{
  "node": {
    "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": []
    }
  },
  "edges": [
    {
      "id": "edge:6534",
      "source": "term:dual-joint-estimation",
      "target": "topic:topic-state",
      "type": "belongsTo",
      "properties": {
        "sourceFile": "terms.json"
      }
    },
    {
      "id": "edge:6535",
      "source": "term:dual-joint-estimation",
      "target": "term:kalman-filter",
      "type": "relatedTo",
      "properties": {
        "sourceFile": "terms.json",
        "meaning": "Related context; not a causal claim"
      }
    },
    {
      "id": "edge:6536",
      "source": "term:dual-joint-estimation",
      "target": "term:recursive-least-squares",
      "type": "relatedTo",
      "properties": {
        "sourceFile": "terms.json",
        "meaning": "Related context; not a causal claim"
      }
    },
    {
      "id": "edge:6537",
      "source": "term:dual-joint-estimation",
      "target": "term:state-of-charge",
      "type": "relatedTo",
      "properties": {
        "sourceFile": "terms.json",
        "meaning": "Related context; not a causal claim"
      }
    },
    {
      "id": "edge:6538",
      "source": "term:dual-joint-estimation",
      "target": "term:state-of-health",
      "type": "relatedTo",
      "properties": {
        "sourceFile": "terms.json",
        "meaning": "Related context; not a causal claim"
      }
    },
    {
      "id": "edge:6539",
      "source": "term:dual-joint-estimation",
      "target": "term:state-observer",
      "type": "relatedTo",
      "properties": {
        "sourceFile": "terms.json",
        "meaning": "Related context; not a causal claim"
      }
    }
  ],
  "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:recursive-least-squares",
      "type": "Term",
      "label": "Recursive least squares",
      "properties": {
        "id": "recursive-least-squares",
        "name": "Recursive least squares",
        "abbr": "RLS",
        "aka": [
          "forgetting factor"
        ],
        "section": "topic-state",
        "definition": "An online fitting method that updates model parameters with each new sample instead of refitting all past data. A forgetting factor set just below 1, such as 0.99, discounts old samples so the estimate follows a cell’s resistance and time constants as temperature, SOC and aging change them; the BMS then hands the parameters to its state estimator.",
        "related": [
          "parameterisation",
          "equivalent-circuit-model",
          "dual-joint-estimation",
          "kalman-filter",
          "rc-pair"
        ],
        "url": "https://lemonergy.com/learn/glossary/recursive-least-squares/",
        "mentions": [
          "state-of-charge",
          "bms"
        ]
      }
    },
    {
      "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:state-of-health",
      "type": "Term",
      "label": "State of health",
      "properties": {
        "id": "state-of-health",
        "name": "State of health",
        "abbr": "SOH",
        "section": "topic-state",
        "definition": "A summary of how far a cell has aged, often expressed through remaining capacity or increased resistance compared with when it was new. By convention a cell has reached end of life at 80 % of its rated capacity.",
        "unit": "%",
        "related": [
          "capacity-fade",
          "impedance-rise",
          "end-of-life",
          "remaining-useful-life",
          "state-of-charge",
          "state-of-certified-energy"
        ],
        "lesson": "fundamentals/capacity-and-c-rate/#real-cells",
        "url": "https://lemonergy.com/learn/glossary/state-of-health/",
        "mentions": [
          "nominal-capacity"
        ]
      }
    },
    {
      "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": []
      }
    }
  ]
}