Use of artificial intelligence for utility-scale battery systems

Abstract

In the battery system, the application of artificial intelligence will have a profound impact on it. With the use of lithium-ion batteries, more and more data are accumulated, so the use of artificial intelligence methods can achieve accurate battery parameter prediction for battery systems. This chapter mainly introduces artificial intelligence that is an application in utility-scale battery systems. First, this chapter analyzes the application of commonly used artificial intelligence methods in battery systems from the selection criteria for selecting artificial intelligence technologies, which include the common artificial intelligence methods for utility-scale battery systems and the artificial intelligence technology evaluation index. Besides, this chapter describes the monitoring of utility-scale battery development with artificial intelligence, which includes the development status and prospects. Last, the basic parameters in AI-based status monitoring are analyzed in this chapter, such as voltage for input and correction, capacity for internal state parameters, internal resistance for internal state parameters, polarization resistance and capacitance internal state parameters, and energy density for correction. For the application of artificial intelligence methods in lithium-ion battery systems, the first part is the selection criteria for artificial intelligence technology. This part analyzes the application of some existing artificial intelligence methods in lithium-ion battery systems, including utility-scale applications. Common artificial intelligence methods are explained, such as some machine-learning artificial intelligence methods, data-driven methods, etc., through the introduction of these methods, the application prospects of artificial intelligence methods in lithium-ion battery systems, and the resulting benefit.