Rana ashutosh (17 risultati)

hardcover. Condizione: New.

Condizione: New. pp. 316.

Condizione: New. pp. 316.

Condizione: New. pp. 316.

Soft cover. Condizione: New. ISBN:9789390885183 N.A.

Condizione: New. pp. 179.

Condizione: New.

Taschenbuch. Condizione: Neu. Neuware.

PAP. Condizione: New. New Book. Shipped from UK. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condizione: New.

PAP. Condizione: New. New Book. Delivered from our UK warehouse in 4 to 14 business days. THIS BOOK IS PRINTED ON DEMAND. Established seller since 2000.

Condizione: New. Print on Demand.

Paperback. Condizione: new. Paperback. Turn battery data into insight, with code that runs.Every electric vehicle has to answer two questions that it cannot measure directly: how much energy is left, and how long will the battery last? This practical guide shows engineers, students and data scientists how to answer them with physics and machine learning, from the first line of Python to a working battery health monitor on a Raspberry Pi.You need no laboratory and no downloads to start. The book includes evbatt, a compact battery simulator that generates realistic drive cycles, voltages, temperatures and ageing histories for whole fleets of cells, with the hidden ground truth included, so you can learn every method before you touch real data. It also points you to the major public battery datasets for when you are ready.Inside you will learn to: Build coulomb counting, an equivalent-circuit model and an extended Kalman filter, and see exactly where they failEstimate state of charge across -10 to 45 0C with gradient boosting and LSTM neural networks in PyTorchEstimate state of health from partial charging curves, with calibrated uncertainty from Gaussian process regressionPredict remaining useful life from the first 100 cycles, with honest conformal prediction intervalsDetect internal short circuits, loose connections and thermal-runaway precursors in battery modulesOptimise health-aware EV charging around solar power and time-of-day tariffsDeploy models on the edge with NumPy, 8-bit quantisation and C code for the ESP32Build a self-calibrating Raspberry Pi battery health monitor as a capstone projectEvery code listing in the book was run before publication, and every figure was produced by the code beside it. Written for engineering students, researchers, BMS and EV engineers, data scientists moving into energy and mobility, and educators looking for meaningful hands-on AI projects. This item is printed on demand. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.…

Condizione: New. Print on Demand.

Paperback. Condizione: New. NEW. SHIPS FROM MULTIPLE LOCATIONS. book.

Paperback. Condizione: new. Paperback. Turn battery data into insight, with code that runs.Every electric vehicle has to answer two questions that it cannot measure directly: how much energy is left, and how long will the battery last? This practical guide shows engineers, students and data scientists how to answer them with physics and machine learning, from the first line of Python to a working battery health monitor on a Raspberry Pi.You need no laboratory and no downloads to start. The book includes evbatt, a compact battery simulator that generates realistic drive cycles, voltages, temperatures and ageing histories for whole fleets of cells, with the hidden ground truth included, so you can learn every method before you touch real data. It also points you to the major public battery datasets for when you are ready.Inside you will learn to: Build coulomb counting, an equivalent-circuit model and an extended Kalman filter, and see exactly where they failEstimate state of charge across -10 to 45 0C with gradient boosting and LSTM neural networks in PyTorchEstimate state of health from partial charging curves, with calibrated uncertainty from Gaussian process regressionPredict remaining useful life from the first 100 cycles, with honest conformal prediction intervalsDetect internal short circuits, loose connections and thermal-runaway precursors in battery modulesOptimise health-aware EV charging around solar power and time-of-day tariffsDeploy models on the edge with NumPy, 8-bit quantisation and C code for the ESP32Build a self-calibrating Raspberry Pi battery health monitor as a capstone projectEvery code listing in the book was run before publication, and every figure was produced by the code beside it. Written for engineering students, researchers, BMS and EV engineers, data scientists moving into energy and mobility, and educators looking for meaningful hands-on AI projects. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.…

Condizione: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. Autor/Autorin: Parkash JaiJai Parkash Godara is currently working as Asst. Prof. in Dept of Electronics & Comm. Engg. at Amity University,Noida,INDIA. He received his M.Sc.(VLSI) in 2003 & M.Tech (Instrumentation) in 2006. His area of interest are.…