithm is finalized through the performance indices mainly by evaluating the values of R- Squared. This paper presents the parameter estimation and prediction of state of charge of Li ion batteries by implementing different machine learning techniques. The major challenge in a battery management system is the parameter estimation and prediction of SOC, this is because the degradation of battery is highly nonlinear in nature. These parameters showed dependence on SOC and temperature. The cell parameters of Li polymer and the Li ion cells (NASA dataset) were found be almost identical. It was found that the cell parameters were independent of the nominal capacity of the cell.

This process was performed by tuning the cell parameters. The mean error and the standard deviation of the error were 0.0529 and 0.0310 respectively. The cell model was tuned against the NASA dataset for different currents in such a way that the error in the terminal voltages (difference in terminal voltage between the dataset and the ECM) is <☐.2 V. To validate the developed model, a NASA dataset was used as the reference dataset. In this regard, a Li polymer cell was modeled as a single Resistor-Capacitor (RC) pair (R0, R1 and C1) model using PowerTrain blockset in MATLAB/Simulink software. The modeling and simulations were performed using MATLAB/Simulink software. In this paper, a new methodology for improving the SOC estimation using Equivalent Cell Model (ECM) approach is proposed. An efficient cell model is extremely important for the development of complex algorithms like core temperature estimation, State of Health (SOH) estimation and State of Charge (SOC) estimation. The main source of power in Electric Vehicles (EVs) is derived from batteries.

Battery pulse charge circuit for use with standard charger