{"id":75,"date":"2026-08-26T18:33:39","date_gmt":"2026-08-26T10:33:39","guid":{"rendered":"https:\/\/bmsvr.com\/?p=75"},"modified":"2026-08-26T18:33:39","modified_gmt":"2026-08-26T10:33:39","slug":"%e6%a2%af%e6%ac%a1%e5%88%a9%e7%94%a8%e7%94%b5%e6%b1%a0%e5%88%86%e9%80%89%e4%b8%8e%e4%b8%80%e8%87%b4%e6%80%a7%e8%af%84%e4%bc%b0%ef%bc%9abms-%e5%b7%a5%e7%a8%8b%e5%b8%88%e7%9a%84%e5%b7%a5%e7%a8%8b","status":"publish","type":"post","link":"https:\/\/bmsvr.com\/?p=75","title":{"rendered":"\u68af\u6b21\u5229\u7528\u7535\u6c60\u5206\u9009\u4e0e\u4e00\u81f4\u6027\u8bc4\u4f30\uff1aBMS \u5de5\u7a0b\u5e08\u7684\u5de5\u7a0b\u843d\u5730\u65b9\u6cd5"},"content":{"rendered":"
\n

title: “\u68af\u6b21\u5229\u7528\u7535\u6c60\u5206\u9009\u4e0e\u4e00\u81f4\u6027\u8bc4\u4f30\uff1aBMS \u5de5\u7a0b\u5e08\u7684\u5de5\u7a0b\u843d\u5730\u65b9\u6cd5”
\ndate: “2026-08-26”
\ncategory: “\u7535\u6c60\u6280\u672f”
\ntags: [“\u68af\u6b21\u5229\u7528”, “\u7535\u6c60\u5206\u9009”, “\u4e00\u81f4\u6027\u8bc4\u4f30”, “BMS”]
\nslug: “echelon-battery-screening-consistency”<\/p>\n

\n

\u5f15\u8a00<\/h2>\n

\u4e0a\u4e00\u7bc7\u6587\u7ae0\u6211\u4eec\u4ece\u4ea7\u4e1a\u5c42\u9762\u804a\u4e86”\u4e24\u6b21\u751f\u547d”\u6a21\u578b\uff1a\u52a8\u529b\u7535\u6c60\u9000\u5f79\u540e\u8fdb\u5165\u50a8\u80fd\u3001\u4f4e\u901f\u8f66\u3001\u901a\u4fe1\u5907\u7535\u7b49\u573a\u666f\u3002\u4f46\u771f\u6b63\u8ba9\u8fd9\u4e2a\u6a21\u578b\u8dd1\u8d77\u6765\u7684\uff0c\u662f\u4e00\u6574\u5957\u85cf\u5728\u540e\u53f0\u7684\u5206\u9009\u4e0e\u4e00\u81f4\u6027\u8bc4\u4f30<\/strong>\u6280\u672f\u3002<\/p>\n

\u5bf9\u4e8e\u76f4\u63a5\u9762\u5bf9\u88f8\u7535\u82af\u7684 BMS \u5de5\u7a0b\u5e08\u6765\u8bf4\uff0c\u68af\u6b21\u5229\u7528\u6700\u68d8\u624b\u7684\u95ee\u9898\u4e0d\u662f”\u80fd\u4e0d\u80fd\u7528”\uff0c\u800c\u662f”\u8fd9\u4e00\u6279\u9000\u5f79\u7535\u82af\u51d1\u5230\u4e00\u8d77\uff0c\u653e\u7535\u653e\u5230 80% SOC \u7684\u65f6\u5019\uff0c\u538b\u5dee\u4f1a\u4e0d\u4f1a\u98d9\u5230 300 mV \u4ee5\u4e0a”\u3002<\/p>\n

\u672c\u6587\u4e0d\u8bb2\u4ea7\u4e1a\u89c4\u6a21\uff0c\u53ea\u8c08\u5de5\u7a0b\u843d\u5730\uff1a\u9000\u5f79\u7535\u82af\u5230\u5e95\u600e\u4e48\u7b5b\u3001\u600e\u4e48\u5206\u7fa4\u3001\u600e\u4e48\u5728 BMS \u91cc\u6301\u7eed\u8bc4\u4f30\u4e00\u81f4\u6027\u3002\u6587\u4e2d\u7684\u65b9\u6cd5\u548c\u516c\u5f0f\u90fd\u662f\u73b0\u573a\u53ef\u590d\u7528\u7684\u3002<\/p>\n

<\/p>\n

\u4e00\u3001\u4e3a\u4ec0\u4e48\u68af\u6b21\u7535\u82af\u7684\u4e00\u81f4\u6027\u5929\u751f\u5c31\u5dee<\/h2>\n

\u4e00\u5757\u65b0\u7535\u6c60\u51fa\u5382\u65f6\uff0c\u5355\u4f53\u4e4b\u95f4\u7684\u4e00\u81f4\u6027\u9760\u7684\u662f\u540c\u6279\u6b21\u6750\u6599\u548c\u4e25\u683c\u5de5\u827a\u3002\u800c\u9000\u5f79\u7535\u82af\u6765\u81ea\u4e0d\u540c\u8f66\u3001\u4e0d\u540c\u5e74\u9650\u3001\u4e0d\u540c\u5145\u653e\u7535\u4e60\u60ef\uff0c\u4e2a\u4f53\u5dee\u5f02\u88ab\u653e\u5927\u4e86\u51e0\u4e2a\u6570\u91cf\u7ea7\u3002<\/p>\n

\u9000\u5f79\u7535\u82af\u7684\u4e00\u81f4\u6027\u5dee\u4e3b\u8981\u4f53\u73b0\u5728\u56db\u4e2a\u7ef4\u5ea6\uff1a<\/p>\n

    \n
  1. \u5bb9\u91cf\u8870\u51cf\u7a0b\u5ea6\u4e0d\u4e00<\/strong>\uff1a\u540c\u4e00\u6279\u56de\u6536\u7535\u82af\uff0c\u5269\u4f59\u5bb9\u91cf\u53ef\u80fd\u4ece 60% \u5230 85% \u4e0d\u7b49\u3002<\/li>\n
  2. \u5185\u963b\u79bb\u6563<\/strong>\uff1a\u5185\u963b\u51b3\u5b9a\u4e86\u53d1\u70ed\u548c\u529f\u7387\u6027\u80fd\uff0c\u8001\u5316\u7535\u82af\u7684\u76f4\u6d41\u5185\u963b\uff08DCR\uff09\u53ef\u80fd\u76f8\u5dee 50% \u4ee5\u4e0a\u3002<\/li>\n
  3. \u81ea\u653e\u7535\u7387\u5dee\u5f02<\/strong>\uff1a\u8fd9\u662f\u6700\u5bb9\u6613\u88ab\u5ffd\u89c6\u7684\uff0c\u4e5f\u662f\u68af\u6b21\u7535\u6c60\u7ec4”\u653e\u7740\u653e\u7740\u5c31\u6ca1\u7535”\u6216”\u8fc7\u51e0\u4e2a\u6708\u538b\u5dee\u53d8\u5927”\u7684\u5143\u51f6\u3002<\/li>\n
  4. \u5f00\u8def\u7535\u538b\uff08OCV\uff09\u66f2\u7ebf\u5f62\u53d8<\/strong>\uff1a\u8001\u5316\u5bfc\u81f4 OCV-SOC \u66f2\u7ebf\u6574\u4f53\u504f\u79fb\u751a\u81f3\u7578\u53d8\uff0c\u76f4\u63a5\u5f71\u54cd SOC \u4f30\u7b97\u57fa\u51c6\u3002<\/li>\n<\/ol>\n

    \u8fd9\u56db\u4e2a\u7ef4\u5ea6\u91cc\uff0c\u524d\u4e24\u4e2a\u53ef\u4ee5\u5728\u9759\u6001\u5206\u9009\u9636\u6bb5\u6d4b\u5f97\u4e03\u4e03\u516b\u516b\uff0c\u540e\u4e24\u4e2a\u5fc5\u987b\u5728\u52a8\u6001\u8bc4\u4f30\u548c\u6210\u7ec4\u540e\u7684 BMS \u91cc\u6301\u7eed\u8ddf\u8e2a\u3002<\/p>\n

    \u4e8c\u3001\u5206\u9009\u7684\u7b2c\u4e00\u6b65\uff1a\u5148\u505a”\u4f53\u68c0”\uff0c\u518d\u8c08”\u914d\u5bf9”<\/h2>\n

    \u5206\u9009\u7684\u6838\u5fc3\u903b\u8f91\u4e00\u53e5\u8bdd\uff1a\u5148\u628a\u6bcf\u4e00\u9897\u7535\u82af\u7684\u53ef\u91cf\u5316\u7279\u5f81\u6d4b\u51fa\u6765\uff0c\u518d\u6309\u7279\u5f81\u805a\u7c7b\u5206\u7ec4<\/strong>\u3002\u8df3\u8fc7\u4f53\u68c0\u76f4\u63a5\u6309”\u770b\u7740\u8fd8\u884c”\u6210\u7ec4\uff0c\u662f\u68af\u6b21\u9879\u76ee\u5931\u8d25\u6700\u5e38\u89c1\u7684\u539f\u56e0\u3002<\/p>\n

    \u4f53\u68c0\u9636\u6bb5\u81f3\u5c11\u8981\u62ff\u5230\u4ee5\u4e0b\u6570\u636e\uff0c\u6bcf\u9897\u7535\u82af\u4e00\u6761\u8bb0\u5f55\uff1a<\/p>\n\n\n\n\n\n\n\n\n
    \u7279\u5f81<\/th>\n\u6d4b\u8bd5\u65b9\u6cd5<\/th>\n\u5de5\u7a0b\u610f\u4e49<\/th>\n<\/tr>\n<\/thead>\n
    \u5269\u4f59\u5bb9\u91cf C<\/td>\n\u6807\u51c6\u5145\u653e\u7535\u5faa\u73af<\/td>\n\u51b3\u5b9a\u6210\u7ec4\u540e\u7684\u603b\u80fd\u91cf\u4e0a\u9650<\/td>\n<\/tr>\n
    \u76f4\u6d41\u5185\u963b DCR<\/td>\n\u8109\u51b2\u653e\u7535\uff08\u5982 1C 10s\uff09<\/td>\n\u51b3\u5b9a\u53d1\u70ed\u4e0e\u5cf0\u503c\u529f\u7387<\/td>\n<\/tr>\n
    \u81ea\u653e\u7535\u7387 k<\/td>\n\u6ee1\u7535\u9759\u7f6e 7~14 \u5929\u6d4b\u538b\u964d<\/td>\n\u51b3\u5b9a\u957f\u671f\u9759\u7f6e\u540e\u7684\u538b\u5dee\u6076\u5316\u901f\u5ea6<\/td>\n<\/tr>\n
    OCV \u66f2\u7ebf<\/td>\n\u5c0f\u7535\u6d41\u6d3b\u5316\u540e\u9010\u70b9\u8bfb\u53d6<\/td>\n\u51b3\u5b9a SOC\/SOP \u4f30\u7b97\u7cbe\u5ea6<\/td>\n<\/tr>\n<\/tbody>\n<\/table>\n

    \u5176\u4e2d DCR \u7684\u8109\u51b2\u6d4b\u8bd5\u6709\u4e2a\u5173\u952e\u7ec6\u8282\uff1a\u653e\u7535\u8109\u51b2\u7ed3\u675f\u540e\u7684\u7535\u538b\u56de\u5f39\uff08recovery\uff09\u672c\u8eab\u4e5f\u643a\u5e26\u4fe1\u606f\u3002\u8001\u5316\u7684\u7535\u82af\u56de\u5f39\u6162\uff0c\u8fd9\u53cd\u6620\u7684\u662f\u5185\u90e8\u9502\u79bb\u5b50\u6269\u6563\u963b\u529b R_diff\uff0c\u800c\u4e0d\u662f\u5355\u7eaf\u7684\u6b27\u59c6\u5185\u963b\u3002\u5982\u679c\u53ea\u6d4b 10s \u7aef\u7535\u538b\u7b97 DCR\uff0c\u4f1a\u628a\u6269\u6563\u963b\u6297\u4e5f\u7cca\u8fdb\u6b27\u59c6\u9879\u91cc\uff0c\u5bfc\u81f4\u5206\u7fa4\u8bef\u5dee\u3002<\/p>\n

    \u4e09\u3001\u4e00\u81f4\u6027\u91cf\u5316\uff1a\u522b\u53ea\u770b\u538b\u5dee\uff0c\u8981\u770b”\u5185\u963b\u52a0\u6743\u538b\u5dee”<\/h2>\n

    \u5f88\u591a\u5de5\u7a0b\u5e08\u559c\u6b22\u7528”\u6700\u5927\u538b\u5dee \u0394V_max”\u6765\u5224\u65ad\u4e00\u7ec4\u7535\u6c60\u5065\u4e0d\u5065\u5eb7\u3002\u8fd9\u5728\u5927\u7535\u6d41\u5145\u7535\u672b\u7aef\u662f\u6709\u7528\u7684\uff0c\u4f46\u5728\u68af\u6b21\u7535\u82af\u4e0a\u4f1a\u8bef\u5224\u3002<\/p>\n

    \u4e3e\u4e00\u4e2a\u771f\u5b9e\u4f1a\u9047\u5230\u7684\u573a\u666f\uff1a\u4e24\u9897\u7535\u82af OCV \u66f2\u7ebf\u8f7b\u5fae\u504f\u79fb\uff0c\u9759\u7f6e\u65f6\u538b\u5dee\u53ea\u6709 10 mV\uff0c\u770b\u7740\u5b8c\u5168\u6ca1\u95ee\u9898\u3002\u4f46\u4e00\u65e6\u5f00\u59cb 1C \u653e\u7535\uff0c\u5176\u4e2d\u4e00\u9897\u5185\u963b\u9ad8 5 m\u03a9\uff0c\u90a3\u4e48 1C\uff08\u5047\u8bbe 100 Ah\uff09\u5c31\u591a\u538b\u964d 0.5 V\uff0c\u538b\u5dee\u77ac\u95f4\u7834\u767e mV\uff0cBMS \u76f4\u63a5\u89e6\u53d1\u6b20\u538b\u4fdd\u62a4\u3002<\/p>\n

    \u6240\u4ee5\u5de5\u7a0b\u4e0a\u66f4\u7a33\u59a5\u7684\u6307\u6807\u662f\u5185\u963b\u52a0\u6743\u7684\u52a8\u6001\u538b\u5dee<\/strong>\uff1a<\/p>\n

    \u0394V_dynamic(i) = |OCV_i(SOC) - OCV_avg(SOC)| + I \u00d7 (R_i - R_avg)\n<\/code><\/pre>\n
    \u5176\u4e2d I \u662f\u5f53\u524d\u7535\u6d41\uff0cR_i \u662f\u7b2c i \u9897\u7535\u82af\u7684 DCR\u3002\u8fd9\u4e2a\u5f0f\u5b50\u628a”\u9759\u7f6e\u5dee\u5f02”\u548c”\u8d1f\u8f7d\u5dee\u5f02”\u62c6\u5f00\u4e86\uff0c\u80fd\u5206\u522b\u5b9a\u4f4d\u95ee\u9898\u662f\u51fa\u5728\u5bb9\u91cf\u4e0d\u4e00\u81f4\u8fd8\u662f\u5185\u963b\u4e0d\u4e00\u81f4\u4e0a\u3002<\/p>\n
    \u771f\u6b63\u8981\u63a7\u5236\u7684\u662f\u603b\u538b\u5dee\u7684\u5305\u7edc\uff0c\u7ecf\u9a8c\u4e0a\u68af\u6b21\u50a8\u80fd\u7cfb\u7edf\u5728 80% SOC\u30010.5C \u653e\u7535\u65f6\uff0c\u7ec4\u5185\u538b\u5dee\u5e94\u63a7\u5236\u5728 50 mV \u4ee5\u5185\uff0c\u8d85\u8fc7\u5c31\u8981\u89e6\u53d1\u4e3b\u52a8\u5747\u8861\u6216\u964d\u529f\u7387\u3002<\/p>\n
    \u56db\u3001\u5206\u7fa4\u7b97\u6cd5\uff1a\u7528 K-Means \u52a0\u7ea6\u675f\uff0c\u800c\u4e0d\u662f\u62cd\u8111\u888b\u5206\u7ec4<\/h2>\n
    \u62ff\u5230\u7279\u5f81\u5411\u91cf\u540e\uff0c\u5206\u7fa4\u672c\u8d28\u662f\u4e00\u4e2a\u805a\u7c7b\u95ee\u9898\u3002\u6700\u5e38\u7528\u7684\u5c31\u662f K-Means\uff0c\u4f46\u68af\u6b21\u5206\u9009\u6709\u51e0\u4e2a\u786c\u7ea6\u675f\u4e0d\u80fd\u4e22\uff1a<\/p>\n
      \n
    1. \u540c\u7ec4\u7535\u82af\u7684\u5bb9\u91cf\u6781\u5dee\u8981\u5c0f<\/strong>\uff1a\u5426\u5219\u6728\u6876\u6548\u5e94\u76f4\u63a5\u5403\u6389\u6574\u7ec4\u6709\u6548\u5bb9\u91cf\u3002<\/li>\n
    2. \u540c\u7ec4\u5185\u963b\u8981\u63a5\u8fd1<\/strong>\uff1a\u5426\u5219\u5927\u7535\u6d41\u4e0b\u538b\u5dee\u5931\u63a7\u3002<\/li>\n
    3. \u540c\u7ec4\u5fc5\u987b\u6709\u76f8\u540c\u7684 SOC \u57fa\u51c6<\/strong>\uff1a\u5206\u7fa4\u524d\u5fc5\u987b\u628a\u6bcf\u9897\u7535\u82af\u5145\/\u653e\u5230\u540c\u4e00 OCV \u57fa\u51c6\u70b9\uff0c\u5426\u5219\u540e\u9762\u5168\u662f\u4e71\u7684\u3002<\/li>\n<\/ol>\n
      \u4e0b\u9762\u662f\u4e00\u6bb5\u53ef\u590d\u7528\u7684 Python \u4f2a\u4ee3\u7801\uff0c\u5c55\u793a”\u5bb9\u91cf-\u5185\u963b”\u4e8c\u7ef4\u805a\u7c7b\u540e\u5e26\u7ea6\u675f\u7b5b\u9009\u7684\u601d\u8def\uff1a<\/p>\n
      from sklearn.cluster import KMeans\nimport numpy as np\n\n# features: [capacity, dcr] \u5f52\u4e00\u5316\u540e\nX = np.column_stack([norm_capacity, norm_dcr])\n\nkmeans = KMeans(n_clusters=k, random_state=0)\nlabels = kmeans.fit_predict(X)\n\ngrouped = {}\nfor idx, lab in enumerate(labels):\n    grouped.setdefault(lab, []).append(idx)\n\n# \u540e\u5904\u7406\u7ea6\u675f\uff1a\u5254\u9664\u7ec4\u5185\u5bb9\u91cf\u6781\u5dee > 2% \u7684\u79bb\u7fa4\u7535\u82af\ndef filter_group(indices):\n    caps = [capacity[i] for i in indices]\n    mean_cap = np.mean(caps)\n    return [i for i, c in zip(indices, caps) if abs(c - mean_cap) \/ mean_cap <= 0.02]\n\nresult = {lab: filter_group(indices) for lab, indices in grouped.items()}\n<\/code><\/pre>\n
      \u805a\u7c7b\u5206\u7fa4\u4e4b\u540e\uff0c\u901a\u5e38\u8fd8\u4f1a\u505a\u4e00\u8f6e\u7ec4\u95f4\u914d\u5e73<\/strong>\uff1a\u5982\u679c\u4e24\u4e2a\u5206\u7fa4\u7684\u5e73\u5747\u5bb9\u91cf\u63a5\u8fd1\uff0c\u53ef\u4ee5\u5408\u5e76\uff1b\u5982\u679c\u67d0\u7ec4\u7535\u82af\u6570\u91cf\u4e0d\u6574\u9664\u4e32\u5e76\u8054\u6a21\u7ec4\uff0c\u8981\u4e48\u8865\u6599\uff0c\u8981\u4e48\u62c6\u6a21\u7ec4\u7ed3\u6784\u3002\u8fd9\u4e00\u6b65\u6ca1\u6709\u6377\u5f84\uff0c\u5f97\u8ddf Pack \u7ed3\u6784\u8bbe\u8ba1\u8054\u52a8\u7b97\u3002<\/p>\n
      \u4e94\u3001\u6210\u7ec4\u540e\u7684 BMS \u6301\u7eed\u4e00\u81f4\u6027\u7ba1\u7406<\/h2>\n
      \u5206\u9009\u89e3\u51b3\u7684\u662f”\u8d77\u70b9\u4e00\u81f4”\uff0c\u4f46\u68af\u6b21\u7535\u82af\u7684\u8001\u5316\u901f\u5ea6\u4e5f\u4e0d\u4e00\u6837\uff0c\u8fd0\u884c\u534a\u5e74\u540e\u539f\u672c\u4e00\u81f4\u7684\u4e00\u7ec4\u4e5f\u4f1a\u6162\u6162\u53d1\u6563\u3002\u6240\u4ee5 BMS \u4e0d\u80fd\u53ea\u505a\u9759\u6001\u4fdd\u62a4\uff0c\u8fd8\u8981\u627f\u62c5\u6301\u7eed\u4e00\u81f4\u6027\u76d1\u63a7<\/strong>\u7684\u804c\u8d23\u3002<\/p>\n
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