Enhancing Lithium-Ion Battery Performance for Accurate State of Charge Prediction Under Various Operating Cycles and Dynamic Environmental Conditions

Lithium‐Ion Battery Parameter Estimation With Adaptive Honey Badger Optimization and State‐of‐Charge Prediction Using the Extended Kalman Filter

Accurate parameter identification and terminal‐voltage estimation are essential for precise state‐of‐charge estimation and effective control of lithium‐ion battery management systems. Conventional optimization methods, such as Particle Swarm Optimization (PSO) and Honey Badger Optimization (HBO), are prone to local o...

Improved Modeling and Parameter Optimization of Li-Ion Batteries for Electric Vehicles Using Artificial Lemming Algorithm

In electric vehicles (EVs), the battery management system (BMS) plays a central role in ensuring safe, efficient, and reliable battery operation under varying driving and environmental conditions. The effectiveness of a BMS largely depends on the availability of an accurate battery model, whose performance is strongly...

Optimization Design of Lithium-Ion Battery Remaining Useful Life Prediction Based on Particle Swarm Optimization

Accurate remaining useful life (RUL) prediction is the core support for battery health management in new energy vehicles and energy storage systems. Aiming at the accuracy bottleneck caused by empirical parameter setting in neural network (NN) prediction models, this paper constructs a coupled RUL prediction model opti...

A Hybrid Algorithm for High‐Accuracy State‐of‐Charge Estimation in Lithium‐Ion Batteries

Accurate state‐of‐charge (SOC) estimation is essential for ensuring the safety and efficiency of lithium‐ion battery systems under complex operating conditions. To address limitations in convergence speed and estimation accuracy, this paper proposes an improved Latin Hypercube Tuna Swarm Optimization (LHTSO) algorithm....

Comparison of Li-Ion Battery State of Charge Prediction by Artificial Neural Network and Adaptive Neuro Fuzzy Inference System

The comparison of ANFIS method with the neural method showed that the ANFIS method is more accurate in estimating the state of charge and correlates the experimental points and the output of the network, so that ANFIS error in some states of charge is less than 2%.

A comprehensive approach to lithium-ion battery state-of-health prediction for energy management