SOC-SOH Estimation and Balance Control Based on Event-Triggered Distributed Optimal Kalman Consensus Filter

Abstract

The inconsistency in state-of-charge (SOC) for electric vehicle batteries will cause component damage and lifespan reduction of batteries. Meanwhile, the consistency in the state-of-health (SOH) also negatively influences the consensus of SOC. To ensure the consensuses of SOC and SOH simultaneously, this paper introduces an innovative distributed optimal Kalman consensus filter (KCF) approach to battery management systems. In addition, at the stage where sensors transmit information to each other, a new event-triggering mechanism (ETM) based on dynamic information is proposed to reduce communication overhead effectively. Theoretical analysis verifies the optimality of the algorithm. By numerical simulations, the proposed event-triggered distributed optimal KCF (ET-DOKCF) method can improve the performance of SOC-SOH estimation and save communication resources.

A Review of Lithium-Ion Battery State of Charge Estimation Methods Based on Machine Learning

“…Model-based approaches are capable of accommodating the dynamic behavior of batteries and changes in environmental conditions within DST. This is particularly true for algorithms like the extended Kalman filter [100], which adjust the model parameters by continuously updating the state equations and observation equations within the state space, thereby maintaining the estimation accuracy under changing test conditions. However, in extreme conditions, the constant need to correct the battery model parameters presents challenges, leading to issues with parameter tuning, model instability, and high computational costs.…”