No abstract

Better performance consistency of regrouped batteries retired from electric vehicles can guarantee the residual value maximized, which greatly improves the second-use application economy of retired batteries. This paper develops a fast identification approach for micro-health parameters characterizing negative electrode material and electrolyte in LiFePO4 batteries on the basis of a simplified pseudo two-dimensional model by using Padé approximation is developed. First, as the basis for accurately identifying micro-health parameters, the liquid-phase and solid-phase diffusion processes of pseudo two-dimensional model are simplified based on Padé approximation, especially according to enhanced boundary conditions of liquid-phase diffusion. Second, the reduced pseudo two-dimensional model with the lumped parameter is proposed, the target parameters characterizing negative electrode material (εn, Ds,n) and electrolyte (De, Ce) are grouped with other unknown but fixed parameters, which ensures that no matter whether the target parameters can be achieved, the corresponding varying traces is able to be effectively and independently monitored by lumped parameters. Third, the fast identification method for target micro-health parameters is developed based on the sensitivity of target parameters to constant-current charging voltage, which shortens the parameter identification time in comparison to that obtained by other approaches. Finally, the identification accuracy of the lumped micro-health parameters is verified under 1 C constant-current charging condition.

Remaining useful life (RUL) prediction of lithium‐ion batteries (LIBs) plays a very important role in the prognostics and health management (PHM). Accurately predicting RUL of batteries can maintain and replace the batteries in advance to guarantee the safety and stability of the energy storage system (ESS). A method based on improved ant lion optimization and support vector regression (IALO‐SVR) is proposed to accurately predict RUL of LIBs. The ALO algorithm easily falls into the local optimal solution, the levy flight algorithm is utilized to improve the shortcoming of the ALO algorithm. With the mathematical comparison of particle swarm optimization (PSO), differential evolution (DE), and ALO algorithms, the results indicate that the IALO algorithm has higher convergence accuracy. Experimental data simulations were performed using the battery datasets of NASA Prognostics Center of Excellence (PCoE) and the Center for Advanced Life Cycle Engineering (CALCE) to verify the proposed method. Through comparison with SVR, PSO‐LSSVM, and ALO‐SVR methods, the results indicate that the RUL prediction is more accurate based upon the IALO‐SVR method. Therefore, the proposed method can provide high prediction accuracy in battery health prognosis.

No abstract

Summary Many researchers have focused on liquid‐cooled devices with simple structure and high efficiency, which promoted the gradual development of the mini‐channel liquid‐cooled plate battery thermal management system (BTMS), due to the advancement of liquid cooling technology. This paper has proposed an electrochemical‐thermal coupling model to numerically predict the thermal behavior of the battery pack in different parameters of mini‐channel cold plates and optimize the parameter combinations. The effects of cooling plate width, mini‐channel interval, and inlet mass flow rate on the heat dissipation performance of the system were analyzed at a constant C‐rate to provide a reliable experimental basis for the optimization model. Results indicate that increasing the cold plate width and the inlet mass flow rate reduce the temperature and temperature gradients. In addition, the minimum temperature difference is obtained at the mini‐channel interval of 6 mm. The optimum cooling plate width (90 mm), mini‐channel interval (4 mm), and inlet mass flow rate (80 g/s) are determined using the orthogonal test, analysis of variance, and comprehensive analysis of multi‐index results. The addition of an auxiliary cooling system based on the optimized combination further reduces the maximum temperature and temperature difference of the battery pack by 4.9% and 9.2%, respectively. The developed strategy and methods can further improve the performance of the BTMS and provide a reference for the development of a compact battery pack at high discharge rates for engineering applications.