The maximum available capacity is an important indicator for determining the State-of-Health (SOH) of a lithium-ion battery. Upon analyzing the experimental results of the cycle life and open circuit voltage tests, a novel health factor which can be used to characterize the maximum available capacity was proposed to predict the battery’s SOH. The health factor proposed contains the features extracted from the terminal voltage drop during the battery rest. In real applications, obtaining such health factor has the following advantages. The battery only needs to have a rest after it is charged or discharged, it is easy to implement. Charging or discharging a battery to a specific voltage rather than a specific state of charge which is difficult to obtain the accurate value, so the health factor has high accuracy. The health factor is not dependent on the cycle number of the cycle life test of the battery and it is less dependent on charging or discharging current rate, as a result, the working conditions have less effect on the health factor. Further, the paper adopted a support vector machine approach to connect the healthy factor to the maximum available battery capacity of the battery. The experimental results show that the proposed method can predict the SOH of the battery well.
The performance behavior of the lithium-ion battery can be simulated by the battery model and thus applied to a variety of practical situations. Although the particle swarm optimization (PSO) algorithm has been used for the battery model development, it is usually unable to find an optimal solution during the iteration process. To resolve this problem, an adaptive random disturbance PSO algorithm is proposed. The optimal solution can be updated continuously by obtaining a new random location around the particle's historical optimal location. There are two conditions considered to perform the model process. Initially, the test operating condition is used to validate the model effectiveness. Secondly, the verification operating condition is used to validate the model generality. The performance results show that the proposed model can achieve higher precision in the lithium-ion battery behavior, and it is feasible for wide applications in industry.
Accurate prediction of the state of health (SOH) is essential to ensure the safety and reliability of battery operation. The thermal factor is an important indicator of SOH and many methods based on temperature are sensitive to measurement noise. To improve SOH estimation precision, a new health indicator (HI) directly extracted from the temperature curve is developed and an integrated multi-Gaussian process regression (MGPR) model is proposed. First, based on the trend analysis of the charging temperature curves with battery degradation, three features that can reflect thermal characteristics are extracted as the HI. Second, considering that the model generated by machine learning is influenced by the training dataset and the inherent inconsistencies in batteries, MGPR model is proposed to improve the model fitness. The training data is reformed and multiple GPR models are established. The multiple models are weighed by taking into account the prediction uncertainty to get the final SOH estimation result. Finally, two types of open-source data relative to different ambient temperatures and operating profiles are used to verify the performance. Experiment results show that the HI developed can characterize the battery degradation well and the MGPR model has high robustness and can obtain high-precision estimation results.
With the increasing severity of energy crisis and the gradual increase of environmental awareness, countries around the world have devoted themselves to the new energy vehicle industry. [1] As the foundation and core component of electric vehicles, the power battery pack and its management system play a decisive role in the performance and development of electric vehicles. The power battery pack often uses lithium-ion batteries with higher energy density, lower self-discharge rate, and long cycle life. [2][3][4][5][6] Accurate stateof-charge (SOC) estimation is an important factor to control the stable operation of batteries, which directly affects the life and safety of the battery. [7][8][9] At present, SOC estimation can be roughly divided into two categories: principle-based and model-based methods. [10,11] The modelbased SOC estimation method is widely used because of its high robustness and accuracy under variable battery operating conditions. [12,13] Battery models, as an important part of model-based SOC estimation methods, commonly include electrochemical models, blackbox models and equivalent circuit models (ECMs). The electrochemical model describes the diffusion process and charge transfer process of lithium ions in the cell based on porous electrodes and concentrated solution theory and uses a set of coupled partial differential equations to achieve real-time estimation of SOC. [14][15][16] Ref.[17] designed a nonlinear observer with terminal-voltage feedback injection based on the electrochemical single-particle model, which improved the accuracy of SOC estimation. Although the electrochemical model can reflect the actual electrochemical reaction process inside the battery more accurately, the structure is complicated and not conducive to practical simulation and calculation. The blackbox model establishes an abstract mapping relationship between inputs and outputs, reflecting a generalized direct causal relationship between the factors involved. Real-time estimation of SOC can be achieved by taking real-time state quantities (e.g., voltage, current, etc.) during battery operation as inputs. [18][19][20][21][22] Ref. [23] proposed an improved feedforward-long short-term memory (FF-LSTM) modeling method to realize an accurate whole-life-cycle SOC prediction by effectively considering the current, voltage, and temperature variations. Although the calculation of the blackbox model is simple, the accuracy of the estimation depends to a large extent on the quality and quantity of the training data, and the training process may take a long time. From the perspective of external electrical characteristics, ECM uses circuit elements such as resistance, capacitance, and voltage source to form a circuit network to express the relationship between voltage and current in the cell. [24] It cleverly avoids the real structure and complex electrochemical reactions inside the battery and can estimate SOC in real-time by combining it with the filtering algorithm. [25][26][27][28][29] Ref. [30] proposed a novel feedback
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