手把手教你学Simulink实例--电动汽车的能量回收与制动场景实例：电动汽车再生制动能量回收效率优化仿真

 % 电气系统模型

    
 function [P_rec, IGBT_loss] = regen_braking_model(ω, I_load, T_env)
    
     % 电机参数
    
     P_mech = ω * I_load * τ;  % 机械功率
    
     P_electric = P_mech * η_mech;  % 电机效率（η_mech=0.92）
    
     
    
     % 逆变器损耗模型
    
     IGBT_loss = (V_dc * I_load * f_sw) * (R_on + (V_dc/(2*f_sw))^2 * C_iss);
    
     
    
     % 能量回收效率
    
     P_charged = P_electric * η_inv * η_batt;  % η_inv=0.95（逆变器效率）
    
     eff = P_charged / P_mech * 100;
    
 end

        1. % 逆变器热损耗计算

    
        2. function Q_loss = thermal_loss(P_loss, T_junction)
    
        3.     Q_loss = P_loss * (T_junction - T_ambient) / R_thermal;
    
        4. end

        1. % 车辆动力学方程

    
        2. function a = vehicle_dynamics(F_brake, m, F_air, F_rolling)
    
        3.     a = (F_brake - F_air - F_rolling) / m;
    
        4. end

 % 基于强化学习的能量回收策略

    
 function action = rl_energy_recuperation(state)
    
     % 状态空间：[车速, SOC, 温度]
    
     % 动作空间：[-1,0,1]（制动强度分级）
    
     
    
     % Q-learning更新
    
     Q(s,a) = Q(s,a) + α*(reward + γ*max_a' Q(s',a') - Q(s,a));
    
     
    
     % 奖励函数设计
    
     reward = (P_charged/P_mech)*100 - 0.1*T_junction - 0.2*|da/dt|;
    
 end

 % NSGA-II算法实现

    
 function front = nsga2(population, fitness)
    
     % 非支配排序与拥挤度计算
    
     fronts = paretoFront(population.fitness);
    
     crowdingDist = calculateCrowdingDistance(fronts);
    
     
    
     % 参数优化目标
    
     for gen = 1:100
    
     population = mergePopulation(population, offspring);
    
     fronts = paretoFront(population.fitness);
    
     end
    
     front = fronts;
    
 end

 % 效率随SOC变化曲线

    
 figure;
    
 hold on;
    
 for T_env in [-30, 25, 50]
    
     [efficiency, power] = simulate_regen(T_env);
    
     plot(SOC, efficiency, 'LineWidth', 2);
    
 end
    
 xlabel('SOC (%)');
    
 ylabel('Efficiency (%)');
    
 legend('-30℃', '25℃', '50℃');
    
 grid on;

 % 制动扭矩响应曲线

    
 figure;
    
 plot(t, torque_ref, 'b-', t, torque_actual, 'r--');
    
 xlabel('Time (s)');
    
 ylabel('Torque (N·m)');
    
 legend('Reference', 'Actual');
    
 grid on;

        1. % 数字线程实现

    
        2. function updateDTmodel(energy_data)
    
        3.     dt_model.updatePhysicsParameters(energy_data);
    
        4.     retrain_neural_network(dt_model);
    
        5. end

        1. % 功率调度函数

    
        2. function P = v2g_energy_sched(P_vehicle, P_grid, SOC)
    
        3.     if SOC > 0.7 && P_grid > 50kW {
    
        4.         P = min(P_vehicle, P_grid);
    
        5.     } else {
    
        6.         P = P_vehicle;
    
        7.     }
    
        8. end