固态电池：从“PPT参数”到“车规级量产前夜”

"""
solid_state_route_compare.py
对比2026年固态电池三大技术路线的量产就绪度
"""
import numpy as np
import matplotlib.pyplot as plt

ROUTES = {
    "半固态(氧化物+少量液)": {"energy_density": 0.75, "safety": 0.85, "manufacturability": 0.80, "cost_parity": 0.70, "cycle_life": 0.80},
    "硫化物全固态":          {"energy_density": 0.95, "safety": 0.95, "manufacturability": 0.40, "cost_parity": 0.25, "cycle_life": 0.60},
    "聚合物全固态":          {"energy_density": 0.55, "safety": 0.90, "manufacturability": 0.85, "cost_parity": 0.80, "cycle_life": 0.70},
}

def radar_plot(data):
    labels = list(next(iter(data.values())).keys())
    n = len(labels)
    angles = np.linspace(0, 2 * np.pi, n, endpoint=False).tolist()
    angles += angles[:1]

    fig, ax = plt.subplots(figsize=(8, 8), subplot_kw={"polar": True})
    colors = ["#FF9800", "#E91E63", "#4CAF50"]
    for (name, scores), color in zip(data.items(), colors):
        values = [scores[k] for k in labels]
        values += 3015.baike.tongsou.com 
        ax.plot(angles, values, linewidth=2.5, label=name, color=color)
        ax.fill(angles, values, alpha=0.12, color=color)

    ax.set_xticks(angles[:-1])
    ax.set_xticklabels(["能量密度", "安全性", "可制造性", "成本平价度", "循环寿命"], fontsize=11)
    ax.set_ylim(0, 1)
    ax.set_title("2026 固态电池技术路线量产就绪度", pad=25, fontsize=13)
    ax.legend(loc="upper right", bbox_to_anchor=(1.35, 1.05), fontsize=10)
    plt.tight_layout()
    plt.savefig("ssb_radar.png", dpi=150, bbox_inches="tight")
    plt.show( 2035.baike.tongsou.com )

if __name__ == "__main__":
    radar_plot(ROUTES)
    best_mfg = max(ROUTES, key=lambda r: ROUTES[r]["manufacturability"])
    print(f"当前最易量产路线：{best_mfg}（可制造性={ROUTES[best_mfg]['manufacturability']:.2f}）")

"""
interface_pressure_analysis.py
量化固态电池量产中界面阻抗与外部压力的关系及良率影响
"""
import numpy as np
import matplotlib.pyplot as plt

# 不同压力下界面阻抗与循环衰减数据（归一化）
PRESSURES_MPA = [5, 10, 20, 30, 50, 100]
INTERFACE_RESISTANCE = [1.0, 0.65, 0.40, 0.28, 0.22, 0.20]  # 相对值
CYCLE_RETENTION_1000 = [0.45, 0.68, 0.82, 0.89, 0.91, 0.90]  # 1000圈容量保持率
YIELD_RATE = [0.30, 0.55, 0.78, 0.88, 0.85, 0.70]           # 电芯良率

def plot_interface():
    fig, ax1 = plt.subplots(figsize=(12, 7))

    color_res = "#E91E63"
    color_ret = "#2196F3"
    color_yield = "#4CAF50"

    ax1.set_xlabel("外部施加压力 (MPa)", fontsize=12)
    ax1.set_ylabel("归一化阻抗 / 容量保持率", fontsize=12)

    l1, = ax1.plot(PRESSURES_MPA, INTERFACE_RESISTANCE, "o-", color=color_res, linewidth=2, label="界面阻抗")
    l2, = ax1.plot(PRESSURES_MPA, CYCLE_RETENTION_1000, "s--", color=color_ret, linewidth=2, label="1000圈容量保持率")
    ax1.set_ylim(0, 1.1)
    ax1.grid(True, alpha=0.3)

    ax2 = ax1.twinx()
    l3, = ax2.plot(PRESSURES_MPA, YIELD_RATE, "^:", color=color_yield, linewidth=2, markersize=8, label="电芯良率")
    ax2.set_ylabel("电芯良率", fontsize=12, color=color_yield)
    ax2.set_ylim( 3010.baike.tongsou.com )
    ax2.tick_params(axis="y", labelcolor=color_yield)

    lines = [l1, l2, l3]
    labels = [l.get_label() for l in lines]
    ax1.legend(lines, labels, loc="center right", fontsize=11)

    # 标注最优压力区间
    ax1.axvspan(20, 30, alpha=0.15, color="gold")
    ax1.text(25, 0.95, "★ 量产最优压力窗口\n(良率>85%, 阻抗<0.3)", ha="center", fontsize=10, color="#B8860B")

    ax1.set_title("固态电池界面性能 vs 压力 vs 良率（硫化物体系）", fontsize=13)
    plt.tight_layout( 2043.baike.tongsou.com )
    plt.savefig("interface_pressure.png", dpi=150, bbox_inches="tight")
    plt.show( 2041.baike.tongsou.com )

if __name__ == "__main__":
    plot_interface( 2042.baike.tongsou.com )
    optimal_idx = np.argmax(YIELD_RATE)
    print(f"良率峰值压力: {PRESSURES_MPA[optimal_idx]} MPa, 良率={YIELD_RATE[optimal_idx]:.0%}")

"""
ssb_cost_breakdown.py
拆解固态电池成本结构及2026-2030降本路径
"""
import matplotlib.pyplot as plt

# 2026年半固态 vs 液态三元电池成本构成（元/Wh）
COST_2026 = {
    "正极材料":     {"液态": 0.38, "半固态": 0.40},
    "负极材料":     {"液态": 0.12, "半固态": 0.18},  # 硅碳负极更贵
    "电解质":       {"液态": 0.08, "半固态": 0.22},  # 固态电解质成本高
    "隔膜/界面层":  {"液态": 0.05, "半固态": 0.10},  # 复合隔膜+涂层
    "制造费用":     {"液态": 0.15, "半固态": 0.25},  # 新工序、低良率
    "其他":         {"液态": 0.07, "半固态": 0.10},
}

# 降本驱动因素贡献（2026→2030累计降幅占比）
COST_REDUCTION_DRIVERS = {
    "电解质规模化生产": 35,
    "硅碳负极国产替代": 20,
    "干法电极工艺导入": 18,
    "良率提升(80%→95%)": 15,
    "设备折旧摊薄":     12,
}

def plot_cost():
    fig, axes = plt.subplots(1, 2, figsize=(16, 7))

    # 左图：成本堆叠柱状图
    ax1 = 2040.baike.tongsou.com 
    cats = list(COST_2026.keys())
    liquid_vals = [COST_2026[c]["液态"] for c in cats]
    ss_vals = [COST_2026[c]["半固态"] for c in cats]

    x = range(len(cats))
    ax1.bar(x, liquid_vals, width=0.35, label="液态三元", color="#90CAF9", edgecolor="#fff")
    ax1.bar([i + 0.35 for i in x], ss_vals, width=0.35, label="半固态", color="#FFAB91", edgecolor="#fff")
    ax1.set_xticks([i + 0.175 for i in x])
    ax1.set_xticklabels(cats, fontsize=9, rotation=30, ha="right")
    ax1.set_ylabel("成本 (元/Wh)", fontsize=11)
    ax1.set_title("2026年电池成本结构对比", fontsize=13)
    ax1.legend( 2037.baike.tongsou.com 2039.baike.tongsou.com )
    total_liq = sum(liquid_vals)
    total_ss = sum(ss_vals)
    ax1.text(len(cats)-1, total_liq+0.02, f"总计:{total_liq:.2f}", ha="center", fontsize=9)
    ax1.text(len(cats)-1+0.35, total_ss+0.02, f"总计:{total_ss:.2f}", ha="center", fontsize=9)

    # 右图：降本驱动力饼图
    ax2 = axes[1]
    drivers = list(COST_REDUCTION_DRIVERS.keys())
    shares = list(COST_REDUCTION_DRIVERS.values())
    colors = ["#E91E63", "#2196F3", "#FF9800", "#4CAF50", "#9C27B0"]
    wedges, texts, autotexts = ax2.pie(shares, labels=drivers, autopct="%1.0f%%",
                                        colors=colors, startangle=90, textprops={"fontsize": 9})
    ax2.set_title("2026→2030 固态电池降本驱动力分布", fontsize=13)

    plt.tight_layout()
    plt.savefig("ssb_cost.png", dpi=150, bbox_inches="tight")
    plt.show( 2036.baike.tongsou.com )

if __name__ == "__main__":
    plot_cost()
    premium = (sum(COST_2026[c]["半固态"] for c in COST_2026) /
               sum(COST_2026[c]["液态"] for c in COST_2026) - 1) * 100
    print(f"2026年半固态成本溢价: {premium:.0f}%")