Model identification and parameter estimation for LiFePO4 batteries

We propose a method for dynamic model identification and parameter estimation of LiFePO4 cells based on current pulse measurements and electrochemical impedance spectroscopy (EIS). Modelling efforts were focused on diffusion as the predominant dynamic process relevant to battery management systems. An equivalent circuit model approach was adopted with parameters dependant on temperature and state of charge (SOC). The model was parameterised at 50°C, 20°C, 0°C and -30°C and between 10% and 100% SOC. Initial parameter estimations for the model identification procedure were informed by EIS. The model was validated (at constant SOC), for the entire temperature range and at C-rates between C/2 and 9C, by voltage simulation based on a dynamic drive cycle profile. Maximal residuals did not exceed 68 mV or 2% of the nominal cell voltage (Vnom) and root-mean-squared deviations remained within 28 mV or 0.8% of Vnom at all temperatures and C-rates

Model identification and parameter estimation for LiFePO4 batteries

We propose a method for dynamic model identification and parameter estimation of LiFePO4 cells based on current pulse measurements and electrochemical impedance spectroscopy (EIS). Modelling efforts were focused on diffusion as the predominant dynamic process relevant to battery management systems. An equivalent circuit model approach was adopted with parameters dependant on temperature and state of charge (SOC). The model was parameterised at 50°C, 20°C, 0°C and -30°C and between 10% and 100% SOC. Initial parameter estimations for the model identification procedure were informed by EIS. The model was validated (at constant SOC), for the entire temperature range and at C-rates between C/2 and 9C, by voltage simulation based on a dynamic drive cycle profile. Maximal residuals did not exceed 68 mV or 2% of the nominal cell voltage (Vnom) and root-mean-squared deviations remained within 28 mV or 0.8% of Vnom at all temperatures and C-rates

Time-domain fitting of battery electrochemical impedance models

Electrochemical impedance spectroscopy (EIS) is an effective technique for diagnosing the behaviour of electrochemical devices such as batteries and fuel cells, usually by fitting data to an equivalent circuit model (ECM). The common approach in the laboratory is to measure the impedance spectrum of a cell in the frequency domain using a single sine sweep signal, then fit the ECM parameters in the frequency domain. This paper focuses instead on estimation of the ECM parameters directly from time-domain data. This may be advantageous for parameter estimation in practical applications such as automotive systems including battery-powered vehicles, where the data may be heavily corrupted by noise. The proposed methodology is based on the simplified refined instrumental variable for continuous-time fractional systems method ('srivcf'), provided by the Crone toolbox [1,2], combined with gradient-based optimisation to estimate the order of the fractional term in the ECM. The approach was tested first on synthetic data and then on real data measured from a 26650 lithium-ion iron phosphate cell with low-cost equipment. The resulting Nyquist plots from the time-domain fitted models match the impedance spectrum closely (much more accurately than when a Randles model is assumed), and the fitted parameters as separately determined through a laboratory potentiostat with frequency domain fitting match to within 13%

Time-domain fitting of battery electrochemical impedance models

Electrochemical impedance spectroscopy (EIS) is an effective technique for diagnosing the behaviour of electrochemical devices such as batteries and fuel cells, usually by fitting data to an equivalent circuit model (ECM). The common approach in the laboratory is to measure the impedance spectrum of a cell in the frequency domain using a single sine sweep signal, then fit the ECM parameters in the frequency domain. This paper focuses instead on estimation of the ECM parameters directly from time-domain data. This may be advantageous for parameter estimation in practical applications such as automotive systems including battery-powered vehicles, where the data may be heavily corrupted by noise. The proposed methodology is based on the simplified refined instrumental variable for continuous-time fractional systems method ('srivcf'), provided by the Crone toolbox [1,2], combined with gradient-based optimisation to estimate the order of the fractional term in the ECM. The approach was tested first on synthetic data and then on real data measured from a 26650 lithium-ion iron phosphate cell with low-cost equipment. The resulting Nyquist plots from the time-domain fitted models match the impedance spectrum closely (much more accurately than when a Randles model is assumed), and the fitted parameters as separately determined through a laboratory potentiostat with frequency domain fitting match to within 13%

Gaussian process regression for in-situ capacity estimation of lithium-ion batteries

Accurate on-board capacity estimation is of critical importance in lithium-ion battery applications. Battery charging/discharging often occurs under a constant current load, and hence voltage vs. time measurements under this condition may be accessible in practice. This paper presents a data-driven diagnostic technique, Gaussian Process regression for In-situ Capacity Estimation (GP-ICE), which estimates battery capacity using voltage measurements over short periods of galvanostatic operation. Unlike previous works, GP-ICE does not rely on interpreting the voltage-time data as Incremental Capacity (IC) or Differential Voltage (DV) curves. This overcomes the need to differentiate the voltage-time data (a process which amplifies measurement noise), and the requirement that the range of voltage measurements encompasses the peaks in the IC/DV curves. GP-ICE is applied to two datasets, consisting of 8 and 20 cells respectively. In each case, within certain voltage ranges, as little as 10 seconds of galvanostatic operation enables capacity estimates with approximately 2--3% RMSE

Degradation diagnostics for lithium ion cells

Degradation in lithium ion (Li-ion) battery cells is the result of a complex interplay of a host of different physical and chemical mechanisms. The measurable, physical effects of these degradation mechanisms on the cell can be summarised in terms of three degradation modes, namely loss of lithium inventory, loss of active positive electrode material and loss of active negative electrode material. The different degradation modes are assumed to have unique and measurable effects on the open circuit voltage (OCV) of Li-ion cells and electrodes. The presumptive nature and extent of these effects has so far been based on logical arguments rather than experimental proof. This work presents, for the first time, experimental evidence supporting the widely reported degradation modes by means of tests conducted on coin cells, engineered to include different, known amounts of lithium inventory and active electrode material. Moreover, the general theory behind the effects of degradation modes on the OCV of cells and electrodes is refined and a diagnostic algorithm is devised, which allows the identification and quantification of the nature and extent of each degradation mode in Li-ion cells at any point in their service lives, by fitting the cells’ OC

Modular converter system for low-cost off-grid energy storage using second life li-ion batteries

Lithium ion batteries are promising for small off-grid energy storage applications in developing countries because of their high energy density and long life. However, costs are prohibitive. Instead, we consider “used” Li-ion batteries for this application, finding experimentally that many discarded laptop cells, for example, still have good capacity and cycle life. In order to make safe and optimal use of such cells, we present a modular power management system using a separate power converter for every cell. This novel approach allows individual batteries to be used to their full capacity. The power converters operate in voltage droop control mode to provide easy charge balancing and implement a battery management system to estimate the capacity of each cell, as we demonstrate experimentally

Modular converter system for low-cost off-grid energy storage using second life li-ion batteries

Lithium ion batteries are promising for small off-grid energy storage applications in developing countries because of their high energy density and long life. However, costs are prohibitive. Instead, we consider “used” Li-ion batteries for this application, finding experimentally that many discarded laptop cells, for example, still have good capacity and cycle life. In order to make safe and optimal use of such cells, we present a modular power management system using a separate power converter for every cell. This novel approach allows individual batteries to be used to their full capacity. The power converters operate in voltage droop control mode to provide easy charge balancing and implement a battery management system to estimate the capacity of each cell, as we demonstrate experimentally