Review Of Various Algorithms Used To Monitor The Performance Of EV Battery

Abstract

Electric vehicles (EV) are gaining a high demand due to its non-reliance on renewable energy and no release of harmful gases. Considering the current condition of high-level pollution in various cities, electric vehicles are the most feasible solution for this problem. Electric vehicles also come with their own disadvantages. The effectiveness of the EV battery, availability of charging station or charging points, correct prediction of remaining battery life and battery health are considered the major issues in EV. Various charging algorithms are used to alleviate many of these problems. The battery part of EV’s plays a major role. Many algorithms have been developed to monitor various parameters of the battery of EVs and also predict their behavioural pattern. The paper discusses the RC parameter optimization algorithms which are used to optimize the parameters of a resistor-capacitor (RC) circuit, which is often used to model the behaviour of an EV battery. These algorithms are used for enhancing the estimates of the battery's State of Charge (SOC) and State of Health (SOH). The optimization algorithms such as Genetic Algorithm (GA), Differential Evolution (DE), Particle Swarm Optimization (PSO), Simulated Annealing (SA) and Levenberg-Marquardt (LM) algorithms are discussed. An overview of various algorithms that are used to monitor EV battery’s performance are also discussed here. These algorithms can help to improve the exactness in estimation of the battery's SOC and SOH, and can be used to optimize the performance of EV batteries. However, the algorithm choice depends on the application's specific requirements and the available data.