Abstract
Accurate and reliable state of charge (SOC) estimation is a key enabling technique for large format lithium-ion battery pack due to its vital role in battery safety and effective management. This paper tries to make three contributions to existing literatures through robust algorithms. (1) Observer based SOC estimation error model is established, where the crucial parameters on SOC estimation accuracy are determined by quantitative analysis, being a basis for parameters update. (2) The estimation method for a battery pack in which the inconsistency of cells is taken into consideration is proposed, ensuring all batteries’ SOC ranging from 0 to 1, effectively avoiding the battery overcharged/overdischarged. Online estimation of the parameters is also presented in this paper. (3) The SOC estimation accuracy of the battery pack is verified using the hardware-in-loop simulation platform. The experimental results at various dynamic test conditions, temperatures, and initial SOC difference between two cells demonstrate the efficacy of the proposed method.
Highlights
As one of the most important performance parameters of traction batteries, real-time state of charge (SOC) estimation of battery becomes necessary in the field of application of batterydriven electric vehicles
Used SOC estimation methods for single battery are as follows [1, 2]: ampere hour integration method; open circuit voltage method using the corresponding relation between the open circuit voltage and SOC; the algorithm based on electric circuit models or electrochemical models and the typical methods which are Kalman filters and methods based on some observer; estimation using fuzzy logic or methods of machine learning
Considering that online identification results of battery model parameters may lead to relatively larger model error when at lower SOC, which leads to SOC estimation error based on PI observer method increases at low SOC, PI observer and ampere hour integration method are combined to obtain SOC for a battery pack
Summary
As one of the most important performance parameters of traction batteries, real-time SOC estimation of battery becomes necessary in the field of application of batterydriven electric vehicles. Some researches based on Kalman filter have achieved online estimation [2,3,4], but online realization is at the expense of additional computing power and its robustness needs to be concerned; the electrochemical method cannot be applied into operation due to the complexity of the model itself [5]; fuzzy control or vector machine algorithm requires a large number of sample data to train the model and is no longer applicable for aged battery [6]. In practical application of pack SOC estimation, the operation conditions, measurement accuracy, and computation of the algorithm need to be considered in integration.
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