The D250SE (CTEK Part No. 40-315) is a DC to DC fully automatic charger that’s designed to charge a Service Battery and is powered by the alternator/starter battery. With dual inputs the D250SE is also solar compatible (a standard solar controller is not necessary). The D250SE charger supplies up to 20A of power to any 12V lead-acid or lithium service battery from 40–300Ah. It also works if the starter battery and service battery are different chemistries, for example if the starter battery is lead/acid and the service battery is lithium. (Note: a service battery may also be called an auxiliary/aux battery, house battery, secondary battery, or backup battery.) This is a great charger for our V1 and V2 Lithium Deep Cycle Batteries.
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Description
CTEK D250SE 12V Smart Charger 20AThe D250SE (CTEK Part No. 40 315) is a DC to DC fully automatic charger thats designed to charge a Service Battery and is powered by the alternator starter battery. With dual inputs the D250SE is also solar compatible (a standard solar controller is not necessary). The D250SE charger supplies up to 20A of power to any 12V lead acid or lithium service battery from 40300Ah. It also works if the starter battery and service battery are different
Dual Inputs
The D250SE CTEK Charger has dual inputs and can charge the service battery from the alternator or a solar panel or both. The charger is designed to work with smart alternators and can still operate if the alternator is shut off but the vehicle has access to solar or wind power. So it can use power from the alternator, solar panel, wind power, or any DC power source can be used through the solar input port (within voltage spec). The D250SE can also charge the service battery from the solar input and the alternator in parallel using its built-in regulator to save fuel.
Optimum Charge
Continuous charging while the vehicle’s on the move means the batteries are fully charged in the shortest possible time. The built-in temperature sensor compensates for hot or cold conditions to prevent the battery from overheating or overcharging, so it always gets the optimum charge.
When the service battery being maintained is fully charged, the D250SE will automatically redirect overflow charge power to top-off the starter battery. The D250SE can maintain a stable output up to 20A to vehicles fitted with smart ECU controlled alternators.
LITHIUM
5-Step Program
- Accept*: Tests if the battery can accept charge. This step prevents that charging proceeds with a defect battery. (*Step 1 for lead/acid is Desulphation)
- Bulk: Charging with maximum current until approximately 90% battery capacity*. (80% capacity for lead/acid.)
- Absorption: Charging with declining current to maximize up to 95% battery capacity*. (100% capacity for lead/acid.)
- Float: Maintaining the battery voltage at maximum level by providing a constant voltage charge.
- Pulse: Maintaining the battery at 95–100% capacity. The charger monitors the battery voltage and gives a pulse when necessary to keep the battery fully charged.
Video about D250SE Charger
Specs
- CTEK PN: 40-315
- Input: 11.5–23V, 25A
- Output: Max 14.4/14.2/14.7V, 20A max
- Back-Current Drain*: Less than 1Ah/month
- Ambient Temperature: -4°F to +122°F (-20°C to +50°C)
- Battery Types: 12-Volt WET, EFB, Ca/Ca, MF, AGM, GEL, Lithium (LiFePO4, Li-Fe, Li-iron, LFP)
- Battery Capacity: 40–300Ah
- Insulation class: IP65 (splash and dust proof)
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4.1 ★★★★★
Based on 1611 reviews
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Product Reviews
★★★★★ 5
Excellent book, possibly currently unique in coverage of latest ideas
This book is possibly currently unique in its coverage of the latest ideas in the field of deep learning -- and it is a very convenient and good survey of fundamental concepts (linear algebra, optimization, performance metrics, activation function types), different network types (multi-layer perceptron, convolutional neural networks, and recurrent neural networks), practical considerations (data set, training and validation, implementation), and applications (comments on existing real-world/commercial uses). The final 235 pages of the content portion of the book is dedicated to topics in "Deep Learning Research", and these topics are truly at the current frontier.
Another reviewer said that one could gain the same knowledge of cutting-edge research by reading all of the latest papers (from academia and industry), but the "research" section of this book offers the following: Selection of the most notable research by the very experienced authors of the book, and collection of similar research in to a broader discussion of themes, and the additional insights. The book covers very advanced and new ideas currently being explored, and it is very nice to be able to have a consistent and coherent presentation of all of those ideas.
However, the book is also packed with valuable observations and pointers about more basic aspects of deep learning implementations and practices -- and such commentary is in depth and includes substantial analysis and mathematical derivation (in an intuitive presentation that often includes graphs illustrating the phenomenon).
As someone with an intermediate level of knowledge and experience of neural networks, I am really grateful for this book, because seems like the ideal resource for learning cutting-edge ideas and practices, with context. The book has excellent scope and depth, and I am confident that anyone with a solid background in linear algebra, calculus, statistics, and general machine learning, and basic neural networks (multi-layer perceptrons) will find this book to be very exciting and perhaps unique in its ability to take the reader to the next level and a new frontier. I was personally excited to learn about the idea of representing the dependencies of intermediate quantities by directed graphs, and how this can be used to perform calculations for recurrent neural networks efficiently. And I think the long chapter on recurrent neural networks is very helpful.
Having said all of this, I think only people with significant working knowledge and experience with neural networks and mathematics -- people whose academic or professional focus has been neural networks for at least a year or two -- would benefit from this book. This book answers a lot of the deeper questions that one is likely to have while developing a solid understanding of the fundamentals, and that's one of the book's tremendous values, but this book assumes an understanding of the fundamentals (but does briskly cover the basics).
I think this book is a perfect follow-up book for the excellent book "Neural Network Design (2nd edition)" by Hagan, Demuth, Beale, and de Jesus, and I highly recommend the latter for gaining the solid background needed to have a thrilling experience with the "Deep Learning" book.
In summary, I am very glad this "Deep Learning" book was written, and I think the "Deep Learning" book will be a great benefit to a lot of people, and to the evolution of the field.
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Reviewed in the United States on April 18, 2017
★★★★★ 5
The definitive guide to becoming a researcher in the field
Format: Hardcover
This is not a coding book. I see a lot of negative reviews around the expectation that this book would teach the reader how to quickly build machine learning systems and write code. This book is not for that audience.
If you just want to build applications, don't worry about how deep learning works. It's akin to needing to understand how an engine works just to drive a car. If you are looking for a coding resource, try: https://www.amazon.com/Hands-Machine-Learning-Scikit-Learn-TensorFlow/dp/1492032646/ref=sr_1_4?keywords=machine+learning+tensorflow&qid=1579608765&sr=8-4 . And even with that book, the material still goes far beyond what you need - use it as a light reference.
I bought this book as an aspiring machine learning researcher, and towards that end, it is the best resource available in print (still true as of 2020). For instance:
The first 5 chapters are timeless. These are things that were mostly established 20 or 30 years ago and beyond and are mostly STEM fundamentals at this point. There are whole textbooks dedicated to each of those chapters, but the authors provide a quick refresher and overview of probably 80% of what you'll encounter in deep learning. If you haven't previously learned each of these subtopics, you'll probably want to study them individually since they are the key to innovating (linear algebra, probability & stats, numerical computation, machine learning fundamentals).
Chapters 6 thru 9 are the foundation of deep learning. We're about 12 years into seeing rapid change in the deep learning space, yet all of these principles and techniques still hold (many recent innovations are still relying on Convolutional models in 2020, which is the most layered/complex topics in those chapters). Therefore, I'd wager that these chapters are also fairly stable knowledge that is worth internalizing if you want to be deeply involved in the future of machine learning.
Chapters after 9 are mostly experimental topics, and many of them are already the wrong strategies for optimal results. But there are interesting ideas in here that you'll often encounter in the wild, so it's good exposure to various topics. But probably not worth much of your time.
And lastly, there is good history in here from people who know the space intimately. It's a good way to piece together the developments and learn the lexicon of deep learning so you can have intelligent conversation with experts.
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Reviewed in the United States on January 21, 2020
★★★★★ 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
★★★★★ 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
★★★★★ 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