SKU: 54553505522

NGK Spark Plug for Ford, Volvo, Fiat, Citroën & Peugeot - BCPR7ES

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Description

NGK Spark Plug for Ford, Volvo, Fiat, Citroën & Peugeot - BCPR7ESVehicle Fitment & Part Details This NGK spark plug (BCPR7ES) is supplied as an OEM grade replacement. Heat range 7. Thread M14 x 1,25. Verify VIN engine before ordering. Key Details SKU BCPR7ES Component Spark Plug Brand NGK Heat Range 7 Part MPN 3330 GTIN Barcode 087295133309 Trade Numbers BCPR7ES Product Specifications Thread Size M14 x 1,25 Thread Length [mm] 19,0 Spanner Size 16 mm Spark Position [mm] 3,0 Electrode Construction Nickel Middle

Vehicle Fitment & Part Details

This NGK spark plug (BCPR7ES) is supplied as an OEM-grade replacement. Heat range 7. Thread M14 x 1,25. Verify VIN/engine before ordering.

Key Details

SKU
BCPR7ES
Component
Spark Plug
Brand
NGK
Heat Range
7
Part MPN
3330
GTIN / Barcode
087295133309
Trade Numbers
BCPR7ES

Product Specifications

Thread Size
M14 x 1,25
Thread Length [mm]
19,0
Spanner Size
16 mm
Spark Position [mm]
3,0
Electrode / Construction
Nickel Middle Electrode, 1 - Earthed Electrode, Interference Suppression 5 kOhm, with gasket seat, Fixed SAE connector
Brand Class
Premium

Vehicle Compatibility

Common fitments include Fiat (FIORINO, PUNTO, SCUDO, TEMPRA, TIPO); Ford (ESCORT, FIESTA, ORION); Peugeot (106, 306, 405, 605, EXPERT); Volvo (C70, S70, S80, V70); Aston Martin (DB7). Always confirm the correct heat range and electrode design for your specific engine variant by VIN before ordering.

OEM Reference Numbers

Vehicle OE Numbers

  • Alfa Romeo: 58 94 58 8, 77 60 38 4, 7GY SSR
  • Ferrari: 77 60 38 4, 7GY SSR, 58 94 58 8
  • Fiat: 7GYSSR, 5894588, 7760384, INFINITI, 22401 12E17
  • Maserati: 58 94 58 8, 77 60 38 4, 7GY SSR
  • Nissan: 22401 12E17
  • Peugeot: 5962 KO, 5962 V8, 5962 W7, 5962 K1, 5962W 6

Aftermarket Equivalents

  • Borgwarner (Beru): 0002345702, 14FR 5DU, Z 30
  • Bosch: FR5DC, 0242245515, FR5DP, 0242245520, 0242245536
  • Champion: OE014, OE 043 B04, OE094 T10, RC7Y, OE110 T10, OE043 R04, OE014 R04, OE 094, +16 more
  • Denso: Q22PR U, 3347, 3010, D 23, Q22PRU
  • Magneti Marelli: 8 LPR, CW 8 LCR, 061 830 072 304, 8 LCR
Alternate OEM Search References: 2240112E17, 5962KO, 5962V8, 5962W7, 5962K1, 5962W6

Fitment Notes

  • Always match heat range, thread size and electrode design before ordering. Verify VIN/engine.

Important: Spark plugs are not returnable, exchangeable, or refundable once fitted. Confirm you are ordering the correct part for your vehicle. Compare the new spark plug to the removed plug for thread reach, thread diameter, hex size, electrode style, gap specification, and heat range before fitting. Mismatched spark plugs can cause misfires, pre-ignition, detonation, fouling, or engine damage.

Returns Notice: Once a spark plug has been fitted or threaded into a cylinder head, it cannot be returned, exchanged, or refunded under any circumstances.

Installation / Use / Maintenance Tips

  • Allow the engine to cool fully before removal - cylinder heads are easily damaged when warm.
  • Use the correct deep-socket plug spanner (16 mm) and avoid impact tools.
  • Inspect the existing plug for fouling, oil contamination, or worn electrodes - may indicate engine condition issues.
  • Verify the gap if your supplied plug is not factory-set; consult your service manual for the correct value.
  • Apply a small amount of anti-seize to the threads (sparingly) and torque to manufacturer specification.

Common Questions

Will this NGK spark plug fit my Ford?
This part (BCPR7ES) is listed for the applications shown on this page. Always confirm the correct heat range, thread size and electrode design against your vehicle's service manual or a parts lookup before ordering. Verify VIN/engine.

What does the NGK part code mean?
The NGK trade number encodes physical and performance characteristics - thread/reach, electrode design, heat rating, and projection. Each digit and letter has meaning in NGK's naming convention. The heat range is usually the standalone digit (e.g. BPR6ES is heat range 6).

What is the difference between this NGK plug and a Bosch / Champion / Denso equivalent?
The cross-references listed under OEM Reference Numbers are functionally equivalent for the same applications, but materials, electrode design and heat dissipation can differ slightly. NGK is OEM-supply quality and most manufacturers specify NGK or Denso as their factory plug.

How do I confirm fitment if my engine variant isn't listed?
Match the supplied thread size, reach, electrode design and heat range against your existing plug. If unsure, request fitment assistance before ordering.

Shipping Notes
  • Free Standard Shipping on $100+ Orders to the USA.
  • Except Preorder products are shipped in 48 hours.
  • Delivery to the USA:
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  • If time is of the essence, please consider selecting expedited delivery for faster service.
Exchange/Return Notes
  • We offer a 30-day return/exchange service after receiving.
  • Final sale items are not eligible for returns or exchanges.
  • To process your return/exchange, please contact us at [email protected]
  • Please click here for more details>>> Return & Exchange Policy
SKU: 54553505522

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4.4 ★★★★★
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Shannon
Belleville, US
★★★★★ 5
The best DL/ML book I have ever seen!!
Format: Hardcover
Fantastic deep-learning book! The logic is very easy to follow, but the content is very thorough when it comes to explaining the theories behind it, making it perfect for beginners as well as math and CS students. The best DL/ML book I have ever seen!!
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Reviewed in the United States on November 30, 2025
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William P Ross
Birmingham, US
★★★★★ 5
Comprehensive Look At An Incredibly Complex Topic
Format: Hardcover
Deep Learning is an advanced book with great explanations and details. There is a heavy math focus with the book's beginning chapters detailing the necessary linear algebra and probability that one will need to understand deep learning. I liked that the author's chose to cover only the parts of these subjects which are relevant to deep learning. There are many interesting philosophical sections in the book as well. Just about when I was feeling overwhelmed with the complexity of the mathematics the authors take a step back and cover the foundations of deep learning such as borrowing concepts from human learning. There was an interesting dicussion about the early studies done on the vision of cat's and monkey's in the 1970s. The text covers the entire history of deep learning and the bibliography is hundreds of sources. It is clear this is the most comprehensive text available about deep learning. For anybody interested in this topic this book is a mandatory read. There are sections about machine learning as well, which makes sense because deep learning is a subset of machine learning. These sections focused on the machine learning concepts which are most relevant to deep learning. The book was well organized and divided into three parts which cover mathematics related to deep learning, typical deep learning techniques, and then more experiment learning techniques. Often the author's state when a technique works well or when it does not, and which types of data works best for the technique. Just a warning, the math in this book is highly complex. It requires a lot of work to go through this book, but the effort will be well rewarded.
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Reviewed in the United States on March 15, 2017
A
Verified Purchase
Adam
Chelsea, US
★★★★★ 4
Too Dry.
Format: Hardcover
This was a required textbook for my class in college. I think it was too dry. The book titled Deep Learning: From Curiosity To Mastery is much more approachable.
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Reviewed in the United States on May 22, 2026
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Amazon Customer
Lowell, US
★★★★★ 5
Comprehensive! The Bible of Deep Learning!
This book has by far surpassed my expectations! I have purchased many machine learning and deep neural network books in the past, but nothing has ever come close to this book! First of all, it is written by the fathers of Deep Learning, and is therefore an authority. Secondly, the book is broken into three parts: 1. A math overview and refresher. 2. Deep Learning applications and 3. Research in Deep Learning. I can't help but go through this book from front to back. It is a smooth read, and every sentence written is meaningful. These guys know their stuff! And after you read this book, YOU WILL ALSO know your stuff! If you feel daunted by the price, just remember, you get what you pay for! I'd say they could easily charge about $300+ for this book, but they are doing everyone a very kind favor by ONLY charging this reasonable amount. You get A LOT of bang for your buck with this purchase. I hesitated at first about buying this book because of the price, but I am soooooo happy that I did! Worth every penny! Look no further, get this book and start your Deep Learning journey!!
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Reviewed in the United States on July 14, 2017
M
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mackster
Charlottesville, US
★★★★★ 1
A rushed, poorly written guide of how the "experts" can't really explain what Deep Learning is
Format: Hardcover
This book, in every sense of the word, is rushed. I think the authors wanted to establish themselves as leaders of this young-ish field, but does so by sacrificing quality. It also shows that Deep Learning theory has been there for a long time, known by another name called Neural Networks. The interesting algorithms are of MLP, Back Propagation and the classical neural networks. The optimization methods such as Adam are the ones that are new and interesting, and the only ones worthy of in this book. So, essentially, what you get from this book is use A for X, B for Y and C for Z type of dry, un-intuitive, badly written waste of paper. As for the structure of the book, it's like an example of how not to structure a book. It has some linear algebra, probability at the start (not good enough, and confuses more people and wastes paper). Goes on to prove other algorithms such as PCA (yeah, ok!). Then, talks about how this architecture works for this and that architecture. So, yeah, if you really want to try out deep learning, don't buy this book. Set up Tensorflow/pytorch/ other library, run the tutorials, find an architecture for the problem you are interested in and start tweaking that. You will have far more fun and would have saved your money. The praise that this book gets is beyond me. Did Musk even read this book? I doubt it.
WAS THIS REVIEW HELPFUL?YesReportShare
Reviewed in the United States on May 15, 2018

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