{
  "node": {
    "id": "term:state-observer",
    "type": "Term",
    "label": "State observer",
    "properties": {
      "id": "state-observer",
      "name": "State observer",
      "aka": [
        "Luenberger observer",
        "sliding-mode observer",
        "H-infinity filter",
        "adaptive observer"
      ],
      "section": "topic-state",
      "definition": "An algorithm that reconstructs internal states such as SOC by running a model alongside the cell and correcting it with the measured voltage error. A Luenberger observer uses a fixed gain, a sliding-mode observer a switching term robust to model error, an H-infinity filter bounds the worst-case error, and an adaptive observer also updates the model parameters.",
      "related": [
        "kalman-filter",
        "equivalent-circuit-model",
        "state-of-charge",
        "observability",
        "particle-filter",
        "dual-joint-estimation"
      ],
      "url": "https://lemonergy.com/learn/glossary/state-observer/",
      "mentions": []
    }
  },
  "edges": [
    {
      "id": "edge:6518",
      "source": "term:state-observer",
      "target": "topic:topic-state",
      "type": "belongsTo",
      "properties": {
        "sourceFile": "terms.json"
      }
    },
    {
      "id": "edge:6519",
      "source": "term:state-observer",
      "target": "term:kalman-filter",
      "type": "relatedTo",
      "properties": {
        "sourceFile": "terms.json",
        "meaning": "Related context; not a causal claim"
      }
    },
    {
      "id": "edge:6520",
      "source": "term:state-observer",
      "target": "term:equivalent-circuit-model",
      "type": "relatedTo",
      "properties": {
        "sourceFile": "terms.json",
        "meaning": "Related context; not a causal claim"
      }
    },
    {
      "id": "edge:6521",
      "source": "term:state-observer",
      "target": "term:state-of-charge",
      "type": "relatedTo",
      "properties": {
        "sourceFile": "terms.json",
        "meaning": "Related context; not a causal claim"
      }
    },
    {
      "id": "edge:6522",
      "source": "term:state-observer",
      "target": "term:observability",
      "type": "relatedTo",
      "properties": {
        "sourceFile": "terms.json",
        "meaning": "Related context; not a causal claim"
      }
    },
    {
      "id": "edge:6523",
      "source": "term:state-observer",
      "target": "term:particle-filter",
      "type": "relatedTo",
      "properties": {
        "sourceFile": "terms.json",
        "meaning": "Related context; not a causal claim"
      }
    },
    {
      "id": "edge:6524",
      "source": "term:state-observer",
      "target": "term:dual-joint-estimation",
      "type": "relatedTo",
      "properties": {
        "sourceFile": "terms.json",
        "meaning": "Related context; not a causal claim"
      }
    },
    {
      "id": "edge:6525",
      "source": "term:state-observer",
      "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: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"
        ]
      }
    },
    {
      "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:observability",
      "type": "Term",
      "label": "Observability and controllability",
      "properties": {
        "id": "observability",
        "name": "Observability and controllability",
        "aka": [
          "observability"
        ],
        "section": "topic-state",
        "definition": "Two control-theory properties of a model: whether its internal states can be inferred from its measured outputs, and whether its inputs can steer those states. SOC is poorly observable on LFP’s flat OCV plateau, where voltage barely changes with charge, and the individual SOCs of series cells cannot be steered by pack current alone, which is why balancing exists.",
        "related": [
          "state-observer",
          "state-space-model",
          "ocv-soc-curve",
          "lfp",
          "cell-balancing",
          "voltage-plateau"
        ],
        "url": "https://lemonergy.com/learn/glossary/observability/",
        "mentions": [
          "state-of-charge",
          "open-circuit-voltage"
        ]
      }
    },
    {
      "id": "term:particle-filter",
      "type": "Term",
      "label": "Particle filter",
      "properties": {
        "id": "particle-filter",
        "name": "Particle filter",
        "aka": [
          "sequential Monte Carlo"
        ],
        "section": "topic-state",
        "definition": "A recursive estimator that represents an uncertain state as a cloud of weighted samples, propagating each through the model and reweighting it against every new measurement. Unlike a Kalman filter it copes with strong nonlinearity and non-Gaussian noise, at the cost of hundreds of model runs per step, so it appears more in remaining-useful-life prognostics than in production BMS firmware.",
        "related": [
          "kalman-filter",
          "remaining-useful-life",
          "state-of-charge",
          "state-observer",
          "equivalent-circuit-model"
        ],
        "url": "https://lemonergy.com/learn/glossary/particle-filter/",
        "mentions": [
          "bms",
          "firmware"
        ]
      }
    },
    {
      "id": "term:dual-joint-estimation",
      "type": "Term",
      "label": "Dual and joint estimation",
      "properties": {
        "id": "dual-joint-estimation",
        "name": "Dual and joint estimation",
        "aka": [
          "co-estimation",
          "dual extended Kalman filter",
          "dual EKF"
        ],
        "section": "topic-state",
        "definition": "Two ways to estimate a cell’s fast-changing states, such as SOC, together with its slowly drifting parameters, such as capacity and resistance. Dual estimation runs two coupled filters on different time scales, each using the other’s latest output; joint estimation stacks both into one augmented vector inside a single filter, simpler to structure but larger and harder to tune.",
        "related": [
          "kalman-filter",
          "recursive-least-squares",
          "state-of-charge",
          "state-of-health",
          "state-observer"
        ],
        "url": "https://lemonergy.com/learn/glossary/dual-joint-estimation/",
        "mentions": []
      }
    },
    {
      "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"
      }
    }
  ]
}