Source: EURLEX
Language: es
Format: md

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| 1.7.2019 | ES | Diario Oficial de la Unión Europea | L 176/67 |

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DECISIÓN DE EJECUCIÓN (UE) 2019/1119 DE LA COMISIÓN

de 28 de junio de 2019

relativa a la aprobación de una tecnología de iluminación eficiente para el exterior de los vehículos que utiliza diodos emisores de luz para su uso en vehículos de motor de combustión interna y en vehículos eléctricos híbridos no recargables desde el exterior como tecnología innovadora para la reducción de las emisiones de CO2 de los turismos de conformidad con el Reglamento (CE) n.o 443/2009 del Parlamento Europeo y del Consejo

(Texto pertinente a efectos del EEE)

LA COMISIÓN EUROPEA,

Visto el Tratado de Funcionamiento de la Unión Europea,

Visto el Reglamento (CE) n.o 443/2009 del Parlamento Europeo y del Consejo, de 23 de abril de 2009, por el que se establecen normas de comportamiento en materia de emisiones de los turismos nuevos como parte del enfoque integrado de la Comunidad para reducir las emisiones de CO2 de los vehículos ligeros [(1)](#ntr1-L_2019176ES.01006701-E0001), y en particular su artículo 12, apartado 4,

Considerando lo siguiente:

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| (1) | El 6 de septiembre de 2018, los fabricantes Toyota Motor Europe NV/SA, Opel Automobile GmbH – PSA, FCA Italy S.p.A., Automobiles Citroën, Automobiles Peugeot, PSA Automobiles SA, Audi AG, Ford Werke GmbH, Jaguar Land Rover, Hyundai Motor Europe Technical Center GmbH, Škoda Auto a.s., BMW AG, Renault SA, Honda Motor Europe Ltd, Volkswagen AG y Volkswagen AG Nutzfahrzeuge («los solicitantes») presentaron una solicitud conjunta de aprobación de una tecnología de iluminación eficiente para el exterior de los vehículos que utiliza diodos emisores de luz (iluminación LED eficiente) para su uso en vehículos de motor de combustión interna y en vehículos eléctricos híbridos no recargables desde el exterior como tecnología innovadora. La solicitud ha sido evaluada de conformidad con el artículo 12 del Reglamento (CE) n.o 443/2009 y con el Reglamento de Ejecución (UE) n.o 725/2011 de la Comisión [(2)](#ntr2-L_2019176ES.01006701-E0002). |

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| (2) | La iluminación LED eficiente es un módulo de iluminación equipado con fuentes de diodos emisores de luz con un consumo de potencia inferior al de la iluminación halógena convencional. |

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| (3) | La solicitud ha sido evaluada de conformidad con el artículo 12 del Reglamento (CE) n.o 443/2009, con el Reglamento de Ejecución (UE) n.o 725/2011 y con las orientaciones técnicas para la preparación de las solicitudes de aprobación de tecnologías innovadoras según el Reglamento (CE) n.o 443/2009 (en lo sucesivo, «orientaciones técnicas», versión de julio de 2018). |

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| (4) | La solicitud se refiere a la reducción de las emisiones de CO2 derivada del uso de una iluminación LED eficiente, evaluada según el procedimiento de ensayo de vehículos ligeros armonizado a nivel mundial (WLTP, Worldwide Harmonised Light Vehicle Test Procedure), establecido en el Reglamento (UE) 2017/1151 de la Comisión [(3)](#ntr3-L_2019176ES.01006701-E0003). |

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| (5) | La iluminación LED eficiente ya ha sido aprobada mediante las Decisiones de Ejecución 2014/128/UE [(4)](#ntr4-L_2019176ES.01006701-E0004), (UE) 2015/206 [(5)](#ntr5-L_2019176ES.01006701-E0005), (UE) 2016/160 [(6)](#ntr6-L_2019176ES.01006701-E0006), (UE) 2016/587 [(7)](#ntr7-L_2019176ES.01006701-E0007) y (UE) 2016/1721 [(8)](#ntr8-L_2019176ES.01006701-E0008) de la Comisión como una tecnología innovadora capaz de reducir las emisiones de CO2 siguiendo el Nuevo Ciclo de Conducción Europeo (NEDC), establecido en el Reglamento (CE) n.o 692/2008 de la Comisión [(9)](#ntr9-L_2019176ES.01006701-E0009). Sobre la base de la experiencia adquirida con esas Decisiones, y teniendo en cuenta la aplicación actual, se ha demostrado de manera satisfactoria y concluyente que una iluminación LED eficiente que incluya una o varias combinaciones adecuadas de luces LED eficientes, como luces de cruce, luces de carretera, luces de posición delanteras, luces antiniebla delanteras, luces antiniebla traseras, indicadores de dirección delanteros, indicadores de dirección traseros, luz de placa de matrícula y luces de marcha atrás, cumple los criterios de admisibilidad contemplados en el artículo 12 del Reglamento (CE) n.o 443/2009 y en el Reglamento de Ejecución (UE) n.o 725/2011. |

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| (6) | La reducción de las emisiones de CO2 derivada del uso de una iluminación LED eficiente puede demostrarse parcialmente en el ensayo WLTP. No obstante, los solicitantes han presentado una metodología de ensayo con la que puede demostrarse, de una manera con la que es posible obtener resultados repetibles, verificables y comparables, que la reducción obtenida, teniendo en cuenta incluso la cobertura parcial, es, como mínimo, de 0,5 g de CO2/km. |

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| (7) | A fin de garantizar la continuidad, en particular por lo que se refiere a la transición entre el NEDC y el ensayo WLTP de las emisiones de CO2, conviene mantener la iluminación halógena como tecnología de referencia como se prevé en las Decisiones de Ejecución 2014/128/UE, (UE) 2015/206, (UE) 2016/160, (UE) 2016/587 y (UE) 2016/1721. |

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| (8) | Los fabricantes deben tener la posibilidad de solicitar a una autoridad de homologación de tipo la certificación de la reducción de las emisiones de CO2 derivada del uso de luces LED eficientes en vehículos de motor de combustión interna y en vehículos eléctricos híbridos no recargables desde el exterior. A tal fin, los fabricantes deben garantizar que la solicitud de certificación vaya acompañada de un informe de verificación de un organismo de verificación independiente en el que se confirme el nivel de reducción de las emisiones de CO2 que se ha de certificar y el cumplimiento de todas las condiciones pertinentes. |

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| (9) | Si autoridad de homologación de tipo considera que la iluminación LED no satisface las condiciones de certificación, debe rechazar la solicitud de certificación de la reducción de emisiones. |

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| (10) | Asimismo, para facilitar un mayor despliegue de la iluminación LED eficiente en los vehículos nuevos, los fabricantes deben tener la posibilidad de solicitar la certificación de la reducción de las emisiones de CO2 derivada de varias luces LED eficientes mediante una única solicitud de certificación. No obstante, conviene asegurar que, cuando se recurra a esa posibilidad, se aplique un mecanismo que solo incentive el despliegue de los sistemas de iluminación LED que ofrezcan la máxima eficiencia. |

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| (11) | La reducción de las emisiones de CO2 certificada con arreglo a la presente Decisión debe tenerse en cuenta en el cálculo de las emisiones medias específicas de CO2 de los fabricantes a partir del año natural de 2021. |

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| (12) | A fin de determinar el código general de las ecoinnovaciones que debe emplearse en los documentos de homologación de tipo pertinentes de conformidad con los anexos I, VIII y IX de la Directiva 2007/46/CE del Parlamento Europeo y del Consejo [(10)](#ntr10-L_2019176ES.01006701-E0010), conviene especificar el código individual que se va a utilizar para la tecnología innovadora de luces LED eficiente utilizada en vehículos de motor de combustión interna y en vehículos eléctricos híbridos no recargables desde el exterior. |

HA ADOPTADO LA PRESENTE DECISIÓN:

Artículo 1

Aprobación

Se aprueba como tecnología innovadora a tenor del artículo 12 del Reglamento (CE) n.o 443/2009 la tecnología utilizada en la iluminación de diodos emisores de luz (LED) eficiente, cuando esa tecnología innovadora se utilice para la iluminación exterior en turismos de motor de combustión interna y en turismo eléctricos híbridos no recargables desde el exterior.

Artículo 2

Definición

A efectos de la presente Decisión, se entiende por iluminación LED eficiente una tecnología consistente en un módulo de iluminación que está equipado con fuentes de diodos emisores de luz (LED) que se utilizan para la iluminación exterior de un vehículo y cuyo consumo de potencia es inferior al de la iluminación halógena convencional.

Artículo 3

Solicitud de certificación de la reducción de las emisiones de CO2

1.   Los fabricantes podrán solicitar la certificación de la reducción de las emisiones de CO2 derivada de una o varias luces exteriores LED eficientes cuando estas se utilizan para la iluminación externa de vehículos de motor de combustión interna de la categoría M1 y de vehículos eléctricos híbridos no recargables desde el exterior de categoría M1. La iluminación LED eficiente incluirá una o una combinación de las luces LED siguientes:

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| a) | luz de cruce (incluido el sistema de alumbrado delantero adaptable); |

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| b) | luz de carretera; |

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| c) | luz de posición delantera; |

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| d) | luz antiniebla delantera; |

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| e) | luz antiniebla trasera; |

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| f) | indicador de dirección delantero; |

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| g) | indicador de dirección trasero; |

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| h) | luz de matrícula; |

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| i) | luz de marcha atrás; |

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| j) | luz angular; |

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| k) | luz de giro estática. |

La luz LED o la combinación de luces LED que constituyen la iluminación LED eficiente permitirá conseguir, como mínimo, la reducción de las emisiones de CO2 especificada en el artículo 9, apartado 1, letra b), del Reglamento de Ejecución (UE) n.o 725/2011, demostrada utilizando la metodología de ensayo establecida en el anexo de la presente Decisión.

2.   Las solicitudes de certificación de la reducción de emisiones derivada de una o una combinación de luces LED eficientes irán acompañadas de un informe de verificación independiente en el que se confirme que se cumplen de las condiciones establecidas en el apartado 1.

3.   La autoridad de homologación de tipo rechazará la solicitud de certificación si comprueba que no se cumplen las condiciones establecidas en el apartado 1.

Artículo 4

Certificación de la reducción de las emisiones de CO2

1.   La reducción de las emisiones de CO2 derivada del uso de un sistema de iluminación LED eficiente a que se refiere el artículo 3, apartado 1, se determinará utilizando la metodología establecida en el anexo.

2.   Cuando un fabricante solicite la certificación de la reducción de las emisiones de CO2 derivada de más de una de las luces LED eficientes a que se refiere el artículo 3, apartado 1, respecto a una versión del vehículo, la autoridad de homologación de tipo determinará cuál de las luces LED eficientes sometidas a ensayo genera la menor reducción de las emisiones de CO2 y registrará el valor más bajo en la documentación de homologación de tipo correspondiente. Ese valor se indicará en el certificado de conformidad con arreglo a lo dispuesto en el artículo 11, apartado 2, del Reglamento de Ejecución (UE) n.o 725/2011.

3.   La autoridad de homologación de tipo registrará el informe de verificación y los resultados del ensayo con arreglo a los cuales se haya determinado la reducción de emisiones y pondrá esa información a disposición de la Comisión a petición de esta.

Artículo 5

Código de ecoinnovación

El código de ecoinnovación n.o 28 figurará en la documentación de homologación de tipo cuando se haga referencia a la presente Decisión de conformidad con el artículo 11, apartado 1, del Reglamento de Ejecución (UE) n.o 725/2011.

La reducción de las emisiones de CO2 registrada haciendo referencia a ese código de ecoinnovación podrá tenerse en cuenta en el cálculo de las emisiones medias específicas de los fabricantes a partir del año natural de 2021.

Artículo 6

Entrada en vigor

La presente Decisión entrará en vigor a los veinte días de su publicación en el Diario Oficial de la Unión Europea.

Hecho en Bruselas, el 28 de junio de 2019.

Por la Comisión

El Presidente

Jean-Claude JUNCKER

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ANEXO

Metodología para determinar la reducción de las emisiones de CO2 del sistema de iluminación LED eficiente siguiendo el procedimiento de ensayo de vehículos ligeros armonizado a nivel mundial

1.   INTRODUCCIÓN

A fin de determinar la reducción de las emisiones de CO2 que puede atribuirse a un sistema de iluminación LED eficiente consistente en una combinación adecuada de luces LED para su uso en el exterior de vehículos de motor de combustión interna de categoría M1 y de vehículos eléctricos híbridos no recargables desde el exterior de categoría M1, es necesario establecer lo siguiente:

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| 1) | las condiciones de ensayo; |

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| 2) | el equipo de ensayo; |

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| 3) | el procedimiento para determinar el ahorro de potencia; |

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| 4) | el procedimiento para determinar la reducción de las emisiones de CO2; |

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| 5) | el procedimiento para determinar la incertidumbre de la reducción de las emisiones de CO2. |

2.   SÍMBOLOS, PARÁMETROS Y UNIDADES

Símbolos latinos

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| AFS | — | Sistema de alumbrado delantero adaptable |
| B | — | Base de referencia |
| CO2 | — | Dióxido de carbono |
| Formula | — | Reducción de las emisiones de CO2 [g CO2/km] |
| C | — | Número de clases del sistema de alumbrado delantero adaptable |
| CF | — | Factor de conversión (l/100 km) - (g CO2/km) (gCO2/l) |
| EI | — | Ecoinnovador |
| VEH | — | Vehículo eléctrico híbrido |
| Formula | — | Factor de corrección de las emisiones de CO2  Formula , tal como se define en el subanexo 8, apéndice 2, del Reglamento (UE) 2017/1151 |
| Formula | — | Media de los T valores de  Formula  Formula |
| m | — | Número de luces exteriores LED eficientes que componen el sistema |
| MT | — | Umbral mínimo [g CO2/km] |
| n | — | Número de mediciones de la muestra |
| SCE | — | Vehículo sin carga exterior |
| P | — | Consumo de potencia de las luces del vehículo [W] |
| Formula | — | Consumo de potencia de la luz i correspondiente de un vehículo de referencia [W] |
| Formula | — | Consumo de potencia de la muestra n correspondiente de cada clase de vehículo [W] |
| Formula | — | Consumo de potencia de cada clase de vehículo (media de las n mediciones) [W] |
| Formula | — | Consumo de potencia del AFS de haz de cruce [W] |
| Formula | — | Consumo medio de potencia de la luz correspondiente del vehículo ecoinnovador [W] |
| ΔPi | — | Ahorro de potencia de cada luz exterior LED eficiente [W] |
| Formula | — | Desviación estándar de la reducción total de las emisiones de CO2 [g CO2/km] |
| Formula | — | Desviación estándar del factor de corrección  Formula  Formula |
| Formula | — | Desviación estándar de la media de los T valores de  Formula  Formula |
| Formula | — | Desviación estándar del consumo medio de potencia de cada clase de vehículo [W] |
| Formula | — | Desviación estándar del consumo de potencia de las luces LED del vehículo ecoinnovador [W] |
| Formula | — | Desviación estándar del consumo medio de potencia de las luces LED del vehículo ecoinnovador [W] |
| Formula | — | Incertidumbre o desviación estándar de la media de la potencia del AFS de haz de cruce [W] |
| T | — | Número de mediciones realizadas por el fabricante para la extrapolación de  Formula |
| t | — | Tiempo de conducción del ciclo de ensayo de vehículos ligeros a nivel mundial (WLTC) [s] (1 800 s) |
| UF | — | Factor de utilización de las luces de vehículo [-], como se define en el cuadro 6 |
| v | — | Velocidad media del ciclo de ensayo de vehículos ligeros a nivel mundial (WLTC) [km/h] |
| VPe | — | Consumo de potencia efectiva [l/kWh] |
| share c | — | Porcentaje de tiempo por intervalo de velocidad en cada clase |
| Image 1 | — | Sensibilidad de la reducción calculada de las emisiones de CO2 en relación con el consumo de potencia de las luces LED |
| Image 2 | — | Sensibilidad de la reducción calculada de las emisiones de CO2 en relación con el factor de corrección de las emisiones de CO2. |
| ηA | — | Eficiencia del alternador [-] |
| ηDCDC | — | Eficiencia del convertidor CC-CC [-] |

Subíndices

El índice (c) se refiere a la clase del sistema de alumbrado delantero adaptable en el que se realizan las mediciones de la muestra

El índice (i) se refiere a cada luz del vehículo

El índice (j) se refiere a la medición de la muestra

El índice (t) se refiere a cada número de mediciones de T

3.   CONDICIONES DE ENSAYO

Las condiciones de ensayo cumplirán los requisitos de los Reglamentos n.os 4 [(1)](#ntr1-L_2019176ES.01007101-E0001), 6 [(2)](#ntr2-L_2019176ES.01007101-E0002), 7 [(3)](#ntr3-L_2019176ES.01007101-E0003), 19 [(4)](#ntr4-L_2019176ES.01007101-E0004), 23 [(5)](#ntr5-L_2019176ES.01007101-E0005), 38 [(6)](#ntr6-L_2019176ES.01007101-E0006), 48 [(7)](#ntr7-L_2019176ES.01007101-E0007), 100 [(8)](#ntr8-L_2019176ES.01007101-E0008), 112 [(9)](#ntr9-L_2019176ES.01007101-E0009), 119 [(10)](#ntr10-L_2019176ES.01007101-E0010) y 123 [(11)](#ntr11-L_2019176ES.01007101-E0011) de la CEPE/ONU. El consumo de potencia se determinará de conformidad con el punto 6.1.4 del Reglamento n.o 112 de la CEPE/ONU y con el anexo 10, puntos 3.2.1 y 3.2.2, de dicho Reglamento.

En el caso del sistema de alumbrado delantero adaptable de haz de cruce (AFS) correspondiente al menos a dos de las clases C, E, V o W definidas en el Reglamento n.o 123 de la CEPE/ONU, a menos que se acuerde con el servicio técnico que la clase C sea la intensidad de LED media/representativa para la aplicación correspondiente del vehículo, las mediciones de potencia se efectuarán a la intensidad de LED de cada clase (Pc), según se define en el Reglamento n.o 123 de la CEPE/ONU. Si la clase C es la intensidad de LED media/representativa para la aplicación del vehículo, las mediciones de potencia se efectuarán de la misma manera que en el caso de cualquier otra luz LED exterior incluida en la combinación.

Equipo de ensayo

Se utilizará el equipo siguiente, como se indica en el gráfico:

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| — | una unidad de alimentación (de tensión variable), |

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| — | dos multímetros digitales, uno para medir la intensidad de la corriente continua y el otro para medir la tensión de la corriente continua. En el gráfico 1 se muestra un ejemplo de configuración de ensayo en el que el multímetro de tensión está integrado en la unidad de alimentación. |

Configuración de ensayo

![Image 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)
[Texto de la imagen](javascript:void(0);)

Monitor de corriente

Fuente de alimentación de tensión variable

Lámpara LED

Mediciones y cálculo del ahorro de potencia

La medición de la corriente de cada luz exterior LED eficiente incluida en la combinación se efectuará como se muestra en el gráfico, con una tensión de 13,2 V. El módulo o módulos LED que funcionen con un dispositivo electrónico de control de la fuente luminosa se medirán de acuerdo con las especificaciones del solicitante.

El fabricante podrá solicitar que se efectúen más mediciones de la corriente a otras tensiones. En ese caso, el fabricante entregará a la autoridad de homologación de tipo documentación verificada sobre la necesidad de efectuar esas otras mediciones. Las mediciones de la corriente a cada una de esas otras tensiones se efectuarán al menos cinco veces consecutivas. Los valores exactos de la tensión instalada y la corriente medida se registrarán redondeados al cuarto decimal.

El consumo de potencia se calculará multiplicando la tensión instalada por la corriente medida. Se calculará el consumo medio de potencia de cada luz exterior LED eficiente (
![Formula](data:image/jpg;base64,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)
). Cada valor se expresará redondeado al cuarto decimal. Cuando se utilice un motor de velocidad gradual o un mando electrónico para el suministro de electricidad a las luces LED, se excluirá de la medición la carga eléctrica de ese componente.

Mediciones adicionales para el sistema de alumbrado delantero adaptable (AFS) de haz de cruce

Cuadro 1

Clases de AFS de haz de cruce

|  |  |  |  |
| --- | --- | --- | --- |
| Clase | Véanse el punto 1.3 y la nota a pie de página 2 del Reglamento n.o 123 de la CEPE/ONU. | % de intensidad de LED | Modo de activación[(\*1)](#ntr*1-L_2019176ES.01007101-E0012) |
| C | Haz de cruce básico (conducción interurbana) | 100 % | 50km/h < velocidad < 100km/h  O cuando no está activado ningún modo de otra clase de haz de cruce (V, W, E) |
| V | Conducción urbana | 85 % | Velocidad < 50km/h |
| E | Autopista | 110 % | Velocidad > 100 km/h |
| W | Condiciones adversas | 90 % | Limpiaparabrisas activo > 2 min |

Cuando sea necesario medir la potencia a la intensidad del LED de cada clase, una vez realizadas las mediciones de cada Pc, se calculará la potencia del AFS de haz de cruce (
![Formula](data:image/jpg;base64,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)
) como la media ponderada de la potencia del LED durante los intervalos de velocidades del WLTC con la fórmula 1 siguiente.

Fórmula 1

![Formula](data:image/jpg;base64,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)

donde:

|  |  |
| --- | --- |
|  | Formula  es el consumo de potencia (media de las n de mediciones) para cada clase. |

|  |  |
| --- | --- |
|  | Formula  es el porcentaje de tiempo del WLTC por intervalo de velocidad en cada clase (el WLTC dura en total 1 800 s): |

Cuadro 2

|  |  |  |
| --- | --- | --- |
| Intervalo de velocidad | Tiempo | WLTC\_sharec (%) |
| < 50 km/h | 1 058 s | 0,588 (58,8 %) |
| 50 – 100 km/h | 560 s | 0,311 (31,1 %) |
| > 100 km/h | 182 s | 0,101 (10,1 %) |

Cuando el AFS de haz de cruce solo tenga 2 clases que no cubran todas las velocidades del WLTC (por ejemplo, C y V), la ponderación de la potencia de la clase C también incluirá el tiempo del WLTC no cubierto por la segunda clase (por ejemplo, tiempo «t» de la clase C = 0,588 + 0,101).

El ahorro de potencia resultante de cada luz exterior LED eficiente (ΔPi) se calculará mediante la fórmula 2 siguiente:

Fórmula 2

![Formula](data:image/jpg;base64,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)

donde el consumo de potencia de la luz correspondiente del vehículo de referencia se especifica en el cuadro 3.

Cuadro 3

Consumo de potencia de las diferentes luces del vehículo de referencia

|  |  |
| --- | --- |
| Luz del vehículo | Potencia eléctrica total (PB)  [W] |
| Luz de cruce | 137 |
| Luz de carretera | 150 |
| Luz de posición delantera | 12 |
| Luz de matrícula | 12 |
| Luz antiniebla delantera | 124 |
| Luz antiniebla trasera | 26 |
| Indicador de dirección delantero | 13 |
| Indicador de dirección trasero | 13 |
| Luz de marcha atrás | 52 |
| Luz angular | 44 |
| Luz de giro estática | 44 |

4.   CÁLCULO DE LA REDUCCIÓN DE LAS EMISIONES DE CO2 Y MARGEN DE ERROR ESTADÍSTICO

4.1.   Cálculo de la reducción de las emisiones de CO2

La reducción total de las emisiones de CO2 del sistema de iluminación se calculará de acuerdo con el grupo motopropulsor específico del vehículo (es decir, convencional, VEH-SCE).

4.1.1.   Vehículos convencionales (motor de combustión interna únicamente)

La reducción de las emisiones de CO2 se calculará de acuerdo con la fórmula 3 siguiente:

Fórmula 3

![Formula](data:image/jpg;base64,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)

donde:

v: Velocidad media de conducción del WLTC [km/h], es decir, 46,60 km/h

ηA
: Eficiencia del alternador, que es de 0,67

VPe
: Consumo de potencia efectiva especificado en el cuadro 4.

Cuadro 4

Consumo de potencia efectiva

|  |  |
| --- | --- |
| Tipo de motor | Consumo de potencia efectiva (VPe)  [l/kWh] |
| Gasolina | 0,264 |
| Gasolina turbo | 0,280 |
| Gasóleo | 0,220 |

CF: Factor de conversión (l/100 km) - (g CO2/km) [gCO2/l], como se define en el cuadro 5.

Cuadro 5

Factor de conversión del combustible

|  |  |
| --- | --- |
| Tipo de combustible | Factor de conversión (l/100 km) - (g CO2/km) (CF)  [gCO2/l] |
| Gasolina | 2 330 |
| Gasóleo | 2 640 |

UFi
: Factor de utilización de la luz de vehículo [-], como se define en el cuadro 6.

Cuadro 6

Factor de utilización de distintas luces del vehículo

|  |  |
| --- | --- |
| Luz del vehículo | Factor de utilización (UF)  [-] |
| Luz de cruce | 0,33 |
| Luz de carretera | 0,03 |
| Luz de posición delantera | 0,36 |
| Luz de matrícula | 0,36 |
| Luz antiniebla delantera | 0,01 |
| Luz antiniebla trasera | 0,01 |
| Indicador de dirección delantero | 0,15 |
| Indicador de dirección trasero | 0,15 |
| Luz de marcha atrás | 0,01 |
| Luz angular | 0,076 |
| Luz de giro estática | 0,15 |

4.1.2.   Vehículos híbridos (VEH-SCE únicamente)

La reducción de las emisiones de CO2 se calculará de acuerdo con la fórmula 4 siguiente:

Fórmula 4

![Formula](data:image/jpg;base64,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)

donde:

ηDCDC
: Eficiencia del convertidor CC-CC

![Formula](data:image/jpg;base64,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)
: Factor de corrección de las emisiones de CO2

![Formula](data:image/jpg;base64,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)
, tal como se define en el anexo XXI, subanexo 8, apéndice 2, punto 2.2, del Reglamento (UE) 2017/1151.

La eficiencia del convertidor CC-CC (ηDCDC
) se evaluará de acuerdo con la arquitectura adecuada del vehículo, como se especifica en el cuadro 7:

Cuadro 7

Factor de utilización de diferentes luces de vehículo

|  |  |  |
| --- | --- | --- |
| N.o | Arquitectura | ηDCDC |
| 1 | Luces conectadas en paralelo a la batería de baja tensión (luces alimentadas directamente por la batería de alta tensión a través del convertidor CC-CC) | 0,xx |
| 2 | Luces conectadas en serie después de la batería de baja tensión; batería de baja tensión conectada en serie a la batería de alta tensión | 1 |
| 3 | Las baterías de alta tensión y de baja tensión tienen exactamente la misma tensión (12 V, 48 V,...) que las luces. | 1 |

En el caso de la arquitectura n.o 1, la eficiencia del convertidor CC-CC (ηDCDC
) será el valor máximo resultante de los ensayos de eficiencia efectuados en el intervalo de corrientes eléctricas de funcionamiento. El intervalo de medición será inferior o igual al 10 % del intervalo de corriente eléctrica de funcionamiento.

4.2.   Cálculo del margen de error estadístico

El margen de error estadístico del sistema de iluminación se calculará de acuerdo con el grupo motopropulsor específico del vehículo (es decir, convencional, VEH-SCE).

4.2.1.   Vehículos convencionales (motor de combustión interna únicamente)

Se cuantificará el margen de error estadístico de los resultados de la metodología de ensayo ocasionado por las mediciones. En relación con cada luz exterior LED eficiente incluida en el sistema, se calculará la desviación estándar de acuerdo con la fórmula 5:

Fórmula 5

![Formula](data:image/jpg;base64,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)

donde:

n: Número de mediciones de la muestra (mínimo 5).

En caso de que la desviación estándar del consumo de potencia de cada luz exterior LED eficiente (
![Formula](data:image/jpg;base64,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)
) de lugar a un error en la reducción de las emisiones de CO2 (
![Formula](data:image/jpg;base64,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)
), ese error se calculará por medio de la fórmula 6.

Fórmula 6

![Image 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)

4.2.2.   Vehículos híbridos (VEH-SCE únicamente)

Se cuantificará el margen de error estadístico de los resultados de la metodología de ensayo ocasionado por las mediciones. En relación con cada luz exterior LED eficiente incluida en el sistema, se calculará la desviación estándar de acuerdo con la fórmula 7:

Fórmula 7

![Formula](data:image/jpg;base64,/9j/4AAQSkZJRgABAQABLAEsAAD/2wBDAAMCAgICAgMCAgIDAwMDBAYEBAQEBAgGBgUGCQgKCgkICQkKDA8MCgsOCwkJDRENDg8QEBEQCgwSExIQEw8QEBD/wAALCABeAaIBAREA/8QAHQABAAIDAAMBAAAAAAAAAAAAAAcIBAUGAgMJAf/EAEsQAAEDAwIDAgwCBgcECwAAAAECAwQABQYHEQgSIRMxFBUYIkFRV1iVltPVCTIWNGF3l7MXI0JxgZHSMzhW5CQ2VWJzdXaTtdHU/9oACAEBAAA/APqnSlKUpSlKUpSlKUpSlK4DWXVSRpRZbLNt+HTcnuOQ3yNYIFuiSmY63JDyXFJJceUlCUgNq33Pqrh75rtrzYoYmucHWXzgXA32cDJLQ+4NwevKH99unf8AtFaHypdcvcZ1P+JWr69PKl1y9xnU/wCJWr69PKl1y9xnU/4lavr08qXXL3GdT/iVq+vTypdcvcZ1P+JWr69PKl1y9xnU/wCJWr69PKl1y9xnU/4lavr08qXXL3GdT/iVq+vTypdcvcZ1P+JWr69PKl1y9xnU/wCJWr69PKl1y9xnU/4lavr08qXXL3GdT/iVq+vTypdcvcZ1P+JWr69PKl1y9xnU/wCJWr69PKl1y9xnU/4lavr08qXXL3GdT/iVq+vTypdcvcZ1P+JWr69PKl1y9xnU/wCJWr69PKl1y9xnU/4lavr08qXXL3GdT/iVq+vTypdcvcZ1P+JWr69PKl1y9xnU/wCJWr69PKl1y9xnU/4lavr08qXXL3GdT/iVq+vTypdcvcZ1P+JWr69PKl1y9xnU/wCJWr69PKl1y9xnU/4lavr13OkWs2o2ouQybPl/DjmOn8RiIqQ3cbzLhOsvOBaUhlIYdUrmIUVbkbbJPXuqXKUpSlKUpSlKUpSlKUpSoX4lP1vSH951o/kSqmilKUpSlKxLtdrXYbXLvd7uEeBb4DK5EqVJcDbTLSBupa1HolIAJJNeFkvVqySzwr/YpzU23XFhEmLJaO6HmljdK0n0gggg1nUpSlKUpSlKUrRWjOsNv+RXrELLk9tm3vHVNJu1uZkJVIhF1HO32rYPMnmSdwSOtbtZUlClIRzKAJCd9tz6q+f2quoXFppHpbYdfMs1Cutnzu65ymzN6bzGIMm2XCG9KU0xFjhhHahwspS72nalR87fY7V9AkklIJGxI6iv2lKUpSlKUpSlKUpSlKUqF+JT9b0h/edaP5EqpopSlRllHExoHhWWyMCyvVWwWzI4vZ9pan5G0r+sAKNmwOZW4IPmg12uIZji2f43BzDCr/CvVkubZdhz4TocZeSCUkpUPUQQfUQRW4pSqlasuo4p+JZnhmRMDmnenMWNkWoDLThAus11W8G1Ocqhu1sC84kjY7JB22q2TTTTDSGGG0tttpCEISNglIGwAHoFedKUpSlK0Wc5fAwHErnmNzt11nxbUwX3Y1rhLly3RuBs0yjdS1de4VA3l66Z+yfWv+Hlw/008vXTP2T61/w8uH+mnl66Z+yfWv8Ah5cP9NPL10z9k+tf8PLh/pp5eumfsn1r/h5cP9NcxxMMzIeMYvx56NY5c42T4dERLvFslQ1QpV6xpzbwqHLaUOYONJ3dRzAlspUdj0q0mH5XaM5xGzZrjj6ZFtvtvYuUNYUPPadbC0bkbjfZQ3qnWaaC8cWdaqXTUG5zNH21qIh4vJfmXCS/iMQq896E2WEtGWpPVTyxzEgJBSjpV3UghIBO5A6n11+0pSlKUpSlKUpSlKUpSlQvxKfrekP7zrR/IlVNFfOrju0YOkutNl4vzjkrMNPpT7MDUDHFvvKTHbUkNieylH5AEpRud/zpT6HFbWXxDhk4Rc8xa1ZpiWndouVmvcRqdBltSZPK8y4kKSobubjoe49QehqONR+AjH8Rvtr1m4Z7lkWPZni1waurNk8dvPW28IS6FPRFpkLUGe0aLjaSkhA3G6fSOlvWFZyOOA61OaO3a6Y7b9OTj1vubcmDzouKpKpCghtb6VpBbWWucj8xI/Keaug4GMMz3TXhzsGnWpGFzccvlifnB5l+THfQ6l6Y8+hTa2HFgjldSDvsdweh76sBSvB55mMy5IkOobaaSVrWs7JSkDckk9wAqov4bERWRabZ5rlcGXTc9UM7u12W+tYWlyK08WmEtqAHM2nZwAnvPN6K438SfINTNLrnptl2nWsmbY7+l2UxcduNug3FKIfg6k7lbaCglDnQ7q3IO/d0FbHi1Vq3wcYE3xCad685pkcKz3KDFuuK5a+zcIdxjuu8igh3s0usL3Unzkk9B3eu5GJX5OVYrZsoRGMZN4t8eeGSrmLYdbSvlJ9O3Ntv+yttSlKUr1S2PCor0Xt3We2bU32jSuVaNxtzJPoI7wfXXzz0cham5nxwawcPV/4jNVnMWwi1x5tqLV9Q3KC3RFUQ44GtlgdusDoO4b7mpC0x1X1W0c41GeD/ADXUG46j47kONfpBZrxeWmW7nbVIS+VsuuNISmQlRYX1KQRzI9R3ufSlYV7tEPILLPsNxb7SJcorsR9G5HM24gpUNxsR0J7qqz+GXeJS+G+RgU2a9Mc07yu84ql9aAlK22ZHaI5OpPKEvAed1GxHcBVs6UpSlKUpSlK4zVrVfFdGcOczTLTLXHMqNb4saGz2siXMkOBphhtO4HMtagN1EJHUkgDeuSxPiBn3DUAadahaVXzAZy7FKyFuVdbjAfiLisPssr/rY7ywlQU+ncK22HX0iu6Rqfpq4UhGoeMqK1BCQLtHPMonYAef1P7K6UEEbg9K/aUpSlKVC/Ep+t6Q/vOtH8iVU0Vq8otGPZBjd0seWQ4kqyT4b0e4szAksORlIIcDnN05eUnff0VT/wDDaxPJMNgakWfEsg8eaGIyJ4aez5TnNJfCVKTKU30G8fnASlZ2ClIWpI2USbqVxN21k09smq1k0UuV5cay/Ibc/dLfCER5SXYzJPOouhPZpI5VdCoHp+0b9tWFer3Z8btEy/5BdIttttvZVIly5TqWmWGkjdS1rUQEpA6knpXNaXav6d60Wa4ZDppkbd7tlsub1oflNNLQ34U0EFxKSsDmAC0+cNwfQTW3zj/qXf8A/wArl/ylVW78Lr/cg08/vun/AMjJqMfxb4vh2LaLQi++yJGokVousLKHEczahzIUPyqG+4PoNWCufBrphlM61v6j5Pn2dwrPMTcI1qybJpE23mQkHkcWwdkucu52Ctx1O4IJqG+LjUvOtLuJ3SDEmOIm64DgWft3NF5/qrW3GtiYcdvsiy7Ijq5OdxQCudShuoBO3QV2egdw1F1JnQs6xHXDMcw00yCLfbK7KuTdqakW+dEmBpi4Rlx46O1Q6GnglKkKACkqV37U/Dv1C1I1V0Yuue6pag3PJbmcjuFrbVKZjMtMMRlhKOVLLSPOO5Kid/RttVqaVAGYQ+PJ3KLo5gd60Mj46ZK/Fjd1iXZyYmPv5nbKbUEFe3U8o23PT11gW2F+Ie3cYq7tfeH56Cl9BktsQrwh1bXMOcIUVEBRTvsSCN9ulWOr5u6a6fNah/ig8QtuezDK8eEWywHw9j12XAec/qYA5VrQN1J677esA1cXAeF/TbTnIb/ndlkX2fm2QxDCkZVeriq5XRpnkCUoacfCkoSnYEJCdiQOYKAAqmOn/ENkycg1OxzVXjAzKNdcX1HTheOWe3tWJu4XGM5KRGQ8W3IZK+VSypakgJAQrpvsKkHiiyvia0J4aY97lavXpjKrVnabIxfERrcrx1aJMhRZedY7FSGnEoUEAJCTu2SRsrYXetMJdrt0W2v3SVcHWGghUmWpBefI71r5EpTufTskD9lZlVH4BPCV37iJet64xxlerl48VeDdn2XabI7cp5fR/sv2d+3pq3FKUpSlKUpSlV4465mAsaFpgan4ZKyXGLvkNqttxjwkuqmR2nZABkxg155eb/MkDffbbY77GEOGDFIWFcS0HGNGNar7q3pfPxecq8LvS03L9HpSVsdiwJZSOQulJ3jgA7I3Uk7JUOJ4RMbuea8E+X4RZdAY+dyrrkuQQ4rs6TAjw2XXCUIdUt1wPJ7MqB3QjmA/Kd6slptlV14StNtHNAdSIGTZlk17aNli3WA5HejOTkpcfMYuSHm1pS22ClClJAKGx6eg6+HxS4/Mj5+v+jjNG5GnUFq53KOpmEoyoi+33eiOIklp9KfBngoBYIKSNt+lSDpdqFa9WNPbBqTY7dPhWzJILVygtTktpfMd1IU2pSW1rCeZJB25txv12PSuqpSlKVC/Ep+t6Q/vOtH8iVU0VRfi/wCK7TG66qI4UMk1KfwfGWm0ys+yBoOdq/GUgKTaIxaQtQW8Fo7Vzpyt7pG5JAkyycffAvjdnhY/YNY7Hb7bbmERYkSNaprbTDSAAlCEhjYAAAACoqz78SfFdVMjxvQ7hRTd7lleaXRi2KyCTbFMRrPHU6A8+lt4AvOBoOKSCAkdFEkjlqXtVr5nuHcX2iFrtOod1ON5u1fYN0sTzEZcfeHA7Vt1DnZ9slSlqBUOfYlCdgOoMMzdXtZkTZCG+PK3spS8sJb/AKDpy+QBR2Tzbddu7f01NHC7meYZheb/AG/NeIiPqY23DbU1CRp4/jyIwKyFLUt1Oz2/QcoPTqdqxuBdxhVr1qSwtspTrLlGwQRsE9o1t3ej1VZh1pt5tbLzaXG3ElK0KG4UD3gj0iqj/hvOPYxp5qDojcnXfGGmefXe0lpwpHLFdc7ZhaGwTyNr5lkDu35tqmDVPhT0I1rvreR6n4W/fJzBbUyXLzOaaZWgbJWhpp5LaFgf2kpBPpNSJimLWbCseh4vjzUhq3QEFDCJEt6U4AVFR5nXlKcX1J6qUfV3VXTXzRHVjUHij0h1dx7GrBMxnTJF0VKamXUtSJ65jAb2Q32KkpCFJB3Urr16Dbrn2nAdcLdqVYZli05xnGdP8acvl5FohZS+7Iu93m8ykOPDsEobQFvSFFO60guAgeakCvsrTHVrhc4BdRsY1IsuCSHo13fvyGHpkifEurL76FmIUoMZxDgUkEKClAhP5TuQLYp1uah6xYBpQibjTUfK8aeuqGVrlJmuuNthaRFCWjHW0EJWVczoUkBOwO43mOlK9UuMzNivQ5AUWn21NLCVlJKVDY7EEEHY94O9QxjHBnw54dmaNQ8cwebDyRLrby7l+kNzW8+pspKA6VyD2qQUJ81fMk8o3FTRIW83Hdcjsh51KFFtsq5QtQHRO/o3PTeqbaP8OWrGDtauPZ1pNiGTOag5w5mduiDJVtNxVB5D7Dbj3g3OCh1tCiUpIO3cQSK0+tnDjxVazaL3nFr1AxJWVZFqEjKl8+RPmFbrbGS2mLDaJYJUrlRsohCBvzKO5VtUjzstuzXHJgdjyG0YsiVeMCuawlsuu3G1ONusqWwH+1DbiHCSoHsEq2Qeu29WPv8AeYWOWK5ZDcllES1xHpr6gCeVttBWo7DqeiTVXvwzrHNj8Nas8uVvchSdRcnu+WFhS90pRIfKG+Qd4SUNJI36nffuIq2FKVj3C4QLTCfud1nR4cOMguvyJDqW22kDvUpSiAkD1mtRjOf4JmpcGG5rYb92SQtzxZcmZXIncjc9mo7DcEb+sGt/SlKUpSvFSEr25kg7HcbjuPrrDuNsMu2SrfAnP2pyShSRKhobDrSiPzp50qRzD/vJI/ZUbcPHDpi3DVi83DcJyfJLlaJs1y49heH2HizIc2Lq0KbaQrziASCSN+7bc1xXFToZmuuGW6ZC3Y7Z7hjOHXxy93ZEq+yIEmUDHcZSy0WWipOxWFFfOnu22rUY3ofrJienmZ6b4xarNAxheLyrBi1kkZE5J5pEx6S7ImTJJihaSgvhCEpC90A8x5jvWix1ed6GXvhV0UzC3u7Jt8nHHn7NlDzcR2ZEtqt1vxewAktFDQUjmWkoWs7g7Am4FKUr8JAG5PSq5cO3F5adetbNVtLocFqNFwt+I5Y5HnBdzgqTyOydj0KO1AKFJ6FDiD/f03Ep+t6Q/vOtH8iVU0V6HIEF1ZcdhMLUrvUpsEn/ABrx8WW3/s+N/wC0n/6rybgwmVhxmGwhY7lJbAI/xqArXoPq9e9fcc1T1X1Fsd8suCO357GosK2qiykm4FLbYfIIQQzHSW/Tzk8x67bWErGuVug3eBItdziokxJbamX2VjdLiFDYpP7CK5LAtEtIdLZkq4ab6b49jMicnlkuWqA3GLw338/kA5uvrrtqqfqzGm8MvEpH4l4saWvTvPYbNh1D8HaC0WqU1smBdFoSObk6lpxZ35UkE+gVa1p1qQ0h9h1DjbiQtC0KBSpJG4II7wRXnSlet5hmQjs32UOJ332WkEb/AONV3yHBtWc94stP80uWCxbJiGmCb8GLqLo0/wCNW50NllkIZSAtpYWl0qCgQAlOxPN0sZSlKUpSvWY0cveEFhvtQNu05Rzf599Vc4x8uu+pvgfBzpPObeyzPeRGTyWStQx7Hd95El5SOiFugBptCvz856bEGrHYbiViwLE7NhOMQkxLTYYLFuhMJ7m2WkBCB/kB1rc0pVb+OTD8jzrBcNx/Ccpsduyf9MYc2y2u/MuO2zIJUdl94QJIQCOQpbU4OccpU0kdCQRB+mGW6n2rWfOLJnWnll0X1OYwyFOMzHrGL3ZrnZY850KkNxWVJd7ULkchUXFbJb35RtXXxeIfJ5mDwdTmuLW1jD7g6Wmb+rRq4pgApWW1KW8ZHK2lK0qSVLISCOpqYrZifEperdFu9o4ncOmwZrKJEaTHwEONPNKG6VoUJ+ykkEEEVk/oBxS+8div8Pf+fp+gHFL7x2K/w9/5+u90+suodktkiPqNnNuyict/nYkwrJ4sQ01ygchb7Z3mO4J5tx37bdN6rBxXytNk6opbyrLOJ+JNTbWP+j6couAtLaN1bblhooLp6lXnE/l327qhnwrRH/jfjx/yvH0quzwyDHf6ILWvF7tqHcYC35Kku574R45Cu1VzJe8ISlfKD+Xpty7bEiun1DnarQokNWlWOYxd5KnVCWi+3Z+ChtvbzShTLDxUd+8EDp6TXDeP+MH2Y6T/ADjcPt1PH/GD7MdJ/nG4fbqeP+MH2Y6T/ONw+3U8f8YPsx0n+cbh9urQ3mw8ReRX+1ZVftBdEbhebEortk+Tk81x+Eo77lpZtu6Cdz1Tt31Len0zU6bbJDmqWP43abgl/aO1Y7o9OaWzyjzlLdYZKVc3MNgkjYA7+iuL4pnsdZ0mknKLzqVbYKpsZJc09TIN4WrmJCE9ghSw303Ueg2ABPXY0v8ACtEf+N+PH/K8fSqb+Edem72o81WKZTxLzZyLU7vH1K8YeLFNlxvdTfbthHbA8u3XflKtt+u1trlb4t2t0q1zUqVHmMrjvJStSFFC0lKgFJIKTsT1BBHoqjesuMWnhm44tCNUsaiotGJZjbFaaXRtpJDSFIQBBSfOA6ksgb+hgkbnusLxKfrekP7zrR/IlVM6ublPIAVbdAe7eobcv3F6HFBrTLSgo3PKVZhcASPRuPF9ePj/AIwfZjpP843D7dTx/wAYPsx0n+cbh9up4/4wfZjpP843D7dTx/xg+zHSf5xuH26nj/jB9mOk/wA43D7dTx/xg+zHSf5xuH26nj/jB9mOk/zjcPt1eifcOLO6wZFsuekukEuHLaUxIjv5bPW262obKQpJt2ykkEgg9CDXrs7/ABV49aoljsWkGj0C3QGUx4sWNls9tphpI2ShCRbtkpAGwA6Csvx/xg+zHSf5xuH26nj/AIwfZjpP843D7dTx/wAYPsx0n+cbh9up4/4wfZjpP843D7dTx/xg+zHSf5xuH26nj/jB9mOk/wA43D7dTx/xg+zHSf5xuH26nj/jB9mOk/zjcPt1PH/GD7MdJ/nG4fbqeP8AjB9mOk/zjcPt1PH/ABg+zHSf5xuH26nj/jB9mOk/zjcPt1PH/GD7MdJ/nG4fbqeP+MH2Y6T/ADjcPt1am2QeJiy3m65FZ9DtFYV1vq23LnNj5PNbfmqbTyNl5YtvM4Up6DmJ2HQVtvH/ABg+zHSf5xuH26nj/jB9mOk/zjcPt1SZhsjM5WPRn8/tdot18UV+ER7TNclxUDmPJyOuNtqVunYndA2JI699buo91m0K0/15tdks2oUe4uxsfu7V8g+Az3YbjcxttxDa+0aIWOXtCobEdQPVtWhh6F4hp3a8szNFzv8AkeTzsfkW1y+5DcVTpqYSUrWmMhRAS21zkqKUpHMdirfYbVV4O9NdV9Yfw7MZ0xx7KsWx+wZLGu1tnzJFtemzUw3Z0hLwbT2iGkuEFWyjzADbpv1E1agahucGVl0a0mx9GLs4XcFtYmLzkNycjG3rYjKWH3VbcikLDZB3UnZSh6O7DkcVWrSdJtRtYrXg+IXfF8TtQvmP3qHcpXgWQRW1yUSm087QW062qONuikHnSQoghVWFwbLXL/huMX3IXbdCuWRW9iYiKy95pU40HChvn2UvlSrr09BNdPSlKUpVcuKVu56sZXhPC1YpsmLFy15d9zKVEfU09Fx6EtBU2FpIUhUl9TTKVD0Bz1VpXeATgzZ7btrPcm/B3kR3ubOboOzdXtyoVvK6KO42B6ncVKWjfDFo7oJcbldtMrHcoMm7MIjylS73NnBSEKKkgJkOrCTuT1SAalWlKUqtf4hulcvVLhaylVkQoX/EA3ldndbSkuNyIR7RXJv3Etdqkbekjv7q1M/VOHrZo9w3apQ1oJyHNrDJkJSoHs5IjSkvt9OnmupWn/DuFWqpSlKUpSsZ6522NITDkXCM0+ptbyWlupSstp/MsAnflG43PcN684kyJcIzc2BKZkx3khbbrKwtC0nuIUOhH7RUO8T2qWU4JYsWwzTpxhrNNSMhj4xZ5DzfapgJcSpcmcUf2+wYQtYB6FXLuCN6kjGrTbdP8TtOOS8ilS24DLUMXC7zO0ky3T053HFbczi1Enp6TsABsK3Ea4QJrj7UObHfXFc7J9LTiVFpewPKoA+adiDsevWsilK02Y3DJ7Vjc6fhmNx79eWkAxLdIniE2+oqAIU8ULCAASfynfbb01APCjrvq7qvqlrVhGq9vx6A7p7drdbokSylx1prtWXVOAvOBKndyhJ3KE7ddhtW0yzMch0T4l8Vt825zbhg2sjztrQxIcW6LJfmGi40WSSeVmS2FhSO5K2gobAqqwdKUpSlKUpWsyXGrHmFjmY1ktubn2y4NlmVGcJCHmz3pVsQSD6R6R0NabTnSnTrSOzuY/ppiMDHLY66XlQ4KChnnPeoI32STv12239NcXrToNctXszwPKhm0a2R8CuS7vEt71mTMblSlNLa3eUXUkoCHDskAdepJ6bcXkHB7PyDGs1xg6mxbbEy6xnGo8e2423Hi2i3OvOvykMMh4hTjzrxUXFlRTtsnbetLqFGvmnWu3DPiXhllv0aKmdZZTz+PNqkwkIgrS3LafKlKjF0obaIB2UQdj12q11KUpXHatZvftPMCumU4xgV1zG6RGVqi2i2qbS484EKI5lLUAhG485Q3I36JJ6Vy3D9xD4prloJaNckvMWuI7BcevLK3N022QwCJTayeuyClRBI6pKT6a4XhVx57VBOZ8T2b21RkasK8FskOSnzoeKMcyITO39kvArkLHpLyfVVeeErhx0ay7M+Ki35DhMSdCtuY3HH7bEeKnGbfE5VkGOhRKW3Ryo2dA50hCQCNqsvwAXW5Xrg40tn3ee/MkmzKaLz6ytZQ2+6hAJPU7ISlI/YBVg6UpSvVJjR5sZ2HKaS6w+hTTjahuFoUNiD+wg18y9BZUjTLLn+Ei7PntNNddrZPsiFk7qs85mW4zt0A2B2UdvS93D0/TmlKUpSlKoJqDoxppkf4pWMW664uw7Cm6fyMinxQtYZnT0zHEhyQjfZ0eaglCvNJQjcHapT4PLBbMH1m4kNPMYZMHG7Nl9ukW22NnaPCMq3NvPJZR3NoKz0SnYAAAd1ZmtrLEnjh4bmpTTbqWrdmL7aXEhQS6mHHCVgHuUATse8bmoz/FKwHFcismj90uFtKblP1JsthcnsOqakpgvF5S2kOJIKfO2UCOoUAR3V1jum2F6Rcd+lsTTSxs47EyHBL7EusWDu2zNTDVGLC3Uj/aODn6uK3Wdk7k7Va2/P3mNZLhIx2BHnXVqM6uFFkPllp58JJbQtwJUUJKtgVcp2B32NV5/TP8QT2H6QfOEz/wDNXXaW5LxZ3LLGomsGlun9mx1bLhcmWTJJEuS26Bugdk4wkKST0PnDbffrttUz1Tvg1/3qOLX/ANWWr+Q/XV8d3m4rpK6notGsGJ8qh3p3lKB2Po3B2qzNKUpSlKUpSlKUpSlKUr5oXXRXVTDeJLOeDnELe+1pbrlcmM1mXVtxSRa7UhZN1iNgeaFPK7NjpsQhbffv0+kCLLAj2NOO29tUCE3EEJhERRaMdoI5Ehsp6oKRtsR3bCoNxzgk0oxJvKG8dyvUmCrM3VyL46zmU5Lk19agVvLUF7lxWxSV95SpQ7jUg6KaI4boFh6MDwKXfFWRhZVFi3O6OzUxEnvbZ7Qns0bkq5R03JPpqQKUpSlUH4q9KZdh/ED4e9ZLSHm4OV3JFju4QpQbXJipW4wVjuJLazt/4Pd0Jq/FKUpSlKVCt64StNb7q9/TlKv+cNZclsxmpcfJ5TSGIpUVGM2hKtkMFRJ7Meb1NZuk/DFp/o1mF7zjE77mcm6ZIe0u5u2Rypzc57YBLzqHVEKcSkcqVHqEkgdK5bjAwi/PQMI12w61vXO+aPX4ZCu3x2Q4/PtSm1NXBhobElwsKKkpHepAHeQRtdQNEtGOL+w4jnt3yHIbjZWER7zYzaL8/EjJeG6mpPI0oDt0c6k8x85PUdNq81cI+nTupFn1Yl5bqJKyWxNJjwZUjLpjgbYHLzs8pXsW3CgFxPcvc829TdSla7IbK1kdjnWJ64XCC3PYUwqTb5S40loKG3M06ghSFD0KHUVDenHBxpbpTmc3PcOyPP2LrdpyLjdlP5ZMebur6OblVLQpRD/51dFb99c1qMw1xE8S2IaeWpvwnFdGbkjKspmjzmXLz2Shb7ek/lU4jnU+4OpRs13FVWapSlKUpSlKUpSlKUpSlfnKnm5+Uc222+3Xb1V+0pSlKUpWtvGN4/kD1ukXyyQp7tolpn29clhLioslKVJDrZI8xYSpQ5h12UfXWypSlKUpSlKVjwLfAtcVEG2Qo8SM3vyMsNJbQncknZKQANySf7yayKUpSlY8S3wIHbeAQo8bwl5Uh7sW0o7R1X5lq2HnKOw3J6nasilKUpSlKV//2Q==)

donde:

n: Número de mediciones de la muestra (mínimo 5).

El coeficiente de corrección de las emisiones de CO2

![Formula](data:image/jpg;base64,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)
 se determinará a partir de un conjunto de T mediciones realizadas por el fabricante, de acuerdo con el anexo XXI, subanexo 8, apéndice 2, punto 2.2, del Reglamento (UE) 2017/1151. En cada medición se registrarán el balance eléctrico medido durante el ensayo y las emisiones de CO2 medidas.

Para evaluar el error estadístico del factor 
![Formula](data:image/jpg;base64,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)
, se utilizarán todas las T combinaciones sin repeticiones de las T-1 mediciones para extrapolar T valores diferentes del factor 
![Formula](data:image/jpg;base64,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)
 (es decir, 
![Formula](data:image/jpg;base64,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)
). La extrapolación de realizará de acuerdo con el método establecido en el anexo XXI, subanexo 8, apéndice 2, punto 2.2, del Reglamento (UE) 2017/1151.

La desviación estándar de 
![Formula](data:image/jpg;base64,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)

![Formula](data:image/jpg;base64,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)
 se calculará de conformidad con la fórmula 8.

Fórmula 8

![Formula](data:image/jpg;base64,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)

donde:

T: Número de mediciones realizadas por el fabricante para la extrapolación de 
![Formula](data:image/jpg;base64,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)
 como se define en el anexo XXI, subanexo 8, apéndice 2, punto 2.2, del Reglamento (UE) 2017/1151.

![Formula](data:image/jpg;base64,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)
: Media de los T valores de. 
![Formula](data:image/jpg;base64,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)

En caso de que la desviación estándar del consumo de potencia de cada luz exterior LED eficiente (
![Formula](data:image/jpg;base64,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)
) y la desviación estándar de 
![Formula](data:image/jpg;base64,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)

![Formula](data:image/jpg;base64,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)
 den lugar a un error en la reducción de las emisiones de CO2 (
![Formula](data:image/jpg;base64,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)
), ese error se calculará por medio de la fórmula 9.

Fórmula 9

![Image 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)

4.3.   Margen estadístico en relación con los AFS de haz de cruce

En caso de que esté presente un AFS de haz de cruce, la fórmula 9 se adaptará para tener en cuenta las mediciones adicionales necesarias.

El valor de la incertidumbre (
![Formula](data:image/jpg;base64,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)
) que debe utilizarse en relación con el AFS de haz de cruce se calculará por medio de las fórmulas 10 y 11 siguientes:

Fórmula 10

![Formula](data:image/jpg;base64,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)

Fórmula 11

![Formula](data:image/jpg;base64,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)

donde:

n: Número de mediciones de la muestra (mínimo 5)

![Formula](data:image/jpg;base64,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)
: Media de los n valores de Pc.

5.   REDONDEO

El valor calculado de la reducción de las emisiones de CO2 (
![Formula](data:image/jpg;base64,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)
) y el margen de error estadístico de esa reducción (
![Formula](data:image/jpg;base64,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)
) se redondearán a un máximo de dos decimales.

Cada valor utilizado en el cálculo de la reducción de las emisiones de CO2 puede aplicarse sin redondear o redondeado al mínimo de decimales necesario para que la repercusión combinada de todos los valores redondeados en la reducción sea inferior a 0,25 g CO2/km.

6.   SIGNIFICACIÓN ESTADÍSTICA

Se demostrará que, en relación con cada tipo, variante y versión de un vehículo equipado con el sistema de iluminación LED eficiente, la incertidumbre en la reducción de las emisiones de CO2 calculada de conformidad con la fórmula 6 o la fórmula 9 no es superior a la diferencia entre la reducción total de las emisiones de CO2 y el umbral de reducción mínima indicado en el artículo 9, apartado 1, del Reglamento de Ejecución (UE) n.o 725/2011 (véase la fórmula 12).

Fórmula 12

![Formula](data:image/jpg;base64,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)

donde:

|  |  |  |
| --- | --- | --- |
| MT | : | Umbral mínimo [g CO2/km] |
| Formula | : | Reducción total de las emisiones de CO2 [g CO2/km] |
| Formula | : | Desviación estándar de la reducción total de las emisiones de CO2 [g CO2/km]. |

En caso de que la reducción total de las emisiones de CO2 derivada del sistema de iluminación LED, determinada de acuerdo con la metodología de ensayo establecida en el presente anexo, se sitúe por debajo del umbral previsto en el artículo 9, apartado 1, letra b), del Reglamento de Ejecución (UE) n.o 725/2011, será de aplicación el artículo 11, apartado 2, párrafo segundo, de dicho Reglamento.

---

[(1)](#ntc1-L_2019176ES.01007101-E0001)  
[DO L 4 de 7.1.2012, p. 17](./../../../legal-content/ES/AUTO/?uri=OJ:L:2012:004:TOC).

[(2)](#ntc2-L_2019176ES.01007101-E0002)  
[DO L 213 de 18.7.2014, p. 1](./../../../legal-content/ES/AUTO/?uri=OJ:L:2014:213:TOC).

[(3)](#ntc3-L_2019176ES.01007101-E0003)  
[DO L 285 de 30.9.2014, p. 1](./../../../legal-content/ES/AUTO/?uri=OJ:L:2014:285:TOC).

[(4)](#ntc4-L_2019176ES.01007101-E0004)  
[DO L 250 de 22.8.2014, p. 1](./../../../legal-content/ES/AUTO/?uri=OJ:L:2014:250:TOC).

[(5)](#ntc5-L_2019176ES.01007101-E0005)  
[DO L 237 de 8.8.2014, p. 1](./../../../legal-content/ES/AUTO/?uri=OJ:L:2014:237:TOC).

[(6)](#ntc6-L_2019176ES.01007101-E0006)  
[DO L 148 de 12.6.2010, p. 55](./../../../legal-content/ES/AUTO/?uri=OJ:L:2010:148:TOC).

[(7)](#ntc7-L_2019176ES.01007101-E0007)  
[DO L 323 de 6.12.2011, p. 46](./../../../legal-content/ES/AUTO/?uri=OJ:L:2011:323:TOC).

[(8)](#ntc8-L_2019176ES.01007101-E0008)  
[DO L 302 de 28.11.2018, p. 114](./../../../legal-content/ES/AUTO/?uri=OJ:L:2018:302:TOC).

[(9)](#ntc9-L_2019176ES.01007101-E0009)  
[DO L 250 de 22.8.2014, p. 67](./../../../legal-content/ES/AUTO/?uri=OJ:L:2014:250:TOC).

[(10)](#ntc10-L_2019176ES.01007101-E0010)  
[DO L 89 de 25.3.2014, p. 101](./../../../legal-content/ES/AUTO/?uri=OJ:L:2014:089:TOC).

[(11)](#ntc11-L_2019176ES.01007101-E0011)  
[DO L 222 de 24.8.2010, p. 1](./../../../legal-content/ES/AUTO/?uri=OJ:L:2010:222:TOC).

[(\*1)](#ntc*1-L_2019176ES.01007101-E0012)  Las velocidades de activación deben controlarse para cada aplicación del vehículo de conformidad con el Reglamento n.o 48 de la CEPE/ONU, sección 6, capítulo 6.22, apartados 6.22.7.4.1 (clase C), 6.22.7.4.2 (clase V), 6.22.7.4.3 (clase E), 6.22.7.4.4 (clase W).

---

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