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EVエネルギー管理向けAI:AIを使用したEVバッテリーの設計・製造・航続距離・ナビゲーション最適化・フリート管理・V2G (Vehicle to Grid) 用途の改善

AI for EV Energy Management: Using AI to Improve EV Battery Design, Manufacturing, Range, Navigation Optimization, Fleet Management, and Vehicle-to-Grid Applications

出版日: | 発行: Guidehouse Insights (formerly Navigant Research) | ページ情報: 英文 46 Pages; 38 Tables, Charts & Figures | 納期: 即納可能 即納可能とは

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本日の銀行送金レート: 1USD=105.15円
EVエネルギー管理向けAI:AIを使用したEVバッテリーの設計・製造・航続距離・ナビゲーション最適化・フリート管理・V2G (Vehicle to Grid) 用途の改善
出版日: 2020年10月26日
発行: Guidehouse Insights (formerly Navigant Research)
ページ情報: 英文 46 Pages; 38 Tables, Charts & Figures
納期: 即納可能 即納可能とは
  • 全表示
  • 概要
  • 図表
  • 目次
概要

EV業界は、EVエクスペリエンスを向上させるためにAI技術を積極的に適用しています。Guidehouse Insights は、コロナウイルスのパンデミックによる販売台数の一時的な後退にもかかわらず、今後10年間ですべての地域でEVの販売が好調になると予想しています。AI技術の使用は、EVの効率と機能を強化し、EV OEMの競争力を高め、将来のEV購入者の懸念を克服する上でますます重要な役割を果たすことが期待されています。AIはEVエコシステムをより魅力的で競争力のあるものにするため、最終的にはEVの採用が加速すると見込まれています。

当レポートでは、EVエネルギー管理向けAIについて調査し、現世代のEVハードウェアおよびサービスで使用されている内部OEM AIアプリケーションおよびEVバリューチェーンにおける保留中または計画中のAI使用(第2世代アプリケーション)、さらにEVエクスペリエンス全体に貢献する既存または計画中のAI機能をさらに強化することが期待される第3世代のアプリケーションに関する説明を提供しており、商用フリート管理、ユーティリティグリッド管理、およびEV充電統合に関するEV関連のAIの機会についてまとめています。

目次

第1章 エグゼクティブサマリー

  • イントロダクション
  • 市場予測

第2章 市場の問題

  • イントロダクション
    • EVパワー技術の概要
    • 市場の動向
    • 市場の障壁
  • EVエコシステムにおけるAIベースのアプリケーション
    • 内部OEMアプリケーション
    • EVエコシステム向けのサードパーティAIソリューション

第3章 主要企業

  • Auto Motive Power
  • Bidgely
  • C4V
  • Ford Motor Company
  • General Motors
  • Lucid Motors
  • Nissan Motor Corporation
  • Robert Bosch
  • Tesla
  • Texas Instruments
  • TomTom

第4章 市場予測

  • イントロダクション
  • 内部EV OEMアプリケーション提供予測:EV出荷別
    • 車載AIアプリケーションの予測
  • サードパーティEV AIソリューションの予測
    • EVフリートAIアプリケーションの予測
    • EV計画のためのユーティリティAIアプリケーションの予測
  • 結論・提言

第5章 頭字語・略語リスト

第6章 図表

第7章 調査範囲・調査ソース・調査手法・注記

図表

LIST OF CHARTS AND FIGURES

  • Third-Party AI-Based Applications for EV Management Revenue, World Markets: 2020-2029
  • Sources of Electricity Generation, US: 2019
  • Historic and Forecast BEV Sales by Region, Base Scenario, World Markets: 2015-2029*
  • BEV Adoption Barriers, US: 2020
  • Demand for Electricity in California with Overlay of Preferred EV Charging Times, US: April 22, 2020
  • AI-Based Applications for Enhanced Range Estimation by Technology Generation, World Markets: 2020-2029
  • AI-Based Applications for EV-Aware Navigation by Technology Generation, World Markets: 2020-2029
  • AI-Based Applications for Charging Stations by Technology Generation, World Markets: 2020-2029
  • AI-Based Applications Revenue for Fleet Management Software, World Markets: 2020-2029
  • AI-Based EV Planning Applications Revenue for Utilities, World Markets: 2020-2029
  • Forecast EV Sales by Region, World Markets: 2020-2029

LIST OF TABLES

  • AI-Based Applications for Enhanced Range Estimation, World Markets: 2020-2029
  • AI-Based Applications for Enhanced Range Estimation, North America: 2020-2029
  • AI-Based Applications for Enhanced Range Estimation, Europe: 2020-2029
  • AI-Based Applications for Enhanced Range Estimation, Asia Pacific: 2020-2029
  • AI-Based Applications for Enhanced Range Estimation, Latin America: 2020-2029
  • AI-Based Applications for Enhanced Range Estimation, Middle East & Africa: 2020-2029
  • AI-Based Applications for EV-Aware Navigation, World Markets: 2020-2029
  • AI-Based Applications for EV-Aware Navigation, North America: 2020-2029
  • AI-Based Applications for EV-Aware Navigation, Europe: 2020-2029
  • AI-Based Applications for EV-Aware Navigation, Asia Pacific: 2020-2029
  • AI-Based Applications for EV-Aware Navigation, Latin America: 2020-2029
  • AI-Based Applications for EV-Aware Navigation, Middle East & Africa: 2020-2029
  • AI-Based Applications for Charging Station Experience Optimization, World Markets: 2020-2029
  • AI-Based Applications for Charging Station Experience Optimization, North America: 2020-2029
  • AI-Based Applications for Charging Station Experience Optimization, Europe: 2020-2029
  • AI-Based Applications for Charging Station Experience Optimization, Asia Pacific: 2020-2029
  • AI-Based Applications for Charging Station Experience Optimization, Latin America: 2020-2029
  • AI-Based Applications for Charging Station Experience Optimization, Middle East & Africa: 2020-2029
  • Forecast EV Sales by Region, World Markets: 2020-2029
  • AI-Based Applications Revenue for EV Fleet Planning Tools, World Markets: 2020-2029
  • AI-Based Applications Revenue for EV Planning Tools for Utilities, World Markets: 2020-2029
  • Third-Party AI-Based Applications for EV Energy Management Revenue, World Markets: 2020-2029
  • AI Applications in EV Battery Design, Manufacturing, and Performance
  • AI Applications for Improved EV Driver Experience
  • AI Applications for Enhancing the EV Support System Experience
  • AI Applications for the EV Support System for Commercial Fleets
  • AI Applications for Utilities Supporting EVs
目次
Product Code: MF-AIBM-20

EVs are viable substitutes for many vehicles that rely on internal combustion engines (ICEs). Although many EV drivers are enthusiastic about their vehicles, prospective customers have legitimate reasons for hesitating to make the switch. The EV industry is aware of these concerns and is aggressively applying AI technology to enhance the EV experience.

Despite a temporary setback in unit sales due to the coronavirus pandemic, Guidehouse Insights expects strong EV sales in all regions over the next decade. The use of AI technology is anticipated to play an increasingly important role in enhancing the efficiency and capabilities of EVs, advancing the competitive positioning of EV OEMs, and overcoming the objections of prospective EV buyers. Ultimately, EV adoption should accelerate as AI makes the EV ecosystem more attractive and competitive.

This Guidehouse Insights report describes the internal OEM AI applications used in the current generation of EV hardware and services as of 2020. It also documents pending or planned uses of AI in the EV value chain (second generation applications). The report also discusses third generation applications that are expected to offer further enhancements to existing or planned AI capabilities contributing to the overall EV experience, but they have yet to be implemented. In addition to these internal OEM use cases, this report provides an overview of EV-related AI opportunities around commercial fleet management, utility grid management, and EV charging integration.

KEY QUESTIONS ADDRESSED:

  • How is AI used in the manufacturing and operation of EVs?
  • How will AI improve the EV driving experience in the coming years?
  • What new features and capabilities will the EV supply chain and EV OEMs use to compete in the future?
  • How will AI address the objections presented by prospective EV customers and encourage them to acquire their first EV?
  • What should utilities/grid operators do to better prepare for EV adoption?

WHO NEEDS THIS REPORT:

  • General-purpose AI software vendors
  • EV battery and component manufacturers (hardware and software)
  • EV OEM manufacturers
  • Charging station service providers
  • Power utilities
  • Investor community

Table of Contents

1. Executive Summary

  • 1.1. Introduction
  • 1.2. Market Forecasts

2. Market Issues

  • 2.1. Introduction
    • 2.1.1. EV Power Technology Overview
    • 2.1.2. Market Trends
    • 2.1.3. Market Barriers
  • 2.2. AI-Based Applications in the EV Ecosystem
    • 2.2.1. Internal OEM Applications
      • 2.2.1.1. Improving Battery Design, Manufacturing, and Performance
        • 2.2.1.1.1. Uses of First Generation AI in Battery Design, Manufacturing, and Performance
        • 2.2.1.1.2. Uses of Second Generation AI for Battery Design, Manufacturing, and Performance
        • 2.2.1.1.3. Uses of Third Generation AI for Battery Design, Manufacturing, and Performance
      • 2.2.1.2. Improving the EV Experience and Reducing Objections to Buying EVs
        • 2.2.1.2.1. Uses of First Generation AI for the In-Vehicle Driver Experience
        • 2.2.1.2.2. Uses of Second Generation AI for the In-Vehicle Driver Experience
        • 2.2.1.2.3. Uses of Third Generation AI for the In-Vehicle Driver Experience
      • 2.2.1.3. Enhancing the EV Support System Experience
        • 2.2.1.3.1. Uses of First Generation AI for the EV Support System Experience
        • 2.2.1.3.2. Uses of Second Generation AI for the EV Support System Experience
        • 2.2.1.3.3. Uses of Third Generation AI for the EV Support System Experience
    • 2.2.2. Third-Party AI Solutions for the EV Ecosystem
      • 2.2.2.1. Commercial EV Fleet Support
        • 2.2.2.1.1. Uses of First Generation AI for EV Fleets
        • 2.2.2.1.2. Uses of Second Generation AI for EV Fleets
        • 2.2.2.1.3. Uses of Third Generation AI for EV Fleets
      • 2.2.2.2. Utility EV Integration Support
        • 2.2.2.2.1. Uses of First Generation AI Applications for Utilities
        • 2.2.2.2.2. Uses of Second Generation AI Applications for Utilities
        • 2.2.2.2.3. Uses of Third Generation AI Applications for Utilities

3. Key Industry Players

  • 3.1. Auto Motive Power
  • 3.2. Bidgely
  • 3.3. C4V
  • 3.4. Ford Motor Company
  • 3.5. General Motors
  • 3.6. Lucid Motors
  • 3.7. Nissan Motor Corporation
  • 3.8. Robert Bosch
  • 3.9. Tesla
  • 3.10. Texas Instruments
  • 3.11. TomTom

4. Market Forecasts

  • 4.1. Introduction
  • 4.2. Internal EV OEM Application Offering Forecasts by EVs Shipped
    • 4.2.1. Forecasts for In-Vehicle AI Applications
  • 4.3. Third-Party EV AI Solution Forecasts
    • 4.3.1. Forecasts for EV Fleet AI Applications
    • 4.3.2. Forecasts for Utility AI Applications for EV Planning
  • 4.4. Conclusions and Recommendations

5. Acronym and Abbreviation List

6. Table of Charts and Figures

7. Scope of Study, Sources and Methodology, Notes

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