Abstract
Battery management is the key technical link for electric vehicles. A good battery management system can realize the balanced charge and discharge of batteries, reducing the capacity degradation and the loss of health caused by battery overcharge and discharge, which all depend on the real-time and accurate estimation of the battery’s state of charge (SOC). However, the battery’s SOC has highly complex nonlinear time-varying characteristics related to the complex chemical and physical state and dynamic environmental conditions, which are difficult to measure directly, and this has become a difficulty in design and research. According to the characteristics of ternary lithium-ion batteries of electric vehicles, a battery SOC dual estimation algorithm based on the Variable Forgetting Factor Recursive Least Square (VFFRLS) and Multi-Innovation Unscented Kalman Filter (MIUKF) is proposed in this paper. The VFFRLS algorithm is used to estimate battery model parameters, and the MIUKF algorithm is used to estimate the battery’s SOC in real time. The two algorithms are coupled to update battery model parameters and estimate the SOC. The experiment results show that the algorithm has high accuracy and stability.
Highlights
With the reduction in fossil energy reserves and the intensification of environmental pollution, in order to achieve the goal of “carbon neutralization” and optimize the industrial structure and energy structure, countries all over the world have increased their investment in the new energy industry
In order to solve this problem, an online parameter identification method is proposed. This method identifies the parameters of the battery model in real time and synchronously with the state of charge (SOC) online estimation through the online parameter identification algorithm according to the characteristic parameters such as current, voltage and temperature collected by the sensor during the working process of the battery system
The Variable Forgetting Factor Recursive Least Square (VFFRLS) method is an improved algorithm based on the recursive least square method (RLS) used to find the optimal value of the forgetting factor adaptively according to the estimation error in the process of parameter identification [16]
Summary
With the reduction in fossil energy reserves and the intensification of environmental pollution, in order to achieve the goal of “carbon neutralization” and optimize the industrial structure and energy structure, countries all over the world have increased their investment in the new energy industry. In order to solve this problem, an online parameter identification method is proposed This method identifies the parameters of the battery model in real time and synchronously with the SOC online estimation through the online parameter identification algorithm according to the characteristic parameters such as current, voltage and temperature collected by the sensor during the working process of the battery system. The equivalent circuit model uses circuit elements such as resistance, capacitance and power supply to form a circuit network to describe the dynamic characteristics of the battery It can clearly reflect the electrical characteristics of the battery, and the model is relatively simple, meaning that it is suitable and widely used for tasks requiring real-time calculation such as SOC. Where U1 and U2 are the derivatives of U1 and U2 with respect to time, respectively
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