基于Simulink的电动汽车充电站动态响应仿真

 %% 两电平PWM整流器建模

    
 function [voltage, current] = rectifier_model(V_dc, I_ref)
    
     % 参数（基于SiC器件特性）
    
     R_ds_on = 0.15mΩ;  % 晶体管导通电阻
    
     L_filter = 10μH;    % 输出滤波电感
    
     C_filter = 220μF;   % 输出滤波电容
    
     
    
     % PWM生成模块
    
     duty_cycle = modulate(I_ref, V_dc, 'pwm', 20kHz);
    
     voltage_PWM = V_dc * duty_cycle;
    
     
    
     % 电压电流关系
    
     voltage = voltage_PWM - L_filter*di/dt(current) - R_ds_on*I_current;
    
     current = (voltage_PWM - voltage)/R_ds_on;
    
 end

 %% 基于ARIMA的负载预测

    
 function load_profile = dynamic_load_forecast(t)
    
     % 参数：历史数据均值μ=120kW，标准差σ=30kW
    
     load_profile = μ + σ*sin(2π*0.002*t) + 0.5*randn(size(t));
    
 end

 %% 混合控制器实现

    
 function u = hybrid_controller(e, t, Kp, Ki, Kd, alpha)
    
     % PI控制部分
    
     integral = integral(e, t);
    
     derivative = derivative(e, t);
    
     u_pi = Kp*e + Ki*integral + Kd*derivative;
    
     
    
     % 模糊补偿部分
    
     error_abs = abs(e);
    
     if error_abs < 0.1
    
     u_fuzzy = 0;
    
     elseif error_abs < 0.5
    
     u_fuzzy = 0.5*(error_abs - 0.1)/(0.5 - 0.1);
    
     else
    
     u_fuzzy = 1;
    
     end
    
     
    
     % 组合输出
    
     u = u_pi * (1 - alpha) + u_fuzzy * alpha;
    
 end

 %% ISO工况模拟（充电-放电循环）

    
 function [I, V] = iso_profile()
    
     % 时间(s) | 充电功率(kW) | 放电功率(kW)
    
     data = [0, 0, 0; 1, 120, 0; 2, 120, 0; 3, 0, -60; 4, 0, -60];
    
     t = cumsum(data(:,3));
    
     I = interp1(t, data(:,2), linspace(0,4,1000))';
    
     V = 400 * ones(size(t));
    
 end

 %% NSGA-II参数优化代码

    
 function [front, pareto] = nsga2_optimization()
    
     % 目标函数：功率响应时间、电压超调、成本
    
     nvar = 6; % Kp, Ki, Kd, α, β, γ
    
     lb = [0.1, 0.01, 0.05, 0.3, 0.3, 0.1];
    
     ub = [5, 0.5, 0.2, 0.7, 0.7, 0.5];
    
     
    
     options = optimoptions('nsga2', 'PopulationSize', 100, ...
    
                      'Generations', 200, 'PlotFcn', @gaplot);
    
     
    
     [front, pareto] = nsga2(@objective_function, nvar, lb, ub, options);
    
 end
    
  
    
 function f = objective_function(x)
    
     % 运行仿真获取指标
    
     [t_rise, over_volt, cost] = simulate_system(x);
    
     f(:,1) = t_rise;        % 最小化响应时间
    
     f(:,2) = over_volt;     % 最小化电压超调
    
     f(:,3) = cost;          % 最小化硬件成本
    
 end

 // STM32代码移植示例（C语言）

    
 void charger_control(void) {
    
     // 混合控制核心算法
    
     float e = voltage_error;
    
     float u_pi = Kp*e + Ki*integral(e) + Kd*derivative(e);
    
     float u_fuzzy = fuzzy_compensation(e);
    
     
    
     // PWM输出（7段式SVPWM）
    
     svpwm_generate(u_pi*0.7 + u_fuzzy*0.3);
    
     
    
     // 故障保护
    
     if (current > 150A) {
    
     activate Crowbar();
    
     }
    
 }