{
  "node": {
    "id": "term:kalman-filter",
    "type": "Term",
    "label": "Kalman filter",
    "properties": {
      "id": "kalman-filter",
      "name": "Kalman filter",
      "section": "topic-state",
      "definition": "A recursive estimator that blends a model prediction with noisy measurements according to how much each is trusted. In a BMS an extended Kalman filter combines coulomb counting with a voltage model to track SOC and sometimes resistance.",
      "related": [
        "coulomb-counting",
        "equivalent-circuit-model",
        "state-of-charge",
        "bms",
        "fuzzy-logic",
        "data-driven-soc-estimation"
      ],
      "url": "https://lemonergy.com/learn/glossary/kalman-filter/",
      "mentions": [
        "coulomb"
      ]
    }
  },
  "edges": [
    {
      "id": "edge:1307",
      "source": "term:kalman-filter",
      "target": "topic:topic-state",
      "type": "belongsTo",
      "properties": {
        "sourceFile": "terms.json"
      }
    },
    {
      "id": "edge:1308",
      "source": "term:kalman-filter",
      "target": "term:coulomb-counting",
      "type": "relatedTo",
      "properties": {
        "sourceFile": "terms.json",
        "meaning": "Related context; not a causal claim"
      }
    },
    {
      "id": "edge:1309",
      "source": "term:kalman-filter",
      "target": "term:equivalent-circuit-model",
      "type": "relatedTo",
      "properties": {
        "sourceFile": "terms.json",
        "meaning": "Related context; not a causal claim"
      }
    },
    {
      "id": "edge:1310",
      "source": "term:kalman-filter",
      "target": "term:state-of-charge",
      "type": "relatedTo",
      "properties": {
        "sourceFile": "terms.json",
        "meaning": "Related context; not a causal claim"
      }
    },
    {
      "id": "edge:1311",
      "source": "term:kalman-filter",
      "target": "term:bms",
      "type": "relatedTo",
      "properties": {
        "sourceFile": "terms.json",
        "meaning": "Related context; not a causal claim"
      }
    },
    {
      "id": "edge:1312",
      "source": "term:kalman-filter",
      "target": "term:fuzzy-logic",
      "type": "relatedTo",
      "properties": {
        "sourceFile": "terms.json",
        "meaning": "Related context; not a causal claim"
      }
    },
    {
      "id": "edge:1313",
      "source": "term:kalman-filter",
      "target": "term:data-driven-soc-estimation",
      "type": "relatedTo",
      "properties": {
        "sourceFile": "terms.json",
        "meaning": "Related context; not a causal claim"
      }
    },
    {
      "id": "edge:mentions/kalman-filter/coulomb",
      "source": "term:kalman-filter",
      "target": "term:coulomb",
      "type": "mentions",
      "properties": {
        "sourceFile": "terms.json",
        "method": "build_glossary.mention_index + mentions",
        "field": "definition",
        "meaning": "Text mention; not a causal claim"
      }
    },
    {
      "id": "edge:1315",
      "source": "term:kalman-filter",
      "target": "reference:plett-estimation",
      "type": "cites",
      "properties": {
        "sourceFile": "wiki-references.json",
        "scope": "direct technical reference"
      }
    }
  ],
  "neighbors": [
    {
      "id": "topic:topic-state",
      "type": "Topic",
      "label": "State estimation",
      "properties": {
        "id": "topic-state",
        "name": "State estimation",
        "lede": "The quantities a battery management system estimates but cannot measure directly.",
        "context": "Distinguish internal states from noisy measurement estimates.",
        "example": "Current-sensor bias accumulates in coulomb counting. Exact model SOC does not establish real estimator accuracy.",
        "lesson_url": "https://lemonergy.com/learn/fundamentals/capacity-and-c-rate/",
        "lesson_label": "Open charge balance"
      }
    },
    {
      "id": "term:coulomb-counting",
      "type": "Term",
      "label": "Coulomb counting",
      "properties": {
        "id": "coulomb-counting",
        "name": "Coulomb counting",
        "aka": [
          "ampere-hour counting"
        ],
        "section": "topic-state",
        "definition": "Estimating state of charge by integrating the measured current over time. Simple and fast, but sensor offset accumulates, so it is corrected against the OCV–SOC curve at rest or fused with it in a Kalman filter.",
        "formula": "SOC(t) = SOC₀ − ∫ I dt / Q",
        "related": [
          "state-of-charge",
          "ocv-soc-curve",
          "kalman-filter",
          "current-sensor"
        ],
        "example": {
          "title": "Integrate a discharge step",
          "text": "Starting at 80% SOC with a 5 Ah available capacity, an ideal 2 A discharge for 30 min removes 1 Ah and lowers SOC to 60%. This example takes discharge current as positive and ignores efficiency and sensor error."
        },
        "url": "https://lemonergy.com/learn/glossary/coulomb-counting/",
        "mentions": [
          "open-circuit-voltage"
        ]
      }
    },
    {
      "id": "term:equivalent-circuit-model",
      "type": "Term",
      "label": "Equivalent circuit model",
      "properties": {
        "id": "equivalent-circuit-model",
        "name": "Equivalent circuit model",
        "abbr": "ECM",
        "aka": [
          "Thevenin model"
        ],
        "section": "topic-transport",
        "definition": "A cell model built from a voltage source for OCV, a series resistor and one or more RC pairs for the slower dynamics. Its few parameters are fitted from pulse tests, and it is what most battery management systems run in real time.",
        "related": [
          "rc-pair",
          "open-circuit-voltage",
          "hppc",
          "kalman-filter",
          "single-particle-model"
        ],
        "url": "https://lemonergy.com/learn/glossary/equivalent-circuit-model/",
        "mentions": []
      }
    },
    {
      "id": "term:state-of-charge",
      "type": "Term",
      "label": "State of charge",
      "properties": {
        "id": "state-of-charge",
        "name": "State of charge",
        "abbr": "SOC",
        "section": "topic-state",
        "definition": "How full a cell is, as a fraction of its available capacity. State of charge, like temperature, affects how fast a stored cell ages.",
        "unit": "%",
        "related": [
          "depth-of-discharge",
          "ocv-soc-curve",
          "coulomb-counting",
          "state-of-health",
          "bms"
        ],
        "lesson": "fundamentals/battery-aging/#storage-soc",
        "example": {
          "title": "Interpret a charge inventory",
          "text": "If available capacity is 5 Ah and 3 Ah remains by the chosen estimator, SOC is 60%. A different estimate of available capacity changes this percentage, so compare SOC values only with the capacity convention recorded."
        },
        "url": "https://lemonergy.com/learn/glossary/state-of-charge/",
        "mentions": [
          "usable-capacity"
        ]
      }
    },
    {
      "id": "term:bms",
      "type": "Term",
      "label": "Battery management system",
      "properties": {
        "id": "bms",
        "name": "Battery management system",
        "abbr": "BMS",
        "section": "topic-pack",
        "definition": "The electronics and software that keep a battery inside its safe window: measuring every cell voltage and many temperatures, estimating SOC and SOH, balancing cells, computing current limits, controlling the contactors and reporting over the vehicle network.",
        "related": [
          "state-of-charge",
          "cell-balancing",
          "state-of-power",
          "cell-supervision-circuit",
          "contactor",
          "can-bus"
        ],
        "lesson": "fundamentals/battery-basics/#cell-module-pack",
        "url": "https://lemonergy.com/learn/glossary/bms/",
        "mentions": [
          "state-of-health",
          "cell-voltage"
        ]
      }
    },
    {
      "id": "term:fuzzy-logic",
      "type": "Term",
      "label": "Fuzzy logic",
      "properties": {
        "id": "fuzzy-logic",
        "name": "Fuzzy logic",
        "aka": [
          "fuzzy inference",
          "fuzzy controller"
        ],
        "section": "topic-data",
        "definition": "Reasoning with degrees of truth between 0 and 1, so that a temperature can be partly ‘warm’ and partly ‘hot’, combined through if–then rules. It has been applied to SOC and SOH estimation and to charge control because it encodes expert knowledge without a precise model, though its rules and membership functions still need tuning on test data.",
        "related": [
          "state-of-charge",
          "state-of-health",
          "kalman-filter",
          "pid-controller"
        ],
        "url": "https://lemonergy.com/learn/glossary/fuzzy-logic/",
        "mentions": []
      }
    },
    {
      "id": "term:data-driven-soc-estimation",
      "type": "Term",
      "label": "Data-driven SOC estimation",
      "properties": {
        "id": "data-driven-soc-estimation",
        "name": "Data-driven SOC estimation",
        "aka": [
          "machine-learning SOC estimation",
          "neural-network SOC estimation",
          "support vector machine",
          "SVM",
          "LSTM"
        ],
        "section": "topic-state",
        "definition": "Estimating SOC with a model learned from data — LSTM neural networks, support vector regression, Gaussian processes — that maps histories of voltage, current and temperature to charge. It needs no physical model, but is only as good as its training data across temperature and aging, and is harder to verify for functional safety than a Kalman filter.",
        "related": [
          "state-of-charge",
          "kalman-filter",
          "coulomb-counting",
          "functional-safety",
          "equivalent-circuit-model"
        ],
        "url": "https://lemonergy.com/learn/glossary/data-driven-soc-estimation/",
        "mentions": []
      }
    },
    {
      "id": "term:coulomb",
      "type": "Term",
      "label": "Coulomb",
      "properties": {
        "id": "coulomb",
        "name": "Coulomb",
        "abbr": "C",
        "section": "topic-units",
        "definition": "The SI unit of electric charge; one ampere flowing for one second. Battery engineers prefer the ampere-hour, which is 3600 coulombs, because cells deliver their charge over hours.",
        "unit": "C",
        "related": [
          "ampere-hour",
          "faradays-constant"
        ],
        "url": "https://lemonergy.com/learn/glossary/coulomb/",
        "mentions": [
          "ampere",
          "electric-charge"
        ]
      }
    },
    {
      "id": "reference:plett-estimation",
      "type": "Reference",
      "label": "Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation",
      "properties": {
        "title": "Extended Kalman filtering for battery management systems of LiPB-based HEV battery packs: Part 3. State and parameter estimation",
        "publisher": "Journal of Power Sources",
        "url": "https://doi.org/10.1016/j.jpowsour.2004.02.033",
        "type": "Research article",
        "scope": "Model-based battery state-of-charge and parameter estimation with extended Kalman filtering, including uncertainty and model dependence.",
        "year": 2004,
        "authors": [
          "Gregory L. Plett"
        ],
        "accessed": "2026-10-27"
      }
    }
  ]
}