IELTS Reading: Electric vehicles and smart grid integration – Đề mẫu kèm đáp án chi tiết

Mở bài

Electric Vehicles And Smart Grid Integration không chỉ là xu hướng công nghệ mà còn là chủ đề “hot” trong các bài đọc học thuật. Trong IELTS Reading, các passage về năng lượng, môi trường và công nghệ thường xuyên xuất hiện vì chúng kết hợp kiến thức nền và kỹ năng đọc hiểu học thuật. Bài viết này cung cấp cho bạn một đề luyện đọc trọn vẹn gồm 3 passages (từ dễ đến khó) xoay quanh chủ đề Electric vehicles and smart grid integration, bám sát phong cách Cambridge: đa dạng dạng câu hỏi, paraphrase mạnh, và yêu cầu suy luận hợp lý. Bạn sẽ học được:

• Một đề IELTS Reading đầy đủ 3 passages
• Câu hỏi giống thi thật, có đáp án và giải thích
• Từ vựng học thuật và kỹ thuật làm bài hiệu quả
• Phù hợp cho học viên từ band 5.0 trở lên muốn luyện tốc độ, paraphrase và suy luận

1. Hướng dẫn làm bài IELTS Reading

Tổng Quan Về IELTS Reading Test

• Thời gian: 60 phút cho 3 passages
• Tổng số câu hỏi: 40 câu
• Phân bổ thời gian khuyến nghị:
  • Passage 1: 15-17 phút
  • Passage 2: 18-20 phút
  • Passage 3: 23-25 phút

Chiến Lược Làm Bài Hiệu Quả

• Đọc câu hỏi trước, sau đó đọc passage (skimming & scanning)
• Chú ý từ khóa và paraphrase
• Quản lý thời gian chặt chẽ
• Không bỏ trống câu nào

[internal_link: Skimming và Scanning trong IELTS Reading]

Các Dạng Câu Hỏi Trong Đề Này

• Multiple Choice
• True/False/Not Given
• Sentence Completion
• Matching Headings
• Yes/No/Not Given
• Summary/Note Completion
• Matching Features
• Short-answer Questions

Mô tả chủ đề Electric vehicles and smart grid integration và IELTS Reading

2. IELTS Reading Practice Test

PASSAGE 1 – Plugging Cars into Smarter Power (H3)

Độ khó: Easy (Band 5.0-6.5)

Thời gian đề xuất: 15-17 phút

Electric vehicles (EVs) are becoming more common on roads around the world. Yet their greatest promise is unlocked when they connect with a smart grid. A smart grid uses digital tools to balance electricity use and supply more efficiently. When EVs and smart grids work together, drivers enjoy lower costs, and the grid becomes cleaner and more reliable. Not only do EVs consume electricity, but they can also act like flexible devices that help the grid.

A smart grid is different from a traditional grid because it uses sensors and smart meters to observe electricity flows in near real time. These data allow grid operators to detect problems quickly and respond to changes in demand. With better information, the grid can reduce peak demand, integrate more renewable energy, and avoid expensive upgrades. In simple words, a smart grid is a network that communicates, learns, and adapts.

EV charging can be simple, like plugging in at home overnight, or fast, using public stations that deliver high power. Home charging is often cheaper, especially with time-of-use tariffs that signal lower prices during off-peak periods. Public DC fast charging is convenient for long trips but is usually more expensive than off-peak home rates. If drivers plan their charging, they can save money and help the grid at the same time.

Smart charging means using software to decide when and how fast to charge. A smart charger or a phone app can delay charging until prices drop or until solar power is strong at midday. For example, the car might start charging at 1 a.m. when the grid is quiet, or at noon when rooftop solar is producing more than the home needs. Because the charging is automated, drivers keep the convenience while the grid gains flexibility.

Another idea is vehicle-to-grid, or V2G, which allows a parked EV to export power back to the grid when it is needed most. In this way, EVs act like small batteries that can support the system during peak hours or emergencies. Pilot programs at universities and bus depots have shown that coordinated V2G can reduce demand peaks and earn revenue. Although not every EV will participate, even a modest number can make a difference.

Workplace charging can also help. If employees plug in at the office, charging shifts from evening to daytime when solar generation is abundant in some regions. This reduces pressure on the grid during the evening peak. It is a common worry that millions of EVs will all start charging at 6 p.m., but real-world data suggest charging times are staggered, especially when smart charging is available. Therefore, careful planning and simple incentives are enough to avoid major problems.

Of course, challenges remain. Charging networks must be reliable, and different systems should work well together. Drivers need clear prices and easy tools. In many places, not all customers have time-of-use tariffs, and local rules differ. Still, progress is steady as utilities and charging providers improve their services. If stakeholders cooperate, EVs and smart grids can grow together and bring broad benefits to society.

Bold vocabulary in this passage: smart grid, sensors and smart meters, peak, renewable, time-of-use tariffs, off-peak periods, smart charger, export power, V2G, and bold grammar: Not only do EVs…, If drivers plan their charging, Because the charging is automated, Although not every EV will participate, Therefore, careful planning…, If stakeholders cooperate.

Questions 1-13

Questions 1-4
Choose the correct letter, A, B, C or D.

1. What is the main advantage when EVs connect with a smart grid?
   A Lower car prices
   B Longer battery warranties
   C More efficient and cleaner electricity use
   D Free public charging

2. Time-of-use tariffs are designed to encourage drivers to
   A charge only at public stations
   B charge during off-peak periods
   C avoid charging at home
   D replace their home meters

3. Which device can automate home charging decisions?
   A A solar panel
   B A smart charger
   C A fast-charging cable
   D A petrol generator

4. In the passage, V2G is described as a way for EVs to
   A export power to the grid
   B receive free software updates
   C double their driving range
   D charge faster on highways

Questions 5-9
Do the following statements agree with the information in Passage 1?
Write True, False, or Not Given.

5. Smart grids rely on real-time data to manage electricity flows.
6. Public DC fast charging is always cheaper than off-peak home charging.
7. The passage says battery degradation makes V2G impossible.
8. Workplace charging can move demand from evening to daytime.
9. All countries already provide time-of-use tariffs for every customer.

Questions 10-13
Complete the sentences below.
Choose NO MORE THAN TWO WORDS from the passage for each answer.

10. A smart grid uses sensors and __ to monitor and control flows.
11. Time-of-use tariffs signal low prices during __.
12. At home, a __ can decide when to start or stop charging.
13. With V2G, a parked EV can __ to support the system.

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PASSAGE 2 – Markets, Wires, and Motivations (H3)

Độ khó: Medium (Band 6.0-7.5)

Thời gian đề xuất: 18-20 phút

A. In the early days of EV adoption, most charging was unmanaged: cars were plugged in as soon as drivers arrived home. This created a visible bump in evening demand, sometimes called the “shoulder” of the load curve. As adoption grows, unmanaged charging could sharpen peaks and complicate the integration of variable renewables. Managed charging attempts to flatten those peaks by shifting load to midday when solar output is abundant, or to late night when demand is low. The result is a more flexible grid that can absorb more clean energy with fewer costly upgrades.

B. Beyond timing, EVs can provide ancillary services such as frequency regulation and spinning reserve. Aggregators bundle thousands of vehicles into a resource that the market can see and pay. While the revenue per vehicle is often modest, stacking multiple services—demand response, regulation, and capacity—can be meaningful at scale. However, participation depends on communications reliability, verification rules, and customer consent. Transaction costs and fragmented markets may limit early profits, but pilots have already enrolled EV fleets in real frequency response programs.

C. Distribution networks face practical constraints. Low-voltage feeders and pole-top transformers were not designed for simultaneous high-power charging on every driveway. Yet the answer is not always expensive hardware. Coordinated scheduling, informed by local limits, can maintain safe transformer temperatures while meeting driver needs. Algorithms can allocate power fairly, rotating higher rates when there is headroom and slowing down when circuits approach their limits. In many cases, software achieves what wires alone cannot: situational awareness and dynamic control.

D. The consumer perspective is pivotal. Many drivers are willing to help the grid if the experience is frictionless: simple enrollment, clear incentives, and transparent protections. Default opt-in programs with easy opt-out can raise participation without coercion. Equity matters too—renters and residents in multi-unit buildings need access to reliable charging and fair tariffs. Without attention to equity, EVs risk reinforcing existing inequalities, even as they promise cleaner air and lower operating costs.

E. Finally, standards and interoperability determine whether the system scales. Protocols like OCPP for chargers and ISO 15118 for vehicle-grid communication enable plug-and-charge, roaming, and secure data exchange. Standards reduce vendor lock-in and lower integration costs. Yet they do not, by themselves, settle questions about data privacy or consent; clear governance and cybersecurity measures are still required. With the right standards and rules, markets, wires, and motivations can align.

Bold vocabulary: variable renewables, more flexible, ancillary services, modest, verification, pole-top transformers, Coordinated scheduling, headroom, situational awareness, frictionless, OCPP, ISO 15118, plug-and-charge, data privacy. Bold grammar: none needed here, but complex structures are used throughout.

Questions 14-26

Questions 14-18
Matching Headings
Choose the correct heading for paragraphs A–E from the list of headings below.
Write the correct number, i–ix, in boxes 14–18.

List of Headings
i. Why standards alone are not enough
ii. Consumers at the heart of flexibility
iii. The cost of replacing all transformers
iv. From unmanaged to managed charging
v. How EVs earn from ancillary services
vi. Limits of renewable generation
vii. Aligning tariffs with rooftop solar
viii. Algorithms instead of wires
ix. Competition between aggregators

14. Paragraph A
15. Paragraph B
16. Paragraph C
17. Paragraph D
18. Paragraph E

Questions 19-22
Do the following statements agree with the writer’s claims?
Write Yes, No, or Not Given.

19. The writer believes unmanaged charging will inevitably overwhelm every grid.
20. Some EV fleets have already joined frequency response markets.
21. Coordinated scheduling always requires expensive hardware upgrades.
22. ISO 15118 automatically solves all data privacy issues.

Questions 23-26
Summary Completion
Choose NO MORE THAN TWO WORDS from the passage for each answer.

Aggregators can combine many EVs to offer services such as 23 __ and demand response. Although the income per car is often 24 __, stacking services can be profitable at scale. On the grid side, avoiding overloads may depend on 25 __ that respect local limits rather than new equipment. Still, program success hinges on a 26 __ experience for drivers so they enroll and stay engaged.

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PASSAGE 3 – Algorithms, Markets, and Trust at the Edge of the Grid (H3)

Độ khó: Hard (Band 7.0-9.0)

Thời gian đề xuất: 23-25 phút

As EV penetration rises, the problem of coordinating millions of mobile batteries becomes a question of designing market signals and control policies that are at once efficient, fair, and secure. Advanced approaches treat charging as a stochastic optimization problem, where uncertain driving patterns, volatile prices, and intermittent renewables must be balanced across time and space. Under such uncertainty, model predictive control (MPC) adapts to new information, while stochastic dynamic programming explores policies that hedge risks rather than chase every price fluctuation.

Distribution systems add another layer of complexity: congestion constraints and voltage limits vary feeder by feeder, often by the minute. A promising idea is to use distribution locational marginal prices (DLMPs) that reflect local conditions, internalizing costs that wholesale prices cannot see. When DLMPs are broadcast, smart chargers and aggregators can respond by shifting load away from constrained nodes and toward circuits with spare capacity. This “transactive” behavior turns passive infrastructure into an active marketplace.

Yet incentives can conflict. Fleet operators may prefer to maximize arbitrage profits, while regulators prioritize reliability and equity. Such tensions are well captured by bi-level optimization, which explicitly models leader–follower dynamics between system operators and market participants. A complementary view comes from game-theoretic coordination, in which strategic actors—drivers, aggregators, and utilities—seek equilibrium under competing objectives. Agent-based simulation then allows researchers to observe emergent patterns that are otherwise hidden in equation-centric models.

Cybersecurity is not optional. False data injection attacks can distort price signals or fake meter readings, causing overloaded equipment or unfair settlements. Therefore, robust estimation and anomaly detection must be embedded alongside control algorithms. Similarly, privacy-preserving methods—such as federated learning or homomorphic encryption—aim to keep sensitive driver data local while still enabling system-wide optimization. However, security cannot be bolted on at the end; it must shape system architecture from the start.

Resilience adds still another requirement. During wildfire shutoffs or major storms, microgrids may island and rely on vehicle-to-home (V2H) or vehicle-to-building power to keep critical loads online. Here, the objective is not price but survival: refrigeration, communications, and medical devices. Policies must clarify liability and safety for bidirectional power flows, while standards ensure that inverters disconnect or reconnect safely as conditions change.

Finally, long-term sustainability depends on more than kilowatt-hours. Charging policies that ignore battery health can accelerate degradation, eroding both consumer value and grid capacity. Life-cycle assessment suggests that integrating EVs with a smart grid reduces emissions most when charging aligns with clean generation and when fairness constraints prevent burdening vulnerable customers or neighborhoods. Only by combining economics, engineering, and ethics can EV–grid integration scale responsibly.

Bold vocabulary: stochastic optimization, model predictive control (MPC), stochastic dynamic programming, distribution locational marginal prices (DLMPs), bi-level optimization, game-theoretic coordination, agent-based simulation, False data injection, vehicle-to-home (V2H), degradation, fairness constraints. Bold grammar: Therefore, robust estimation…, However, security cannot be bolted on…, Only by combining economics, engineering, and ethics can….

Questions 27-40

Questions 27-31
Choose the correct letter, A, B, C or D.

27. DLMPs are intended to
    A set national fuel standards
    B reflect local grid conditions in prices
    C replace wholesale markets entirely
    D reward only rooftop solar owners

28. Which method adapts plans as new information arrives?
    A Game-theoretic coordination
    B Bi-level optimization
    C Agent-based simulation
    D Model predictive control

29. The passage suggests that cybersecurity should
    A be added after markets are designed
    B be handled by drivers individually
    C be integrated into system design from the start
    D be avoided to reduce costs

30. During wildfire shutoffs, V2H is used mainly to
    A earn extra income from arbitrage
    B power critical household loads
    C charge neighboring vehicles
    D reduce wholesale market prices

31. According to the passage, large-scale integration must consider
    A only engineering constraints
    B ethical fairness in outcomes
    C only consumer profits
    D battery range extensions

Questions 32-36
Matching Features
Match each method (A–E) with the feature that best describes it.
Write the correct letter, A–E, in boxes 32–36.

Methods
A. Stochastic dynamic programming
B. Model predictive control
C. Bi-level optimization
D. Game-theoretic coordination
E. Agent-based simulation

Features
i. Captures leader–follower relationships between regulators and market players
ii. Uses probability to hedge against uncertain inputs
iii. Observes emergent patterns from many interacting actors
iv. Re-solves a short-horizon problem as conditions change
v. Models strategic behavior seeking equilibrium

32. Handles uncertainty with distributions
33. Replans repeatedly over a moving horizon
34. Captures regulator–participant interactions
35. Studies emergent behavior
36. Suits strategic users finding balance

Questions 37-40
Short-answer Questions
Answer the questions below.
Choose NO MORE THAN THREE WORDS AND/OR A NUMBER for each answer.

37. What pricing signal is proposed to reflect local distribution constraints?
38. What household backup function can keep critical loads online?
39. What cyberattack manipulates measurements or price signals?
40. What kind of outages highlight the need for microgrid islanding in the passage?

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3. Answer Keys – Đáp Án

PASSAGE 1: Questions 1-13

1. C
2. B
3. B
4. A
5. True
6. False
7. Not Given
8. True
9. False
10. smart meters
11. off-peak periods
12. smart charger
13. export power

PASSAGE 2: Questions 14-26

14. iv
15. v
16. viii
17. ii
18. i
19. No
20. Yes
21. No
22. No
23. ancillary services
24. modest
25. coordinated scheduling
26. frictionless

PASSAGE 3: Questions 27-40

27. B
28. D
29. C
30. B
31. B
32. A
33. B
34. C
35. E
36. D
37. DLMPs
38. vehicle-to-home
39. false data injection
40. wildfire shutoffs

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4. Giải Thích Đáp Án Chi Tiết

Passage 1 – Giải Thích

• Câu 2: B
  • Dạng câu hỏi: Multiple Choice
  • Từ khóa: time-of-use tariffs, encourage
  • Vị trí: Đoạn 3
  • Giải thích: Bài nhấn mạnh TOU “signal lower prices during off-peak periods” → khuyến khích sạc giờ thấp điểm.
• Câu 6: False
  • Dạng: T/F/NG
  • Từ khóa: Public DC fast charging, cheaper
  • Vị trí: Đoạn 3
  • Giải thích: Bài nói public DC “is usually more expensive than off-peak home rates” → mâu thuẫn với “always cheaper”.
• Câu 13: export power
  • Dạng: Sentence Completion
  • Vị trí: Đoạn 5
  • Giải thích: V2G cho phép EV “export power back to the grid”.

Passage 2 – Giải Thích

• Câu 15: v
  • Dạng: Matching Headings
  • Vị trí: Đoạn B
  • Giải thích: Nội dung về aggregators và ancillary services; từ khóa “frequency regulation”, “revenue”.
• Câu 21: No
  • Dạng: Yes/No/Not Given
  • Vị trí: Đoạn C
  • Giải thích: “The answer is not always expensive hardware… Coordinated scheduling…” → không “always requires”.
• Câu 26: frictionless
  • Dạng: Summary Completion
  • Vị trí: Đoạn D
  • Giải thích: Thành công phụ thuộc trải nghiệm “frictionless”.

Passage 3 – Giải Thích

• Câu 27: B
  • Dạng: MCQ
  • Vị trí: Đoạn 2
  • Giải thích: DLMPs “reflect local conditions.”
• Câu 29: C
  • Dạng: MCQ
  • Vị trí: Đoạn 4
  • Giải thích: “security cannot be bolted on at the end; it must shape system architecture from the start.”
• Câu 37: DLMPs
  • Dạng: Short-answer
  • Vị trí: Đoạn 2
  • Giải thích: Cụm từ viết tắt được nêu rõ.

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5. Từ Vựng Quan Trọng Theo Passage

Passage 1 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa tiếng Việt	Ví dụ từ bài	Collocation
smart grid	n	/smɑːrt ɡrɪd/	lưới điện thông minh	connect with a smart grid	smart grid integration
smart meters	n	/smɑːrt ˈmiːtərz/	công tơ thông minh	sensors and smart meters	install smart meters
peak	n	/piːk/	đỉnh, giờ cao điểm	reduce peak demand	peak demand/prices
renewable	adj/n	/rɪˈnjuːəbl/	tái tạo	integrate more renewable energy	renewable integration
time-of-use tariffs	n	/ˌtaɪm əv ˈjuːs ˈtærɪfs/	biểu giá theo thời gian	with time-of-use tariffs	TOU tariffs
off-peak periods	n	/ˌɒf ˈpiːk ˈpɪəriədz/	giờ thấp điểm	prices during off-peak periods	off-peak rates
smart charger	n	/smɑːrt ˈtʃɑːrdʒər/	bộ sạc thông minh	A smart charger can delay charging	smart charger app
export power	v phr	/ˈekspɔːrt ˈpaʊər/	xuất điện	EVs can export power	export power back
pilot program	n	/ˈpaɪlət ˈproʊɡræm/	chương trình thí điểm	Pilot programs have shown…	V2G pilot
reliability	n	/rɪˌlaɪəˈbɪləti/	độ tin cậy	networks must be reliable	grid reliability

Passage 2 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa	Ví dụ	Collocation
variable renewables	n	/ˈvɛəriəbl rɪˈnjuːəblz/	năng lượng tái tạo biến thiên	integration of variable renewables	variable renewable integration
ancillary services	n	/ænˈsɪləri ˈsɜːrvɪsɪz/	dịch vụ phụ trợ	provide ancillary services	frequency regulation service
modest	adj	/ˈmɒdɪst/	khiêm tốn	revenue per vehicle is modest	modest returns
verification	n	/ˌvɛrɪfɪˈkeɪʃn/	xác minh	verification rules	data verification
pole-top transformers	n	/poʊl tɑːp trænsˈfɔːrmərz/	máy biến áp treo cột	strain on pole-top transformers	transformer limits
coordinated scheduling	n	/koʊˈɔːrdɪneɪtɪd ˈskɛdʒuːlɪŋ/	lập lịch phối hợp	coordinated scheduling can maintain	coordinated control
headroom	n	/ˈhɛdruːm/	dư địa	when there is headroom	capacity headroom
situational awareness	n	/ˌsɪtʃuːˈeɪʃənl əˈwɛənəs/	nhận thức tình huống	provides situational awareness	real-time awareness
frictionless	adj	/ˈfrɪkʃn ləs/	không ma sát, dễ dàng	frictionless experience	frictionless enrollment
OCPP	n	/oʊ siː siː piː piː/	giao thức OCPP	protocols like OCPP	OCPP compliance
ISO 15118	n	—	tiêu chuẩn ISO 15118	ISO 15118 for communication	ISO 15118 plug-and-charge
data privacy	n	/ˈdeɪtə ˈpraɪvəsi/	quyền riêng tư dữ liệu	data privacy concerns	privacy governance

Passage 3 – Essential Vocabulary

Từ vựng	Loại từ	Phiên âm	Nghĩa	Ví dụ	Collocation
stochastic optimization	n	/stoʊˈkæstɪk ˌɒptɪmaɪˈzeɪʃn/	tối ưu ngẫu nhiên	treat charging as stochastic optimization	stochastic model
model predictive control (MPC)	n	—	điều khiển dự báo mô hình	MPC adapts to new information	MPC scheme
stochastic dynamic programming	n	—	quy hoạch động ngẫu nhiên	explores policies that hedge risks	SDP policy
distribution locational marginal prices (DLMPs)	n	—	giá cận biên theo vị trí phân phối	DLMPs reflect local conditions	DLMP signals
bi-level optimization	n	—	tối ưu hai tầng	models leader–follower dynamics	bi-level model
game-theoretic coordination	n	—	phối hợp theo lý thuyết trò chơi	strategic actors seek equilibrium	game-theoretic equilibrium
agent-based simulation	n	—	mô phỏng dựa trên tác tử	observe emergent patterns	agent-based model
false data injection	n	—	tấn công chèn dữ liệu giả	false data injection attacks	detection of FDI
vehicle-to-home (V2H)	n	—	xe phát điện về nhà	rely on V2H to power loads	V2H backup
degradation	n	/ˌdɛɡrəˈdeɪʃn/	suy giảm (pin)	accelerate degradation	battery degradation
fairness constraints	n	—	ràng buộc công bằng	impose fairness constraints	fairness in markets
transactive	adj	/trænˈzæktɪv/	có tính giao dịch	transactive behavior	transactive energy
island	v	/ˈaɪlənd/	tách lưới	microgrids may island	islanding operation
homomorphic encryption	n	—	mã hóa đồng cấu	enable privacy-preserving learning	encryption scheme
anomaly detection	n	—	phát hiện bất thường	robust estimation and anomaly detection	anomaly detection system

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6. Kỹ Thuật Làm Bài Theo Từng Dạng Câu Hỏi

Multiple Choice

• Cách làm:
  • Đọc kỹ câu hỏi, gạch chân từ khóa
  • Loại trừ đáp án sai rõ ràng
  • Tìm paraphrase trong passage
  • Chọn đáp án khớp nhất về nghĩa
• Lỗi thường gặp:
  • Chọn đáp án chứa từ giống hệt passage nhưng sai nghĩa
  • Không đọc hết tất cả options
• Ví dụ: Q27 “reflect local conditions” paraphrase của “intended to reflect local grid conditions” → B.

True/False/Not Given

• Cách phân biệt:
  • True: nội dung khớp (có thể paraphrase)
  • False: mâu thuẫn
  • Not Given: passage không đủ thông tin
• Lỗi thường gặp:
  • Nhầm lẫn False và Not Given
  • Suy luận theo kiến thức ngoài bài
• Ví dụ: Q7 “battery degradation makes V2G impossible” không được đề cập → Not Given.

Yes/No/Not Given

• Tương tự T/F/NG nhưng “Yes/No” phản ánh quan điểm/tuyên bố của tác giả, không phải sự thật phổ quát.
• Ví dụ: Q21 “always requires expensive hardware upgrades” tác giả phủ định → No.

Matching Headings

• Cách làm:
  • Đọc headings trước
  • Xác định main idea mỗi đoạn (đọc câu đầu/cuối)
  • Gạch bỏ heading đã dùng
• Tips:
  • Bỏ qua ví dụ chi tiết, tập trung ý chính
• Ví dụ: Đoạn C nói về giải pháp “software/algorithms” thay vì phần cứng → viii.

Summary/Note Completion

• Cách làm:
  • Xác định loại từ cần điền (n/adj/v)
  • Tìm cụm paraphrase quanh chỗ trống
  • Tuân thủ giới hạn từ
• Ví dụ: Q25 “coordinated scheduling” nằm ngay đoạn C.

Matching Features

• Cách làm:
  • Đọc kỹ định nghĩa/đặc điểm
  • So khớp với thuật ngữ trong passage
  • Cẩn thận các thuật ngữ gần nghĩa
• Ví dụ: “emergent patterns” → agent-based simulation.

Short-answer Questions

• Cách làm:
  • Tìm từ khóa chính xác trong passage
  • Giới hạn từ (THREE WORDS)
• Ví dụ: Q37 “pricing signal” → DLMPs.

Minh họa kỹ thuật làm bài IELTS Reading về EV và smart grid

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7. Chiến Lược Đạt Band Cao

Band 6.0-6.5: Nền Tảng

• Nắm ý chính passage
• Làm đúng 23-26/40 câu
• Tập trung Passage 1-2
• Luyện skimming, scanning cơ bản

Band 7.0-7.5: Trung Cấp Cao

• Nhận diện paraphrase phức tạp
• Làm đúng 30-32/40 câu
• Giữ nhịp độ ổn định với Passage 3
• Thành thạo tất cả dạng câu hỏi

Band 8.0-9.0: Nâng Cao

• Đọc hiểu sâu, nắm ý ngầm
• Làm đúng 35-40/40 câu
• Quản lý thời gian xuất sắc
• Kiểm tra bẫy từ đồng dạng, tránh careless errors

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Kết bài

Tóm Tắt

Chủ đề Electric vehicles and smart grid integration xuất hiện ngày càng nhiều trong IELTS Reading vì tính thời sự và học thuật. Bộ đề 3 passages ở trên mô phỏng độ khó tăng dần, bao phủ nhiều dạng câu hỏi giống thi thật để bạn luyện tập hiệu quả. Đáp án và giải thích đi kèm giúp bạn tự đánh giá, hiểu rõ paraphrase và vị trí thông tin. Bảng từ vựng thiết yếu cùng kỹ thuật làm bài theo dạng câu hỏi sẽ giúp bạn cải thiện tốc độ, độ chính xác, và tự tin trước kỳ thi. Hãy lưu lại bộ đề này, luyện theo thời gian khuyến nghị, và tiếp tục mở rộng kiến thức với các chủ đề liên quan như năng lượng tái tạo, chính sách môi trường, và đổi mới công nghệ trong giao thông.