Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management

個数：

電子版価格
¥32,224

電子版あり

Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management

Jili, Tao/ Zhang, Ridong/ Ma, Longhua

ウェブストア価格 ¥38,192（本体¥34,720）
Elsevier - Health Sciences Division（2024/05発売）
外貨定価 US$ 175.00
ポイント 347pt

提携先の海外書籍取次会社に在庫がございます。通常3週間で発送いたします。
【重要ご説明事項】
1. 納期遅延や、ご入手不能となる場合が若干ございます。
2. 複数冊ご注文の場合は、ご注文数量が揃ってからまとめて発送いたします。
3. 美品のご指定は承りかねます。

●3Dセキュア導入とクレジットカードによるお支払いについて

【入荷遅延について】
世界情勢の影響により、海外からお取り寄せとなる洋書・洋古書の入荷が、表示している標準的な納期よりも遅延する場合がございます。
おそれいりますが、あらかじめご了承くださいますようお願い申し上げます。

◆画像の表紙や帯等は実物とは異なる場合があります。

◆ウェブストアでの洋書販売価格は、弊社店舗等での販売価格とは異なります。
また、洋書販売価格は、ご注文確定時点での日本円価格となります。
ご注文確定後に、同じ洋書の販売価格が変動しても、それは反映されません。

製本 Paperback:紙装版/ペーパーバック版／ページ数 346 p.
言語 ENG
商品コード 9780443131899
DDC分類 629.2293028563

Full Description

Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management presents the state of the art in hybrid electric vehicle system modeling and management. With a focus on learning-based energy management strategies, this book provides detailed methods, mathematical models, and strategies designed to optimize the energy management of the energy supply module of a hybrid vehicle.

This book first addresses the underlying problems in Hybrid Electric Vehicle (HEV) modeling, and then introduces several artificial intelligence-based energy management strategies of HEV systems, including those based on fuzzy control with driving pattern recognition, multiobjective optimization, fuzzy Q-learning and Deep Deterministic Policy Gradient (DDPG) algorithms. To help readers apply these management strategies, this book also introduces State of Charge and State of Health prediction methods and real-time driving pattern recognition. For each application, the detailed experimental process, program code, experimental results, and algorithm performance evaluation are provided.

Application of Artificial Intelligence in Hybrid Electric Vehicle Energy Management is a valuable reference for anyone involved in the modeling and management of hybrid electric vehicles, and will be of interest to graduate students, researchers, and professionals working on HEVs in the fields of energy, electrical, and automotive engineering.

Preface
Acknowledgments
1. Introduction
2. System modeling of lithiumeion battery, PEMFC, and supercapacitor in HEV
3. Neural network modeling for SOH of lithium-ion battery and performance degradation prediction of fuel cell
4.Optimal fuzzy energy management for fuel cell/supercapacitor systems using neural network-based driving pattern recognition
5. Optimal fuzzy energy management system optimization based on NSGA-III-SD for lithium battery/supercapacitor HEV
6. Q learning-based hybrid energy management strategy
7. Improved DDPG hybrid energy management strategy based on LSH
8. Further idea on meta EMS for HEV
Index