Cooperative Estimation Of State Of Charge And Health Of Lithium-ion Batteries Based On Fractional Order Model

Energy Storage Systems(ESS)have become an important part of active distribution networks and microgrids,which can supply power to various new energy distributed generation systems or store energy as a load independently.In order to ensure the safe operation of energy storage systems,it is important to detect the operating status of energy storage systems online in real time.A cooperative online estimation method of state of charge(SOC)and state of health(SOH)of a single battery is studied in this thesis.By detecting the SOC and SOH of a single battery in an energy storage system,predicted battery information can be obtained in real time to provide a basis for safe operation,inspection and maintenance.The main contents of are as follows:First,considering the model accuracy,constant phase element(CPE)with fractional order property is introduced to establish the second-order fractional order model(FOM)of the battery,and the state space equations are established based on the circuit model.The battery state space equation is discretization,and the battery parameters needed to be identified are derived.Next,the initial values of the battery parameters are identified offline using particle swarm optimizer(PSO).Considering that the battery parameters are slow variables and the SOC is a fast variable,a multi-timescale online estimation method of SOC and battery parameters based on a combination of short timescale state filter and long timescale parameter filter is investigated,which uses the dual fractional order model extended kalman filter(DFOEKF)for simultaneous estimation of battery parameters and SOC.Furthermore,the experimental validation analysis is conducted under constant current charge/discharge and dynamic stress test(DST)conditions using a dedicated battery test cabinet.The results show that the above algorithm can estimate the battery SOC and battery parameters in real time,and the online estimation error of the SOC of batteries with different aging levels is within 0.5%.Meanwhile,based on the time-series weighting method,the estimated SOC is used to estimate SOH in real time.By recording the initial and final values of SOC and calculating the accumulated time at different currents,the algorithm can realize the real-time estimation of the SOH of the battery at any depth of discharge and at any rate of discharge,thus realizing the cooperative estimation of SOC and SOH.By comparing the SOH and internal resistance of batteries with different aging levels,the conditions for battery retirement are set and the batteries are retired in steps to achieve the maximum utilization of the batteries.Finally,a self-built battery test system based on the bidirectional DC-DC converter is installed to experimentally verify the content of this thesis,and the experimental results also verify the feasibility and effectiveness of the proposed method.