The incremental capacity analysis(ICA)technique is notably limited by its sensitivity to variations in charging conditions,which constrains its practical applicability in real-world scenarios.This paper introduces an ...The incremental capacity analysis(ICA)technique is notably limited by its sensitivity to variations in charging conditions,which constrains its practical applicability in real-world scenarios.This paper introduces an ICA-compensation technique to address this limitation and propose a generalized framework for assessing the state of health(SOH)of batteries based on ICA that is applicable under differing charging conditions.This novel approach calculates the voltage profile under quasi-static conditions by subtracting the voltage increase attributable to the additional polarization effects at high currents from the measured voltage profile.This approach's efficacy is contingent upon precisely acquiring the equivalent impedance.To obtain the equivalent impedance throughout the batteries'lifespan while minimizing testing costs,this study employs a current interrupt technique in conjunction with a long short-term memory(LSTM)network to develop a predictive model for equivalent impedance.Following the derivation of ICA curves using voltage profiles under quasi-static conditions,the research explores two scenarios for SOH estimation:one utilizing only incremental capacity(IC)features and the other incorporating both IC features and IC sampling.A genetic algorithm-optimized backpropagation neural network(GABPNN)is employed for the SOH estimation.The proposed generalized framework is validated using independent training and test datasets.Variable test conditions are applied for the test set to rigorously evaluate the methodology under challenging conditions.These evaluation results demonstrate that the proposed framework achieves an estimation accuracy of 1.04%for RMSE and 0.90%for MAPE across a spectrum of charging rates ranging from 0.1 C to 1 C and starting SOCs between 0%and 70%,which constitutes a major advancement compared to established ICA methods.It also significantly enhances the applicability of conventional ICA techniques in varying charging conditions and negates the necessity for separate testing protocols for each charging scenario.展开更多
Finite-control-set model predictive control(FCSMPC)has advantages of multi-objective optimization and easy implementation.To reduce the computational burden and switching frequency,this article proposed a simplified M...Finite-control-set model predictive control(FCSMPC)has advantages of multi-objective optimization and easy implementation.To reduce the computational burden and switching frequency,this article proposed a simplified MPC for dual three-phase permanent magnet synchronous motor(DTPPMSM).The novelty of this method is the decomposition of prediction function and the switching optimization algorithm.Based on the decomposition of prediction function,the current increment vector is obtained,which is employed to select the optimal voltage vector and calculate the duty cycle.Then,the computation burden can be reduced and the current tracking performance can be maintained.Additionally,the switching optimization algorithm was proposed to optimize the voltage vector action sequence,which results in lower switching frequency.Hence,this control strategy can not only reduce the computation burden and switching frequency,but also maintain the steady-state and dynamic performance.The simulation and experimental results are presented to verify the feasibility of the proposed strategy.展开更多
基金funded by the Bavarian State Ministry of ScienceResearch and Art(Grant number:H.2-F1116.WE/52/2)。
文摘The incremental capacity analysis(ICA)technique is notably limited by its sensitivity to variations in charging conditions,which constrains its practical applicability in real-world scenarios.This paper introduces an ICA-compensation technique to address this limitation and propose a generalized framework for assessing the state of health(SOH)of batteries based on ICA that is applicable under differing charging conditions.This novel approach calculates the voltage profile under quasi-static conditions by subtracting the voltage increase attributable to the additional polarization effects at high currents from the measured voltage profile.This approach's efficacy is contingent upon precisely acquiring the equivalent impedance.To obtain the equivalent impedance throughout the batteries'lifespan while minimizing testing costs,this study employs a current interrupt technique in conjunction with a long short-term memory(LSTM)network to develop a predictive model for equivalent impedance.Following the derivation of ICA curves using voltage profiles under quasi-static conditions,the research explores two scenarios for SOH estimation:one utilizing only incremental capacity(IC)features and the other incorporating both IC features and IC sampling.A genetic algorithm-optimized backpropagation neural network(GABPNN)is employed for the SOH estimation.The proposed generalized framework is validated using independent training and test datasets.Variable test conditions are applied for the test set to rigorously evaluate the methodology under challenging conditions.These evaluation results demonstrate that the proposed framework achieves an estimation accuracy of 1.04%for RMSE and 0.90%for MAPE across a spectrum of charging rates ranging from 0.1 C to 1 C and starting SOCs between 0%and 70%,which constitutes a major advancement compared to established ICA methods.It also significantly enhances the applicability of conventional ICA techniques in varying charging conditions and negates the necessity for separate testing protocols for each charging scenario.
基金supported by the National Natural Science Foundation of China under Grant 5227705。
文摘Finite-control-set model predictive control(FCSMPC)has advantages of multi-objective optimization and easy implementation.To reduce the computational burden and switching frequency,this article proposed a simplified MPC for dual three-phase permanent magnet synchronous motor(DTPPMSM).The novelty of this method is the decomposition of prediction function and the switching optimization algorithm.Based on the decomposition of prediction function,the current increment vector is obtained,which is employed to select the optimal voltage vector and calculate the duty cycle.Then,the computation burden can be reduced and the current tracking performance can be maintained.Additionally,the switching optimization algorithm was proposed to optimize the voltage vector action sequence,which results in lower switching frequency.Hence,this control strategy can not only reduce the computation burden and switching frequency,but also maintain the steady-state and dynamic performance.The simulation and experimental results are presented to verify the feasibility of the proposed strategy.
