{"id":58,"date":"2026-08-15T12:24:10","date_gmt":"2026-08-15T04:24:10","guid":{"rendered":"https:\/\/bmsvr.com\/?p=58"},"modified":"2026-08-15T12:24:10","modified_gmt":"2026-08-15T04:24:10","slug":"%e7%94%b5%e6%b1%a0%e5%af%bf%e5%91%bd%e9%a2%84%e6%b5%8b%e6%a8%a1%e5%9e%8b%e4%b8%8e%e5%8a%a0%e9%80%9f%e8%80%81%e5%8c%96%e6%b5%8b%e8%af%95%e6%96%b9%e6%b3%95","status":"publish","type":"post","link":"https:\/\/bmsvr.com\/?p=58","title":{"rendered":"\u7535\u6c60\u5bff\u547d\u9884\u6d4b\u6a21\u578b\u4e0e\u52a0\u901f\u8001\u5316\u6d4b\u8bd5\u65b9\u6cd5"},"content":{"rendered":"

\u7535\u6c60\u5bff\u547d\u9884\u6d4b\u6a21\u578b\u4e0e\u52a0\u901f\u8001\u5316\u6d4b\u8bd5\u65b9\u6cd5<\/h1>\n
\n

\u7535\u6c60\u5bff\u547d\u9884\u6d4b\uff08Remaining Useful Life, RUL\uff09\u662f\u7535\u6c60\u7ba1\u7406\u7cfb\u7edf\uff08BMS\uff09\u4e2d\u6280\u672f\u95e8\u69db\u6700\u9ad8\u3001\u5de5\u7a0b\u4ef7\u503c\u6700\u5927\u7684\u73af\u8282\u4e4b\u4e00\u3002\u5b83\u76f4\u63a5\u51b3\u5b9a\u6574\u8f66\u7684\u8d28\u4fdd\u7b56\u7565\u3001\u4e8c\u624b\u6b8b\u503c\u8bc4\u4f30\u3001\u50a8\u80fd\u7535\u7ad9\u7684\u8fd0\u7ef4\u8ba1\u5212\u4e0e\u68af\u6b21\u5229\u7528\u7684\u53ef\u884c\u6027\u3002\u672c\u6587\u7cfb\u7edf\u68b3\u7406\u4ece\u7ecf\u9a8c\u6a21\u578b\u5230\u6570\u636e\u9a71\u52a8\u7684\u5bff\u547d\u9884\u6d4b\u65b9\u6cd5\u8c31\u7cfb\uff0c\u6df1\u5165\u89e3\u6790\u52a0\u901f\u8001\u5316\u6d4b\u8bd5\uff08Accelerated Aging Test\uff09\u7684\u8bbe\u8ba1\u903b\u8f91\u3001\u5de5\u51b5\u6784\u9020\u4e0e\u6a21\u578b\u6807\u5b9a\u6d41\u7a0b\uff0c\u4e3a BMS \u7b97\u6cd5\u5de5\u7a0b\u5e08\u63d0\u4f9b\u4e00\u5957\u53ef\u76f4\u63a5\u843d\u5730\u7684\u5bff\u547d\u9884\u6d4b\u4e0e\u6d4b\u8bd5\u9a8c\u8bc1\u6846\u67b6\u3002<\/p>\n<\/blockquote>\n

\n

1. \u4e3a\u4ec0\u4e48\u8981\u505a\u5bff\u547d\u9884\u6d4b\uff1aSOH \u7684\u5de5\u7a0b\u610f\u4e49<\/h2>\n

\u7535\u6c60\u7684”\u5bff\u547d”\u662f\u4e00\u4e2a\u542b\u7cca\u7684\u6982\u5ff5\uff0c\u5fc5\u987b\u88ab\u91cf\u5316\u4e3a\u53ef\u89c2\u6d4b\u3001\u53ef\u9884\u6d4b\u7684\u6307\u6807\u3002\u5de5\u7a0b\u4e0a\u6700\u5e38\u7528\u7684\u662f\u5065\u5eb7\u72b6\u6001\uff08State of Health, SOH\uff09<\/strong>\uff0c\u5176\u6700\u5e38\u89c1\u7684\u5b9a\u4e49\u4e3a\uff1a<\/p>\n

SOH = \u5f53\u524d\u53ef\u7528\u5bb9\u91cf \/ \u989d\u5b9a\u5bb9\u91cf \u00d7 100%<\/strong><\/p>\n

\u6b64\u5916\uff0c\u4e5f\u6709\u4ee5\u76f4\u6d41\u5185\u963b\uff08DCIR\uff09\u6216\u529f\u7387\u80fd\u529b\u5b9a\u4e49\u7684 SOH\u3002BMS \u4e2d\u7684\u5bff\u547d\u9884\u6d4b\u672c\u8d28\u4e0a\u5c31\u662f\u56de\u7b54\u4e00\u4e2a\u95ee\u9898\uff1a\u5728\u5269\u4f59\u7684\u4f7f\u7528\u6761\u4ef6\u4e0b\uff0c\u7535\u6c60\u8fd8\u80fd\u7ef4\u6301\u591a\u5c11\u6b21\u5faa\u73af\u3001\u591a\u5c11\u5e74\u670d\u5f79\uff0c\u624d\u9000\u5316\u5230\u5bff\u547d\u7ec8\u70b9\uff08\u901a\u5e38\u4e3a SOH=80%\uff09\uff1f<\/strong><\/p>\n

\u5bff\u547d\u9884\u6d4b\u5728\u6574\u4e2a\u7535\u6c60\u751f\u547d\u5468\u671f\u4e2d\u626e\u6f14\u4e09\u91cd\u89d2\u8272\uff1a<\/p>\n

    \n
  1. \u5b89\u5168\u89d2\u8272<\/strong>\uff1a\u5bb9\u91cf\u8870\u51cf\u4f34\u968f\u5185\u963b\u4e0a\u5347\u4e0e\u6790\u9502\u98ce\u9669\uff0c\u5bff\u547d\u9884\u6d4b\u662f\u5b89\u5168\u9884\u8b66\u7684\u524d\u7f6e\u73af\u8282\u3002<\/li>\n
  2. \u7ecf\u6d4e\u89d2\u8272<\/strong>\uff1a\u6574\u8f66\u5382\u7535\u6c60\u8d28\u4fdd\uff088 \u5e74\/16 \u4e07\u516c\u91cc\u3001SOH \u2265 70%\uff09\u7684\u5236\u5b9a\u4f9d\u636e\uff0c\u4f4e\u4f30\u5bff\u547d\u610f\u5473\u7740\u5de8\u989d\u7684\u552e\u540e\u4e09\u5305\u6210\u672c\u3002<\/li>\n
  3. \u8fd0\u7ef4\u89d2\u8272<\/strong>\uff1a\u50a8\u80fd\u7535\u7ad9\u9700\u8981\u6839\u636e RUL \u63d0\u524d\u89c4\u5212\u7535\u82af\u66f4\u6362\u4e0e\u5bb9\u91cf\u8865\u507f\uff0c\u68af\u6b21\u5229\u7528\u66f4\u4f9d\u8d56\u7cbe\u51c6\u7684\u5269\u4f59\u5bff\u547d\u8bc4\u4f30\u3002<\/li>\n<\/ol>\n
    \n

    2. \u5bff\u547d\u9884\u6d4b\u65b9\u6cd5\u8c31\u7cfb\uff1a\u4ece\u7ecf\u9a8c\u5230\u673a\u7406\u5230\u6570\u636e<\/h2>\n

    \u5bff\u547d\u9884\u6d4b\u65b9\u6cd5\u53ef\u4ee5\u5206\u6210\u4e09\u5927\u6d41\u6d3e\uff0c\u5b83\u4eec\u5e76\u975e\u4e92\u65a5\uff0c\u5b9e\u9645\u5de5\u7a0b\u4e2d\u5f80\u5f80\u662f”\u6df7\u5408\u4f7f\u7528\u3001\u5206\u7ea7\u51b3\u7b56”\u3002<\/p>\n

    2.1 \u7ecf\u9a8c\/\u534a\u7ecf\u9a8c\u6a21\u578b<\/h3>\n

    \u7ecf\u9a8c\u6a21\u578b\u4e0d\u5173\u5fc3\u7535\u6c60\u5185\u90e8\u53d1\u751f\u4e86\u4ec0\u4e48\uff0c\u53ea\u62df\u5408\u9000\u5316\u8d8b\u52bf\u3002\u6700\u7ecf\u5178\u7684\u662f\u5faa\u73af\u5bff\u547d\u7ecf\u9a8c\u516c\u5f0f<\/strong>\uff1a<\/p>\n

    Q_loss(n) = A \u00d7 n^z\n<\/code><\/pre>\n
    \u5176\u4e2d n<\/code> \u4e3a\u5faa\u73af\u6b21\u6570\uff0cA<\/code> \u4e3a\u6bd4\u4f8b\u7cfb\u6570\uff0cz<\/code> \u4e3a\u5e42\u6307\u6570\uff08\u901a\u5e38 0.5~1.0\uff0c\u53d6\u51b3\u4e8e\u8001\u5316\u673a\u5236\uff09\u3002\u8fd9\u7c7b\u6a21\u578b\u7b80\u5355\u3001\u8ba1\u7b97\u91cf\u5c0f\uff0c\u9002\u5408\u505a\u77ed\u671f\u5916\u63a8\uff0c\u4f46\u65e0\u6cd5\u53cd\u6620\u4e0d\u540c\u5de5\u51b5\uff08\u6e29\u5ea6\u3001\u500d\u7387\u3001SOC \u7a97\u53e3\u3001DOD\uff09\u5bf9\u5bff\u547d\u7684\u5dee\u5f02\u5316\u5f71\u54cd<\/strong>\uff0c\u56e0\u6b64\u7cbe\u5ea6\u6709\u9650\u3002<\/p>\n
    \u8fdb\u4e00\u6b65\u7ec6\u5316\u7684\u662f\u5e42\u5f8b + \u963f\u4f26\u5c3c\u4e4c\u65af\uff08Arrhenius\uff09\u8026\u5408\u6a21\u578b<\/strong>\uff0c\u5c06\u6e29\u5ea6\u3001\u653e\u7535\u6df1\u5ea6\uff08DOD\uff09\u3001\u5145\u653e\u7535\u500d\u7387\uff08C-rate\uff09\u7b49\u56e0\u7d20\u4ee5\u5e94\u529b\u56e0\u5b50\u7684\u5f62\u5f0f\u5f15\u5165\uff1a<\/p>\n
    Q_loss = B \u00d7 exp(-Ea\/(R\u00d7T)) \u00d7 DOD^\u03b1 \u00d7 Crate^\u03b2 \u00d7 n^z\n<\/code><\/pre>\n
    \u8fd9\u662f\u52a0\u901f\u8001\u5316\u6d4b\u8bd5\u4e2d\u5916\u63a8\u6a21\u578b<\/strong>\u7684\u7406\u8bba\u57fa\u7840\u3002<\/p>\n
    2.2 \u673a\u7406\/\u7535\u5316\u5b66\u6a21\u578b<\/h3>\n
    \u673a\u7406\u6a21\u578b\u8bd5\u56fe\u4ece\u7535\u6c60\u5185\u90e8\u7269\u7406\u5316\u5b66\u8fc7\u7a0b\u51fa\u53d1\uff0c\u5efa\u6a21 SEI \u819c\u589e\u957f\u3001\u6d3b\u6027\u7269\u8d28\u635f\u5931\uff08LAM\uff09\u3001\u53ef\u7528\u9502\u79bb\u5b50\u635f\u5931\uff08LLI\uff09\u7b49\u9000\u5316\u673a\u5236\u3002\u4ee3\u8868\u6027\u6846\u67b6\u5305\u62ec\uff1a<\/p>\n
      \n
    • \u5355\u7c92\u5b50\u6a21\u578b\uff08SPM\uff09<\/strong> \u53ca\u6269\u5c55 SPMe<\/strong>\uff1b<\/li>\n
    • \u4f2a\u4e8c\u7ef4\u6a21\u578b\uff08P2D\uff09<\/strong>\uff0c\u7531 Newman \u56e2\u961f\u5efa\u7acb\uff1b<\/li>\n
    • \u9762\u5411 SEI \u589e\u957f\u7684\u9000\u5316\u8026\u5408\u65b9\u7a0b\u3002<\/li>\n<\/ul>\n
      \u673a\u7406\u6a21\u578b\u7684\u4f18\u52bf<\/strong>\u5728\u4e8e\u7269\u7406\u53ef\u89e3\u91ca\u6027\u5f3a\u3001\u5916\u63a8\u53ef\u4fe1\uff0c\u52a3\u52bf<\/strong>\u5728\u4e8e\u53c2\u6570\u8fa8\u8bc6\u56f0\u96be\uff08\u9700\u8981\u7834\u574f\u6027\u6d4b\u8bd5\uff09\u3001\u8ba1\u7b97\u91cf\u5927\uff0c\u96be\u4ee5\u5b9e\u65f6\u8fd0\u884c\u4e8e\u8f66\u89c4\u7ea7 MCU\u3002\u56e0\u6b64\u5b9e\u9645\u4e2d\u673a\u7406\u6a21\u578b\u66f4\u591a\u7528\u4e8e\u79bb\u7ebf\u6807\u5b9a\u4e0e\u4eff\u771f\u9a8c\u8bc1<\/strong>\uff0c\u4e3a\u7ecf\u9a8c\/\u6570\u636e\u6a21\u578b\u63d0\u4f9b\u5148\u9a8c\u3002<\/p>\n
      2.3 \u6570\u636e\u9a71\u52a8\u6a21\u578b<\/h3>\n
      \u6570\u636e\u9a71\u52a8\u65b9\u6cd5\u7ed5\u5f00\u673a\u7406\uff0c\u76f4\u63a5\u4ece\u6d77\u91cf\u5386\u53f2\u6570\u636e\u4e2d\u5b66\u4e60\u9000\u5316\u6620\u5c04\u3002\u6309\u8f93\u5165\u5c3a\u5ea6\u53ef\u5206\u4e3a\uff1a<\/p>\n\n\n\n\n\n\n\n\n
      \u7c7b\u522b<\/th>\n\u4ee3\u8868\u65b9\u6cd5<\/th>\n\u8f93\u5165\u7279\u5f81<\/th>\n\u5178\u578b\u5e94\u7528<\/th>\n<\/tr>\n<\/thead>\n
      \u7edf\u8ba1\u56de\u5f52<\/td>\n\u7ebf\u6027\/\u591a\u9879\u5f0f\u56de\u5f52\u3001\u9ad8\u65af\u8fc7\u7a0b\u56de\u5f52\uff08GPR\uff09<\/td>\n\u5faa\u73af\u6b21\u6570\u3001\u7d2f\u8ba1\u541e\u5410\u91cf<\/td>\n\u5728\u7ebf\u5916\u63a8<\/td>\n<\/tr>\n
      \u673a\u5668\u5b66\u4e60<\/td>\nSVR\u3001\u968f\u673a\u68ee\u6797\u3001XGBoost<\/td>\n\u5bb9\u91cf\/\u5185\u963b\u5386\u53f2\u66f2\u7ebf\u7279\u5f81<\/td>\n\u7279\u5f81\u5de5\u7a0b\u9a71\u52a8<\/td>\n<\/tr>\n
      \u6df1\u5ea6\u5b66\u4e60<\/td>\nLSTM\u3001GRU\u3001TCN\u3001Transformer<\/td>\n\u5145\u653e\u7535\u65f6\u5e8f\uff08V\/I\/T\uff09<\/td>\n\u8f66\u8f7d\u7aef\u5230\u7aef\u9884\u6d4b<\/td>\n<\/tr>\n
      \u6df7\u5408\u6a21\u578b<\/td>\n\u673a\u7406+\u6570\u636e\u878d\u5408\u3001\u8fc1\u79fb\u5b66\u4e60<\/td>\n\u673a\u7406\u53c2\u6570 + \u6570\u636e\u6b8b\u5dee<\/td>\n\u9ad8\u7cbe\u5ea6\u6cdb\u5316<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
      \u6570\u636e\u9a71\u52a8\u7684\u6838\u5fc3\u6311\u6218<\/strong>\u5728\u4e8e\uff1a\u771f\u5b9e\u8f66\u51b5\u4e0b”\u6807\u7b7e”\uff08\u771f\u5b9e\u5bb9\u91cf\uff09\u5f88\u96be\u83b7\u53d6\u2014\u2014\u8f66\u8f86\u6781\u5c11\u7ecf\u5386\u5b8c\u6574\u7684\u6ee1\u5145\u6ee1\u653e\uff0c\u5bb9\u91cf\u53ea\u80fd\u901a\u8fc7\u5b89\u65f6\u79ef\u5206\u79bb\u7ebf\u6807\u5b9a\u3002\u8fd9\u5bfc\u81f4\u4e86\u8457\u540d\u7684”\u6807\u7b7e\u7a00\u7f3a”\u95ee\u9898\uff0c\u4e5f\u662f\u5bff\u547d\u9884\u6d4b\u843d\u5730\u6700\u96be\u7684\u4e00\u73af\u3002<\/p>\n
      \n
      3. \u52a0\u901f\u8001\u5316\u6d4b\u8bd5\u7684\u8bbe\u8ba1\u903b\u8f91<\/h2>\n
      \u5bff\u547d\u9884\u6d4b\u6a21\u578b\u9700\u8981\u6570\u636e\u6807\u5b9a\uff0c\u800c\u83b7\u5f97\u771f\u5b9e\u5bff\u547d\u6570\u636e\u610f\u5473\u7740\u4ee5\u5e74\u8ba1\u7684\u6d4b\u8bd5\u5468\u671f\uff0c\u5de5\u7a0b\u4e0a\u4e0d\u53ef\u63a5\u53d7\u3002\u52a0\u901f\u8001\u5316\u6d4b\u8bd5\uff08Accelerated Aging Test, AAT\uff09<\/strong> \u901a\u8fc7\u63d0\u9ad8\u5e94\u529b\u6c34\u5e73\uff0c\u5728\u77ed\u671f\u5185\u8bf1\u5bfc\u7535\u6c60\u9000\u5316\uff0c\u518d\u7528\u5916\u63a8\u6a21\u578b\u8fd8\u539f\u5230\u6b63\u5e38\u5de5\u4f5c\u6761\u4ef6\u4e0b\u7684\u5bff\u547d\u3002<\/p>\n
      3.1 \u5e94\u529b\u56e0\u5b50\u7684\u9009\u62e9<\/h3>\n
      \u52a0\u901f\u8001\u5316\u901a\u5e38\u65bd\u52a0\u4e09\u7c7b\u5e94\u529b\uff0c\u9700\u8981\u5e73\u8861”\u52a0\u901f\u6548\u679c”\u4e0e”\u5931\u6548\u673a\u5236\u4e0d\u5931\u771f”<\/strong>\u2014\u2014\u5e94\u529b\u8fc7\u9ad8\u53ef\u80fd\u89e6\u53d1\u73b0\u5b9e\u4e2d\u4e0d\u4f1a\u53d1\u751f\u7684\u5931\u6548\u6a21\u5f0f\uff08\u5982\u9ad8\u6e29\u4e0b\u7684\u7535\u89e3\u8d28\u5206\u89e3\uff09\uff0c\u5bfc\u81f4\u5916\u63a8\u5931\u6548\uff1a<\/p>\n\n\n\n\n\n\n\n\n
      \u5e94\u529b\u56e0\u5b50<\/th>\n\u5178\u578b\u8303\u56f4<\/th>\n\u52a0\u901f\u673a\u5236<\/th>\n\u98ce\u9669<\/th>\n<\/tr>\n<\/thead>\n
      \u6e29\u5ea6 T<\/td>\n25\u00b0C~60\u00b0C<\/td>\nSEI \u589e\u957f\u52a0\u901f\uff08\u963f\u4f26\u5c3c\u4e4c\u65af\uff09<\/td>\n\u9ad8\u6e29\u4e0b\u673a\u5236\u6539\u53d8<\/td>\n<\/tr>\n
      \u653e\u7535\u6df1\u5ea6 DOD<\/td>\n20%~100%<\/td>\n\u77f3\u58a8\u9897\u7c92\u673a\u68b0\u5e94\u529b\u3001SEI \u91cd\u6784<\/td>\n\u9ad8 DOD \u673a\u5236\u590d\u6742\u5316<\/td>\n<\/tr>\n
      \u500d\u7387 C-rate<\/td>\n0.5C~3C<\/td>\n\u6790\u9502\u3001\u4ea7\u70ed<\/td>\n\u5927\u500d\u7387\u5f15\u53d1\u6790\u9502<\/td>\n<\/tr>\n
      \u5b58\u50a8 SOC<\/td>\n50%~100%<\/td>\n\u9ad8 SOC \u5b58\u50a8\u52a0\u901f SEI \u589e\u957f<\/td>\n\u9ad8 SOC \u526f\u53cd\u5e94<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n
      3.2 \u52a0\u901f\u6d4b\u8bd5\u7684\u6570\u5b66\u6a21\u578b<\/h3>\n
      \u52a0\u901f\u56e0\u5b50\u7684\u91cf\u5316\u4f9d\u8d56\u963f\u4f26\u5c3c\u4e4c\u65af\u65b9\u7a0b\u4e0e\u7ecf\u9a8c\u5e42\u5f8b\u3002\u5bf9\u6e29\u5ea6\u52a0\u901f\uff0c\u8001\u5316\u901f\u7387\u5e38\u6570\u6ee1\u8db3\uff1a<\/p>\n
      k = A \u00d7 exp(-Ea \/ (R \u00d7 T))\n<\/code><\/pre>\n
      \u52a0\u901f\u56e0\u5b50\uff08Acceleration Factor, AF\uff09<\/strong> \u5b9a\u4e49\u4e3a\u52a0\u901f\u6761\u4ef6\u4e0b\u4e0e\u57fa\u51c6\u6761\u4ef6\u4e0b\u7684\u8001\u5316\u901f\u7387\u4e4b\u6bd4\uff1a<\/p>\n
      AF = exp( (Ea\/R) \u00d7 (1\/T_ref - 1\/T_acc) )\n<\/code><\/pre>\n
      \u5bf9\u591a\u5e94\u529b\u8026\u5408\uff0c\u901a\u5e38\u5047\u8bbe\u5404\u5e94\u529b\u72ec\u7acb\u76f8\u4e58\uff1a<\/p>\n
      AF_total = AF_T \u00d7 AF_DOD \u00d7 AF_Crate\n<\/code><\/pre>\n
      \u8fd9\u4e00”\u72ec\u7acb\u6027\u5047\u8bbe”\u662f\u5916\u63a8\u8bef\u5dee\u7684\u91cd\u8981\u6765\u6e90\uff0c\u5de5\u7a0b\u4e0a\u9700\u901a\u8fc7\u4ea4\u53c9\u5b9e\u9a8c\u8bbe\u8ba1\uff08DOE\uff09<\/strong> \u52a0\u4ee5\u4fee\u6b63\u3002<\/p>\n
      3.3 \u5de5\u51b5\u5256\u9762\u6784\u9020<\/h3>\n
      \u771f\u5b9e\u4f7f\u7528\u5de5\u51b5\u662f\u52a8\u6001\u7684\uff0c\u52a0\u901f\u6d4b\u8bd5\u9700\u8981\u628a\u590d\u6742\u7684\u771f\u5b9e\u5de5\u51b5\u538b\u7f29\u4e3a\u6807\u51c6\u5faa\u73af\u5256\u9762\uff08Drive Profile \/ Duty Cycle\uff09<\/strong>\u3002\u5e38\u89c1\u505a\u6cd5\uff1a<\/p>\n
        \n
      1. \u6052\u5b9a\u53c2\u6570\u5faa\u73af<\/strong>\uff1a\u56fa\u5b9a\u6e29\u5ea6\u3001\u500d\u7387\u3001DOD \u7684\u5355\u4e00\u5256\u9762\uff0c\u6700\u6613\u5916\u63a8\u4f46\u504f\u79bb\u771f\u5b9e\u3002<\/li>\n
      2. \u52a0\u6743\u6807\u51c6\u5de5\u51b5<\/strong>\uff1a\u5982 NEDC\/WLTC \u6298\u7b97\u7684\u7b49\u6548\u5faa\u73af\uff0c\u517c\u987e\u771f\u5b9e\u6027\u4e0e\u53ef\u590d\u73b0\u6027\u3002<\/li>\n
      3. \u52a8\u6001\u5e94\u529b\u6d4b\u8bd5\uff08DST\/UW<\/strong>\uff09**\uff1a\u7528\u529f\u7387\u8c31\u6a21\u62df\u771f\u5b9e\u8d1f\u8f7d\uff0c\u4fdd\u7559\u52a8\u6001\u7279\u6027\u3002<\/li>\n<\/ol>\n
        \n
        \u5173\u952e\u5de5\u7a0b\u6743\u8861<\/strong>\uff1a\u52a0\u901f\u6d4b\u8bd5\u5fc5\u987b\u5728”\u5468\u671f\u53ef\u627f\u53d7”\u4e0e”\u7ed3\u679c\u53ef\u5916\u63a8”\u4e4b\u95f4\u53d6\u5f97\u5e73\u8861\u3002\u4e00\u5473\u5806\u9ad8\u5e94\u529b\u5f97\u5230\u7684\u5bff\u547d\u6570\u636e\uff0c\u5916\u63a8\u5230\u5b9e\u9645\u5de5\u51b5\u65f6\u53ef\u80fd\u4e25\u91cd\u5931\u771f\u3002<\/p>\n<\/blockquote>\n
        \n
        4. \u5bff\u547d\u9884\u6d4b\u7684\u5de5\u7a0b\u843d\u5730\uff1aBMS \u4e2d\u7684 SOH \u4e0e RUL \u7b97\u6cd5<\/h2>\n
        \u628a\u4e0a\u8ff0\u65b9\u6cd5\u642c\u8fdb\u771f\u5b9e BMS\uff0c\u9700\u8981\u89e3\u51b3\u4e09\u4e2a\u5c42\u5c42\u9012\u8fdb\u7684\u95ee\u9898\u3002<\/p>\n
        4.1 \u5728\u7ebf SOH \u4f30\u8ba1<\/h3>\n
        BMS \u65e0\u6cd5\u76f4\u63a5”\u79f0\u91cf”\u5bb9\u91cf\uff0c\u53ea\u80fd\u57fa\u4e8e\u53ef\u89c2\u6d4b\u4fe1\u53f7\u95f4\u63a5\u4f30\u8ba1\u3002\u4e3b\u6d41\u65b9\u6848\uff1a<\/p>\n
        \u5b89\u65f6\u79ef\u5206\u6cd5<\/strong>\uff08\u6700\u76f4\u63a5\u7684\u5bb9\u91cf\u6807\u5b9a\uff09\uff1a<\/p>\n
        SOH = \u222b I dt \/ \u0394SOC \/ C_rated\n<\/code><\/pre>\n
        \u9700\u8981\u4e00\u6b21\u5b8c\u6574\u7684\u5145\u653e\u7535\u533a\u95f4\uff0c\u8f66\u8f7d\u573a\u666f\u96be\u4ee5\u5e38\u6001\u5316\u6ee1\u8db3\uff0c\u901a\u5e38\u4ec5\u5728\u6162\u5145\u6ee1\u5145\u6ee1\u653e\u65f6\u66f4\u65b0\u3002<\/p>\n
        \u57fa\u4e8e OCV-SOC \u7684\u9759\u7f6e\u6cd5<\/strong>\uff1a\u5229\u7528\u957f\u65f6\u95f4\u9759\u7f6e\u540e\u7684\u5f00\u8def\u7535\u538b\u6620\u5c04 SOC \u5dee\u5f02\uff0c\u95f4\u63a5\u8ba1\u7b97\u5bb9\u91cf\uff0c\u9700\u8f83\u957f\u9759\u7f6e\u65f6\u95f4\u3002<\/p>\n
        \u57fa\u4e8e\u5185\u963b\u7684\u4f30\u8ba1<\/strong>\uff1aDCIR \u968f\u8001\u5316\u5355\u8c03\u4e0a\u5347\uff0c\u53ef\u901a\u8fc7\u8109\u51b2\u5de5\u51b5\u5728\u7ebf\u8fa8\u8bc6\u5185\u963b\uff0c\u8fdb\u800c\u6620\u5c04 SOH\u3002\u5185\u963b\u6cd5\u54cd\u5e94\u5feb\u4f46\u53d7\u6e29\u5ea6\u3001SOC \u5f71\u54cd\u5927\uff0c\u9700\u4e25\u683c\u4fee\u6b63\u3002<\/p>\n
        4.2 \u9000\u5316\u7279\u5f81\uff08Health Indicator, HI\uff09\u63d0\u53d6<\/h3>\n
        \u5bff\u547d\u9884\u6d4b\u6a21\u578b\u4e0d\u80fd\u76f4\u63a5\u7528\u5bb9\u91cf\u505a\u5b9e\u65f6\u8f93\u5165\uff0c\u9700\u8981\u4ece\u6bcf\u6b21\u5145\u653e\u7535\u4e2d\u63d0\u53d6\u5065\u5eb7\u6307\u793a\u56e0\u5b50 HI<\/strong>\uff0c\u5178\u578b\u7684\u6709\uff1a<\/p>\n
          \n
        • \u6052\u6d41\u5145\u7535\u6bb5\u7535\u538b\u66f2\u7ebf\u7279\u5f81\uff08\u5982\u7b49\u538b\u5dee\u5145\u7535\u65f6\u95f4 \u0394t_V\uff09\uff1b<\/li>\n
        • \u5145\u7535\u5bb9\u91cf\u66f2\u7ebf\u62d0\u70b9\u4f4d\u7f6e\uff1b<\/li>\n
        • \u5185\u963b\u589e\u91cf\uff1b<\/li>\n
        • \u653e\u7535\u7535\u538b\u5e73\u53f0\u659c\u7387\u3002<\/li>\n<\/ul>\n
          \u8fd9\u4e9b HI \u4e0e\u5bb9\u91cf\u9000\u5316\u9ad8\u5ea6\u76f8\u5173\uff0c\u4e14\u53ef\u5728\u65e5\u5e38\u5145\u7535\u4e2d\u4f4e\u6210\u672c\u83b7\u53d6\uff0c\u662f\u6570\u636e\u9a71\u52a8 RUL \u9884\u6d4b\u7684\u7279\u5f81\u6765\u6e90\u3002\u589e\u91cf\u5bb9\u91cf\u5206\u6790\uff08ICA\uff09<\/strong> \u4e0e \u5dee\u5206\u7535\u538b\u5206\u6790\uff08DVA\uff09<\/strong> \u662f\u63d0\u53d6 HI \u7684\u4e24\u5927\u7ecf\u5178\u4fe1\u53f7\u5904\u7406\u624b\u6bb5\u3002<\/p>\n
          4.3 RUL \u9884\u6d4b\u4e0e\u7f6e\u4fe1\u533a\u95f4<\/h3>\n
          RUL \u9884\u6d4b\u4e0d\u4ec5\u7ed9\u51fa\u70b9\u4f30\u8ba1\uff0c\u8fd8\u9700\u7ed9\u51fa\u7f6e\u4fe1\u533a\u95f4<\/strong>\u4ee5\u652f\u6301\u8d28\u4fdd\u51b3\u7b56\u3002\u9ad8\u65af\u8fc7\u7a0b\u56de\u5f52\uff08GPR\uff09\u5929\u7136\u63d0\u4f9b\u9884\u6d4b\u65b9\u5dee\uff0c\u662f\u7535\u6c60 RUL \u9884\u6d4b\u7684\u4f18\u9009\u65b9\u6cd5\u3002\u4e00\u4e2a\u5178\u578b\u7684 RUL \u9884\u6d4b\u6d41\u7a0b\uff1a<\/p>\n
          \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510   \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510   \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n\u2502 \u63d0\u53d6 HI \u5e8f\u5217 \u2502 \u2192 \u2502 \u62df\u5408\u9000\u5316\u6a21\u578b   \u2502 \u2192 \u2502 \u5916\u63a8\u81f3\u9608\u503c    \u2502\n\u2502 (V\/I\/T \u66f2\u7ebf) \u2502   \u2502 (GPR\/\u6307\u6570\u6a21\u578b) \u2502   \u2502 SOH=80%     \u2502\n\u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518   \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518   \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n                                          \u2193\n                                  \u250c\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2510\n                                  \u2502 RUL + \u7f6e\u4fe1\u533a\u95f4 \u2502\n                                  \u2514\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2500\u2518\n<\/code><\/pre>\n
          \n
          5. \u4e00\u4e2a\u53ef\u843d\u5730\u7684\u5b9e\u73b0\u6846\u67b6\uff08Python \u793a\u4f8b\uff09<\/h2>\n
          \u4ee5\u4e0b\u7ed9\u51fa\u4e00\u4e2a\u57fa\u4e8e\u6307\u6570\u9000\u5316\u6a21\u578b + \u7b80\u5355\u5916\u63a8\u7684 RUL \u9884\u6d4b\u9aa8\u67b6\uff0c\u4fbf\u4e8e BMS \u7b97\u6cd5\u5de5\u7a0b\u5e08\u7406\u89e3\u6838\u5fc3\u903b\u8f91\uff1a<\/p>\n
          import numpy as np\n\ndef exp_degradation(x, a, b, c):\n    """\u6307\u6570\u9000\u5316\u6a21\u578b: SOH = a*exp(b*x) + c"""\n    return a * np.exp(b * x) + c\n\ndef predict_rul(cycles, soh, threshold=0.8):\n    """\n    cycles: \u5df2\u89c2\u6d4b\u7684\u5faa\u73af\u6b21\u6570\u6570\u7ec4\n    soh:    \u5bf9\u5e94\u7684 SOH \u5e8f\u5217 (0~1)\n    threshold: \u5bff\u547d\u7ec8\u70b9 SOH\n    \u8fd4\u56de: \u9884\u6d4b RUL (\u5faa\u73af\u6b21\u6570) \u4e0e\u62df\u5408\u6b8b\u5dee\n    """\n    from scipy.optimize import curve_fit\n    popt, _ = curve_fit(exp_degradation, cycles, soh,\n                        p0=[1.0, -0.001, 0.0], maxfev=10000)\n    a, b, c = popt\n    # \u6c42\u89e3 SOH \u8fbe\u5230\u9608\u503c\u7684\u5faa\u73af\u6b21\u6570\n    # a*exp(b*n) + c = threshold  =>  n = ln((threshold-c)\/a)\/b\n    if (threshold - c) \/ a > 0:\n        cycle_end = np.log((threshold - c) \/ a) \/ b\n        rul = max(0, cycle_end - cycles[-1])\n    else:\n        rul = np.nan\n    resid = np.std(exp_degradation(cycles, *popt) - soh)\n    return rul, popt, resid, cycle_end\n\n# \u793a\u4f8b\u6570\u636e\uff08\u6a21\u62df\u9000\u5316\uff09\ncycles = np.arange(0, 500, 50)\nsoh = 0.95 * np.exp(-0.0004 * cycles) + 0.03\n\nrul, params, resid, end_cycle = predict_rul(cycles, soh)\nprint(f"\u9884\u4f30\u5bff\u547d\u7ec8\u70b9\u5faa\u73af\u6570: {end_cycle:.0f}")\nprint(f"\u5269\u4f59\u53ef\u7528\u5faa\u73af (RUL): {rul:.0f}")\nprint(f"\u62df\u5408\u6b8b\u5dee\u6807\u51c6\u5dee: {resid:.4f}")\n<\/code><\/pre>\n
          \n
          \u5de5\u7a0b\u63d0\u793a<\/strong>\uff1a\u771f\u5b9e\u573a\u666f\u4e2d\uff0c\u6307\u6570\u6a21\u578b\u53ea\u662f\u9000\u5316\u8d8b\u52bf\u7684\u4e00\u9636\u8fd1\u4f3c\u3002\u66f4\u7a33\u5065\u7684\u65b9\u6848\u662f\u5f15\u5165\u5206\u6bb5\u6a21\u578b<\/strong>\uff08\u524d\u671f\u7f13\u964d\u3001\u540e\u671f\u9661\u964d\u7684”\u62d0\u70b9”\u6548\u5e94\uff09\uff0c\u6216\u91c7\u7528 GPR \u8fd9\u7c7b\u80fd\u7ed9\u51fa\u4e0d\u786e\u5b9a\u6027\u5ea6\u91cf\u7684\u65b9\u6cd5\u3002\u6b64\u5916\uff0c\u52a1\u5fc5\u5bf9 HI \u505a\u6e29\u5ea6\u4e0e SOC \u4fee\u6b63<\/strong>\u540e\u518d\u5efa\u6a21\uff0c\u5426\u5219\u8de8\u5de5\u51b5\u6cdb\u5316\u80fd\u529b\u4f1a\u5f88\u5dee\u3002<\/p>\n<\/blockquote>\n
          \n
          6. \u52a0\u901f\u8001\u5316\u6d4b\u8bd5\u7684\u6570\u636e\u5916\u63a8\u4e0e\u9a8c\u8bc1<\/h2>\n
          \u52a0\u901f\u6d4b\u8bd5\u7684\u6700\u7ec8\u76ee\u6807\u662f\u5916\u63a8\u771f\u5b9e\u5bff\u547d\uff0c\u5916\u63a8\u94fe\u6761\u7684\u8d28\u91cf\u51b3\u5b9a\u4e86\u9884\u6d4b\u7684\u53ef\u4fe1\u5ea6\u3002\u5de5\u7a0b\u4e0a\u9700\u8981\u5173\u6ce8\uff1a<\/p>\n
            \n
          1. \u673a\u5236\u4e00\u81f4\u6027\u6821\u9a8c<\/strong>\uff1a\u901a\u8fc7\u5bb9\u91cf\u589e\u91cf\u5206\u6790\uff08ICA\uff09\u6bd4\u5bf9\u52a0\u901f\u6837\u672c\u4e0e\u6b63\u5e38\u6837\u672c\u7684\u9000\u5316\u673a\u5236\u662f\u5426\u4e00\u81f4\uff0c\u82e5 ICA \u5cf0\u4f4d\/\u5cf0\u5f62\u51fa\u73b0\u8d28\u53d8\uff0c\u8bf4\u660e\u5931\u6548\u673a\u5236\u6539\u53d8\uff0c\u5916\u63a8\u9700\u8c28\u614e\u3002<\/li>\n
          2. \u5916\u63a8\u6a21\u578b\u9a8c\u8bc1<\/strong>\uff1a\u4fdd\u7559\u4e00\u7ec4”\u57fa\u51c6\u5de5\u51b5”\u6837\u672c\u4f5c\u4e3a\u9a8c\u8bc1\u96c6\uff0c\u68c0\u9a8c\u52a0\u901f\u5916\u63a8\u7ed3\u679c\u7684\u504f\u5dee\uff0c\u91cf\u5316\u6a21\u578b\u8bef\u5dee\u3002<\/li>\n
          3. \u591a\u5e94\u529b\u4ea4\u53c9\u9a8c\u8bc1<\/strong>\uff1a\u901a\u8fc7 DOE \u5398\u6e05\u5e94\u529b\u95f4\u7684\u4ea4\u4e92\u4f5c\u7528\uff0c\u4fee\u6b63\u72ec\u7acb\u6027\u5047\u8bbe\u5e26\u6765\u7684\u7cfb\u7edf\u6027\u504f\u5dee\u3002<\/li>\n<\/ol>\n
            \u52a0\u901f\u8001\u5316\u6d4b\u8bd5\u7684\u8fb9\u754c<\/strong>\uff1a\u5b83\u7ed9\u6211\u4eec\u7684\u662f”\u76f8\u5bf9\u5bff\u547d\u6392\u5e8f”\u4e0e”\u5bff\u547d\u91cf\u7ea7”\u7684\u53ef\u9760\u4fe1\u606f\uff0c\u800c\u975e\u7edd\u5bf9\u7cbe\u786e\u7684\u5bff\u547d\u6570\u5b57\u3002\u5c06\u52a0\u901f\u6570\u636e\u6298\u7b97\u4e3a\u771f\u5b9e\u7684\u8d28\u4fdd\u5e74\u9650\u65f6\uff0c\u5e94\u7ed9\u51fa\u4fdd\u5b88\u7684\u5b89\u5168\u7cfb\u6570<\/strong>\u3002<\/p>\n
            \n
            7. \u5c55\u671b\uff1a\u4ece\u6d4b\u8bd5\u53f0\u5230\u8f66\u7aef\u7684\u95ed\u73af<\/h2>\n
            \u5bff\u547d\u9884\u6d4b\u7684\u4ef7\u503c\u6700\u7ec8\u8981\u56de\u5230”\u8f66\u2014\u4e91\u2014\u5b9e\u9a8c\u5ba4”\u7684\u95ed\u73af\u4e2d\u91ca\u653e\uff1a<\/p>\n
              \n
            • \u8f66\u7aef<\/strong>\uff1a\u8f7b\u91cf\u7ea7 SOH \u4f30\u8ba1\u4e0e HI \u63d0\u53d6\uff0c\u4fdd\u8bc1\u5b9e\u65f6\u6027\u4e0e\u4f4e\u7b97\u529b\u5f00\u9500\uff1b<\/li>\n
            • \u4e91\u7aef<\/strong>\uff1a\u6c47\u96c6\u8f66\u961f\u7ea7\u8fd0\u884c\u6570\u636e\uff0c\u7528\u5927\u6570\u636e\u4fee\u6b63\u6a21\u578b\u504f\u5dee\uff0c\u5b9e\u73b0”\u7fa4\u4f53\u5b66\u4e60”\uff1b<\/li>\n
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