146 lines
5.9 KiB
Python
146 lines
5.9 KiB
Python
import os
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import sys
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# ===== 新增：OpenMP冲突兼容设置，避免PyTorch初始化报错 =====
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os.environ.setdefault("KMP_DUPLICATE_LIB_OK", "TRUE")
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import numpy as np
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import matplotlib
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matplotlib.use('Agg')
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import matplotlib.pyplot as plt
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MODEL_SRC_PATH = os.path.join(os.path.dirname(os.path.abspath(__file__)), "Model", "src")
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if MODEL_SRC_PATH not in sys.path:
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# ===== 新增：将混动模型源码路径加入导入搜索路径 =====
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sys.path.insert(0, MODEL_SRC_PATH)
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def _profile_points(profile_name):
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if profile_name == "高机动阶跃":
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return [
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(0.0, 1600.0, 70.0),
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(8.0, 3200.0, 220.0),
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(20.0, 2500.0, 130.0),
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(35.0, 3400.0, 250.0),
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(50.0, 1800.0, 80.0),
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]
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if profile_name == "经济巡航":
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return [
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(0.0, 1500.0, 60.0),
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(15.0, 2100.0, 95.0),
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(35.0, 2300.0, 105.0),
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(55.0, 2000.0, 90.0),
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]
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return [
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(0.0, 1500.0, 50.0),
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(10.0, 3000.0, 200.0),
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(30.0, 2800.0, 150.0),
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(50.0, 1800.0, 60.0),
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]
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def _target_from_profile(t, points, rpm_scale, load_scale):
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rpm, torque = points[0][1], points[0][2]
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for p_t, p_rpm, p_torque in points:
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if t >= p_t:
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rpm, torque = p_rpm, p_torque
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else:
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break
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return rpm * rpm_scale, torque * load_scale
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def run_case_demo(sim_time_s, dt, initial_soc_pct, initial_engine_power_kw, profile_name, rpm_scale, load_scale):
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try:
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try:
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# ===== 新增：懒加载混动系统模型，便于捕获缺失依赖 =====
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from series_hybrid_sim import SeriesHybridSystem
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except ModuleNotFoundError as e:
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if getattr(e, "name", "") == "torch":
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return None, "算例仿真失败：缺少依赖 torch，请先在当前环境安装 PyTorch。", []
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return None, f"算例仿真失败：缺少依赖 {e.name}。", []
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sim_time_s = float(np.clip(sim_time_s, 10.0, 240.0))
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dt = float(np.clip(dt, 0.01, 0.2))
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initial_soc_pct = float(np.clip(initial_soc_pct, 10.0, 95.0))
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initial_engine_power_kw = float(np.clip(initial_engine_power_kw, 20.0, 260.0))
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rpm_scale = float(np.clip(rpm_scale, 0.5, 1.6))
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load_scale = float(np.clip(load_scale, 0.5, 1.6))
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points = _profile_points(profile_name)
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system = SeriesHybridSystem()
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system.battery.SOC = initial_soc_pct / 100.0
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system.bus_voltage = system.battery._get_ocv(system.battery.SOC)
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system.genset.set_steady_state_by_power(H_env=0.0, Ma_env=0.0, Power_target=initial_engine_power_kw)
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time_array = np.arange(0.0, sim_time_s, dt)
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log = {k: [] for k in [
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"soc", "bus_voltage", "prop_speed_rpm", "target_prop_rpm",
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"target_engine_pwr", "p_engine_out_kw", "p_drive_req_kw",
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"p_batt_actual_kw", "wf_kg_h"
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]}
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for t in time_array:
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target_rpm, load_torque = _target_from_profile(t, points, rpm_scale, load_scale)
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res = system.step(dt, target_rpm, load_torque)
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res["target_prop_rpm"] = target_rpm
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for k in log:
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log[k].append(res[k])
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speed_error = np.array(log["target_prop_rpm"]) - np.array(log["prop_speed_rpm"])
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soc_arr = np.array(log["soc"])
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fuel_arr = np.array(log["wf_kg_h"])
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engine_pwr_arr = np.array(log["p_engine_out_kw"])
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batt_pwr_arr = np.array(log["p_batt_actual_kw"])
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fig, axes = plt.subplots(3, 1, figsize=(12, 10), sharex=True)
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axes[0].plot(time_array, log["target_prop_rpm"], "k--", lw=1.5, label="目标转速")
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axes[0].plot(time_array, log["prop_speed_rpm"], "b-", lw=1.5, label="实际转速")
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axes[0].set_ylabel("RPM")
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axes[0].set_title("推进轴转速响应")
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axes[0].grid(True, linestyle=":")
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axes[0].legend()
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axes[1].plot(time_array, log["p_drive_req_kw"], "k--", lw=1.2, label="电机需求")
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axes[1].plot(time_array, log["p_engine_out_kw"], "r-", lw=1.2, label="发动机输出")
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axes[1].plot(time_array, log["p_batt_actual_kw"], "g-", lw=1.2, label="电池功率")
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axes[1].axhline(0, color="gray", lw=1)
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axes[1].set_ylabel("kW")
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axes[1].set_title("功率分配")
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axes[1].grid(True, linestyle=":")
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axes[1].legend()
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axes[2].plot(time_array, log["bus_voltage"], "m-", lw=1.2, label="母线电压")
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axes[2].set_ylabel("V")
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axes[2].set_xlabel("时间 (s)")
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axes[2].set_title("电气状态")
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axes[2].grid(True, linestyle=":")
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ax_soc = axes[2].twinx()
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ax_soc.plot(time_array, log["soc"], "c--", lw=1.6, label="SOC")
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ax_soc.set_ylabel("SOC (%)")
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fig.tight_layout()
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summary = (
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f"### 算例结果解读\n"
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f"- 仿真时长：{sim_time_s:.1f} s，步长：{dt:.3f} s\n"
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f"- 最大转速误差：{np.max(np.abs(speed_error)):.1f} RPM\n"
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f"- SOC 变化：{soc_arr[0]:.2f}% → {soc_arr[-1]:.2f}%（最小 {np.min(soc_arr):.2f}%）\n"
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f"- 平均发动机输出：{np.mean(engine_pwr_arr):.2f} kW\n"
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f"- 平均电池功率：{np.mean(batt_pwr_arr):.2f} kW\n"
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f"- 平均燃油流量：{np.mean(fuel_arr):.2f} kg/h"
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)
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pick_idx = np.linspace(0, len(time_array) - 1, 8, dtype=int)
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table_data = []
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for idx in pick_idx:
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table_data.append([
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round(float(time_array[idx]), 2),
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round(float(log["target_prop_rpm"][idx]), 1),
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round(float(log["prop_speed_rpm"][idx]), 1),
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round(float(log["p_engine_out_kw"][idx]), 2),
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round(float(log["p_batt_actual_kw"][idx]), 2),
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round(float(log["soc"][idx]), 2),
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])
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return fig, summary, table_data
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except Exception as e:
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return None, f"算例仿真失败：{e}", []
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