Abstract
State of charge (SOC) is a crucial index used in the assessment of electric vehicle (EV) battery storage systems. Thus, SOC estimation of lithium-ion batteries has been widely investigated because of their fast charging, long-life cycle, and high energy density characteristics. However, precise SOC assessment of lithium-ion batteries remains challenging because of their varying characteristics under different working environments. Machine learning techniques have been widely used to design an advanced SOC estimation method without the information of battery chemical reactions, battery models, internal properties, and additional filters. Here, the capacity of optimized machine learning techniques are presented toward enhanced SOC estimation in terms of learning capability, accuracy, generalization performance, and convergence speed. We validate the proposed method through lithium-ion battery experiments, EV drive cycles, temperature, noise, and aging effects. We show that the proposed method outperforms several state-of-the-art approaches in terms of accuracy, adaptability, and robustness under diverse operating conditions.
Highlights
State of charge (SOC) estimation of lithium-ion batteries is commonly estimated using three methods, namely, conventional[8,9], model-based[10,11,12], and machine learning (ML) approaches[13,14,15]
The superiority of lightning search algorithm (LSA) is compared with three powerful optimization algorithms, namely, backtracking search optimization (BSA), gravitational search algorithm (GSA), and particle swarm optimization (PSO) methods
The results are enhanced in the case of mean absolute error (MAE) which drops by 48.9%, 38.6%, 36.4% compared with RNARX based BSA, GSA, and PSO algorithms, respectively
Summary
SOC estimation through constant discharge test (CDT). The SOC experimental results under different discharge current rates are presented . In HPPC 0.25 C load profile, the mean absolute error (MAE) of RNARX-LSA is 0.2287%, indicating 43.5%, 79.8%, 86.9%, 52%, and 83.3% reductions compared with BPNN-LSA, RBFNN-LSA, ELM-LSA, DRNN-LSA, and RF-LSA, respectively. In HPPC 0.07 C load profile, MSE decreases by 84.8%, 95.8%, 97.6%, 56.3% and 97.4% in RNARX-LSA compared with BPNN-LSA, RBFNN-LSA, ELM-LSA, DRNN-LSA, and RF-LSA methods, respectively. The addition of bias noise to EV drive cycles does not deviate the SOC estimation results considerably, where the proposed approach achieves RMSE and maximum SOC error values of 0.8086%, and 3.42%, respectively in DST drive cycle. The proposed method achieves RMSE, MSE, MAE, MAPE, SD and SOC error of 0.59%, 0.0036%, 0.48%, 2.98%, 0.57% and [−1.84%, 2.78%], respectively, under 50 aging cycles.
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