Abstract
A battery’s state-of-power (SOP) refers to the maximum power that can be extracted from the battery within a short period of time (e.g., 10 s or 30 s). However, as its use in applications is growing, such as in automatic cars, the ability to predict a longer usage time is required. To be able to do this, two issues should be considered: (1) the influence of both the ambient temperature and the rise in temperature caused by Joule heat, and (2) the influence of changes in the state of charge (SOC). In response, we propose the use of a model-based extreme learning machine (Model-ELM, MELM) to predict the battery future voltage, power, and surface temperature for any given load current. The standard ELM is a kind of single-layer feedforward network (SLFN). We propose using a set of rough models to replace the active functions (such as logsig()) in the ELM for better generalization performance. The model parameters and initial SOC in these “rough models” are randomly selected within a given range, so little prior knowledge about the battery is required. Moreover, the identification of the complex nonlinear system can be transferred into a standard least squares problem, which is suitable for online applications. The proposed method was tested and compared with RLS (Recursive Least Square)-based methods at different ambient temperatures to verify its superiority. The temperature prediction accuracy is higher than ±1.5 °C, and the RMSE (Root Mean Square Error) of the power prediction is less than 0.25 W. It should be noted that the accuracy of the proposed method does not rely on the accuracy of the state estimation such as SOC, thereby improving its robustness.
Highlights
Electric vehicles (EV) and Plug-in hybrid electric vehicles (PHEV) are transportation solutions to the worldwide energy and environmental crisis [1,2,3], in which lithium-based batteries are commonly used as the only power source [4,5]
Understanding that the battery should not be overused, the predicted maximum output power should satisfy a set of constraints such as those relating to state of charge (SOC) [13], state of energy (SOE)[14], voltage [15], and temperature [16]
In response to the above problems, we propose a model-based extreme learning machine (MELM)
Summary
Electric vehicles (EV) and Plug-in hybrid electric vehicles (PHEV) are transportation solutions to the worldwide energy and environmental crisis [1,2,3], in which lithium-based batteries are commonly used as the only power source [4,5]. Understanding that the battery should not be overused, the predicted maximum output power should satisfy a set of constraints such as those relating to SOC [13], state of energy (SOE)[14], voltage [15], and temperature [16]. Method to model the battery and predict the future temperature, voltage, and power. The online identification of some model parameters in this case, such as the time delay or temperature coefficients, can be difficult and computationally complex To overcome this problem, we rely on the advantages of the ELM method: first, randomly select these parameters within a given range for each model in the network and, second, integrate these models with random parameters using a standard least squares (LS) algorithm.
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