79 lines
4.4 KiB
Mathematica
79 lines
4.4 KiB
Mathematica
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function lifeMdl = func_LifeMdl_LoadParameters(battery_chemistry)
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switch battery_chemistry
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case 'NMC|Gr'
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lifeMdl.model = 'NMC|Gr';
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% battery life model parameters
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lifeMdl.ref = 'NREL/K. Smith et al. American Control Conference, 2017';
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lifeMdl.description = 'Model fit to 13 aging conditions for Kokam 75Ah cell. Data collected by NREL';
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lifeMdl.chemistry = 'Graphite negative electrode, Ni-Mn-Co (NMC) positive electrode';
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lifeMdl.names_states = {...
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'dq_LLI_t: Delta relative capacity-Li loss due to SEI growth, time dependent';...
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'dq_LLI_EFC: Delta relative capacity-Li loss due to SEI growth, charge-throughput dependent';...
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'dq_LAM: Delta relative capacity-Electrode damage and lithium plating, charge-throughput dependent';...
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'dr_LLI_t: Delta relative resistance-SEI growth, time dependent';...
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'dr_LLI_EFC: Delta relative resistance-SEI growth, charge-throughput dependent';...
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'dr_LAM: Delta relative resistance-Electrode damage and lithium plating, charge-throughput dependent'};
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lifeMdl.names_outputs= {...
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'q: relative discharge capacity at C/5 rate (5 hour discharge)';...
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'qLoss_LLI: relative capacity loss due to SEI growth';...
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'qLoss_LLI_t: relative capacity loss due to SEI growth, time dependent';...
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'qLoss_LLI_t_LLI_EFC: relative capacity loss due to SEI growth, charge-throughput dependent';...
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'qLoss_LLI_t_LAM: relative capacity loss due to electrode damage and lithium plating, charge-throughput dependent';...
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'r: relative DC pulse resistance';...
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'rGain_LLI: relative resistance gain due to SEI growth';...
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'rGain_LLI_t: relative resistance gain due to SEI growth, time dependent';...
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'rGain_LLI_EFC: relative resistance gain due to SEI growth, charge-throughput dependent';...
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'rGain_LAM: relative resistance gain due to electrode damage and lithium plating, charge-throughput dependent'};
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lifeMdl.n_states = 6; % number of states in model that are integrated with time or cycles
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lifeMdl.n_outputs= 10; % number of outputs in model
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% Model parameters
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lifeMdl.q1_0 = 2.66e7;
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lifeMdl.q1_1 = -17.8;
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lifeMdl.q1_2 = -5.21;
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lifeMdl.q2 = 0.357;
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lifeMdl.q3_0 = 3.80e3;
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lifeMdl.q3_1 = -18.4;
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lifeMdl.q3_2 = 1.04;
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lifeMdl.q4 = 0.778;
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lifeMdl.q5_0 = 1e4;
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lifeMdl.q5_1 = 153;
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lifeMdl.p_LAM = 10;
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lifeMdl.r1 = 0.0570;
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lifeMdl.r2 = 1.25;
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lifeMdl.r3 = 4.87;
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lifeMdl.r4 = 0.712;
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lifeMdl.r5 = -0.08;
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lifeMdl.r6 = 1.09;
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case 'LFP|Gr'
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% Life model information
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lifeMdl.model = 'LFP|Gr';
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lifeMdl.names_states = {...
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'dqCal: time dependent capacity loss',...
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'dqCyc: energy-throughput dependent capacity loss',...
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'drCal: time dependent resistance growth',...
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'drCyc: energy-throughput dependent resistance growth'};
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lifeMdl.names_outputs= {...
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'q: Relative capacity',...
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'qLossCal: Time dependent capacity loss',...
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'qLossCyc: Energy-throughput dependent capacity loss',...
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'r: Relative resistance',...
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'rGainCal: Time dependent resistance growth',...
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'rGainCyc: Energy-throughput dependent resistance growth',...
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};
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lifeMdl.n_states = 4; % number of states in model that are integrated with time or cycles
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lifeMdl.n_outputs= 6; % number of outputs in model
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% Instantiate parameter table
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pVars = {'p1','p2','p3','pcal','p4','p5','p6','pcyc'};
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p = [798.352102069967,-3781.20591728016,-2.04881543271387,0.494361078068684,0.000170691091574341,0.000476715369722458,0.00217277383391531,0.439274000189976];
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p = array2table(p, 'VariableNames', pVars);
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lifeMdl.p = p;
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% pvars_rdc = {'r1','r2','r3','rcal','r4','r5','r6','rcyc'};
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p_rdc = [537306.726059759,-6747.41305124106,-1.66987638954611,0.994115565719101,4.64136140815756e-08,-5.79557082332511e-11,7.28339782547118e-06,0.931662358013465];
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p_rdc = array2table(p_rdc, 'VariableNames', pVars);
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lifeMdl.p_rdc = p_rdc;
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end
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end
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