{
  "node": {
    "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"
      ]
    }
  },
  "edges": [
    {
      "id": "edge:6526",
      "source": "term:recursive-least-squares",
      "target": "topic:topic-state",
      "type": "belongsTo",
      "properties": {
        "sourceFile": "terms.json"
      }
    },
    {
      "id": "edge:6527",
      "source": "term:recursive-least-squares",
      "target": "term:parameterisation",
      "type": "relatedTo",
      "properties": {
        "sourceFile": "terms.json",
        "meaning": "Related context; not a causal claim"
      }
    },
    {
      "id": "edge:6528",
      "source": "term:recursive-least-squares",
      "target": "term:equivalent-circuit-model",
      "type": "relatedTo",
      "properties": {
        "sourceFile": "terms.json",
        "meaning": "Related context; not a causal claim"
      }
    },
    {
      "id": "edge:6529",
      "source": "term:recursive-least-squares",
      "target": "term:dual-joint-estimation",
      "type": "relatedTo",
      "properties": {
        "sourceFile": "terms.json",
        "meaning": "Related context; not a causal claim"
      }
    },
    {
      "id": "edge:6530",
      "source": "term:recursive-least-squares",
      "target": "term:kalman-filter",
      "type": "relatedTo",
      "properties": {
        "sourceFile": "terms.json",
        "meaning": "Related context; not a causal claim"
      }
    },
    {
      "id": "edge:6531",
      "source": "term:recursive-least-squares",
      "target": "term:rc-pair",
      "type": "relatedTo",
      "properties": {
        "sourceFile": "terms.json",
        "meaning": "Related context; not a causal claim"
      }
    },
    {
      "id": "edge:mentions/recursive-least-squares/state-of-charge",
      "source": "term:recursive-least-squares",
      "target": "term:state-of-charge",
      "type": "mentions",
      "properties": {
        "sourceFile": "terms.json",
        "method": "build_glossary.mention_index + mentions",
        "field": "definition",
        "meaning": "Text mention; not a causal claim"
      }
    },
    {
      "id": "edge:mentions/recursive-least-squares/bms",
      "source": "term:recursive-least-squares",
      "target": "term:bms",
      "type": "mentions",
      "properties": {
        "sourceFile": "terms.json",
        "method": "build_glossary.mention_index + mentions",
        "field": "definition",
        "meaning": "Text mention; 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:parameterisation",
      "type": "Term",
      "label": "Parameterisation",
      "properties": {
        "id": "parameterisation",
        "name": "Parameterisation",
        "aka": [
          "parameter identification"
        ],
        "section": "topic-transport",
        "definition": "Measuring or fitting the numbers a model needs: OCV curves, diffusion coefficients, exchange current densities, thicknesses and porosities for a physics model, or resistances and time constants for a circuit model. It is usually most of the modelling effort.",
        "related": [
          "newman-model",
          "equivalent-circuit-model",
          "gitt",
          "hppc",
          "electrochemical-impedance-spectroscopy"
        ],
        "url": "https://lemonergy.com/learn/glossary/parameterisation/",
        "mentions": [
          "open-circuit-voltage",
          "diffusion"
        ]
      }
    },
    {
      "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: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": "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:rc-pair",
      "type": "Term",
      "label": "RC pair",
      "properties": {
        "id": "rc-pair",
        "name": "RC pair",
        "aka": [
          "RC element",
          "RC branch"
        ],
        "section": "topic-transport",
        "definition": "A resistor and capacitor in parallel, the building block of an equivalent circuit model. Each pair reproduces one time constant of the cell’s voltage response, from the seconds of charge transfer to the minutes of diffusion.",
        "formula": "τ = R × C",
        "related": [
          "equivalent-circuit-model",
          "relaxation",
          "charge-transfer"
        ],
        "example": {
          "title": "Calculate a response time",
          "text": "An illustrative 0.02 Ω resistance and 500 F capacitance have τ = RC = 10 s. A first-order step response reaches about 63% of its final change after one time constant."
        },
        "url": "https://lemonergy.com/learn/glossary/rc-pair/",
        "mentions": [
          "diffusion",
          "capacitor"
        ]
      }
    },
    {
      "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"
        ]
      }
    }
  ]
}