Estimation of Lithium-Ion Battery State of Charge Based on Genetic Algorithm Support Vector Regression under Multiple Temperatures

Abstract

In the energy crisis and post-epidemic era, the new energy industry is thriving, encompassing new energy vehicles exclusively powered by lithium-ion batteries. Within the battery management system of these new energy vehicles, the state of charge (SOC) estimation plays a pivotal role. The SOC represents the current state of charge of the lithium-ion battery. This paper proposes a joint estimation algorithm based on genetic algorithm (GA) simulating biogenetic properties and support vector regression (SVR) to improve the prediction accuracy of lithium-ion battery SOC. Genetic algorithm support vector regression (GASVR) is proposed to address the limitations of traditional SVR, which lacks guidance on parameter selection. The model attains notable accuracy. GASVR constructs a set of solution spaces, generating initial populations that adhere to a normal distribution using a stochastic approach. A fitness function calculates the fitness value for each individual. Based on their fitness, the roulette wheel method is employed to generate the next-generation population through selection, crossover, and mutation. After several iterations, individuals with the highest fitness values are identified. These top individuals acquire parameter information, culminating in the training of the final SVR model. The model leverages advanced mathematical techniques to address SOC prediction challenges in the Hilbert space, providing theoretical justification for handling intricate nonlinear problems. Rigorous testing of the model at temperatures ranging from −20 °C to 25 °C under three different working conditions demonstrates its superior accuracy and robustness compared to extreme gradient boosting (XGBoost), random forest regression (RFR), linear kernel function SVR, and the original radial basis kernel function SVR. The model proposed in this paper lays the groundwork and offers a scheme for predicting the SOC within the battery management system of new energy vehicles.

3D Vase Design Based on Interactive Genetic Algorithm and Enhanced XGBoost Model

“…If generation > interactive_generation && is_adjust() Then (7) adjust(population[generation]) (8) end (9) evaluate(population[generation]) (10) chromosomes = encode(population[generation]) (11) While(population[generation+1].size() < population_size) do (12) pairs = select(chromosomes) (13) If rand() < crossover_rate Then (14) pairs = crossover(pairs) (15) end…”

“…The GA involves the optimization of target systems, models, and performance in multiple fields. In recent years, the GA has been applied to effective feature selection for IoT botnet attack detection [6], active disturbance rejection control of bearingless permanent magnet synchronous motors [7], gesture recognition CAPTCHA [8], state-ofcharge estimation of lithium-ion batteries [9], and residential virtual power plants [10]. The application of the GA in these areas has become increasingly widespread, with more and more researchers applying the GA to solve various practical problems in their own fields.…”