NS_Navigator for EV architecture

[hyunjimoon]

first application of #159

strcutured questions to be answered

EV architecture
a. EV architecture: are all BEVs likely to build ono the same architecture
b. Architecting process for EV: would the architecture process be different for EVs?
c. HW/SW architecture: are we likely to see centralized vs. distributed architecture for controllers?

Tech infusion
d. Tech infusion into EVs: what tech could break the BEV architectures?
e. EV diffusion with technology: why have historical projections not matched sales?

GPT prompt

1. what are the questions to ask to answer "XXX (one of a-e above)"
2. what are the hypothesis to test each question?
3. could you order these hypothesis using the measure? The measure is: Criticality X Fidelity / Opportunity Cost, introduced in 15.911, Entrepreneurial Strategy by Scott Stern. Each components are defined as "how much does the ideal version of this test (measured accurately and at low cost) meaningfully reduce uncertainty surrounding the core value creation and capture hypotheses? (Criticality)", "Is there a (reasonable cost) test with a reasonable level of accuracy and precision? (Fidelity)", "What are the resource, time, and strategic costs of an experiment? (How much) is there a tradeoff between faster, better, and cheaper? (Opportunity cost)".

[hyunjimoon]

answer for "a. EV architecture: are all BEVs likely to build ono the same architecture":

To order the hypotheses using the measure ( \frac{Criticality \times Fidelity}{Opportunity \ Cost} ), we need to evaluate each hypothesis based on the three components: Criticality, Fidelity, and Opportunity Cost. While I can provide a hypothetical ranking based on general assumptions, the actual values for each component would require specific data and context related to the BEV industry and the strategic goals of the company in question.

Here's a hypothetical ranking based on general assumptions:

1. Technical Specifications (Performance Requirements)

   • Criticality: High. The performance of a BEV directly impacts its market acceptance and competitive positioning.
   • Fidelity: High. Performance tests and simulations are well-established.
   • Opportunity Cost: Moderate. Testing can be time-consuming but is essential.

2. Regulatory and Safety Standards

   • Criticality: High. Meeting regulations is mandatory for market entry.
   • Fidelity: High. Regulatory requirements are clearly defined.
   • Opportunity Cost: High. Non-compliance can lead to significant penalties.

3. Charging Infrastructure

   • Criticality: High. Charging infrastructure compatibility affects user convenience.
   • Fidelity: High. Charging standards and protocols are well-defined.
   • Opportunity Cost: Moderate. Infrastructure development can be capital-intensive.

4. Cost Considerations (Price Point)

   • Criticality: High. Price affects market positioning and profitability.
   • Fidelity: Moderate. Costing can vary based on economies of scale and supplier negotiations.
   • Opportunity Cost: Moderate. Cost-cutting can impact quality.

5. Design and Aesthetics

   • Criticality: Moderate. Design influences brand perception and market acceptance.
   • Fidelity: Moderate. Design preferences can be subjective.
   • Opportunity Cost: Moderate. Design changes can be resource-intensive.

6. Brand Identity and Legacy

   • Criticality: Moderate. Brand identity affects customer loyalty and perception.
   • Fidelity: Low. Brand perception is subjective and can vary across markets.
   • Opportunity Cost: Moderate. Rebranding or misalignment can be costly.

7. Innovation and Technology

   • Criticality: Moderate. Innovation can provide a competitive edge.
   • Fidelity: Moderate. The impact of new technologies can be uncertain.
   • Opportunity Cost: High. R&D is resource-intensive.

8. Environmental and Sustainability Considerations

   • Criticality: Moderate. Sustainability is increasingly important for consumers and regulators.
   • Fidelity: Moderate. Sustainability metrics can vary.
   • Opportunity Cost: Moderate. Sustainable practices can increase costs.

9. Purpose and Target Market

   • Criticality: Low. While important, the target market is often predefined.
   • Fidelity: High. Market research methods are well-established.
   • Opportunity Cost: Low. Market research is a standard practice.

10. Manufacturing and Scalability

• Criticality: Low. Scalability is a longer-term consideration.
• Fidelity: Moderate. Predicting future demand can be challenging.
• Opportunity Cost: High. Manufacturing changes are capital-intensive.

This ranking is based on general assumptions and might vary based on the specific context and strategic priorities of a company. For a more accurate ranking, a detailed analysis considering the specific context of the BEV industry and the company's goals would be required.

[jhoon-cho]

To answer the question "Architecting process for EV: would the architecture process be different for EVs?" we first need to identify the sub-questions and hypotheses that need to be tested. Here's a breakdown:

1. Questions to Ask:

1. Technical Design Differences: How does the design of an EV (Electric Vehicle) differ from a traditional gasoline vehicle?
2. Integration of New Components: How do EV-specific components (like batteries, electric motors, and charging systems) affect the architecture process?
3. Infrastructure and Standards: Are there new infrastructural or standardization requirements for EVs compared to gasoline vehicles?
4. Safety Considerations: How do safety concerns and requirements differ for EVs?
5. Cost Implications: How does the cost structure change when designing an EV versus a traditional vehicle?
6. Consumer Preferences: How do consumer preferences and perceptions about EVs influence the design process?

2. Hypotheses to Test for Each Question:

1. Technical Design Differences:

   • H1: The structural design requirements for an EV are significantly different due to the absence of traditional components (e.g., exhaust systems, fuel tanks).

2. Integration of New Components:

   • H2: The placement and integration of batteries significantly change the architecture process.
   • H3: The integration of electric motors requires a rethinking of vehicle dynamics and layout.

3. Infrastructure and Standards:

   • H4: EVs require adherence to new infrastructure standards, especially regarding charging infrastructure.
   • H5: The need for universal charging connections and systems dictates design elements.

4. Safety Considerations:

   • H6: Battery safety concerns necessitate unique architectural considerations in EVs.
   • H7: EVs might require enhanced protective measures due to high voltages.

5. Cost Implications:

   • H8: Cost structures for EVs lean heavily towards battery and electronic components rather than mechanical ones.

6. Consumer Preferences:

   • H9: Consumers prioritize longer driving range, leading to larger battery packs and design implications.
   • H10: Consumers value fast charging, influencing charging system architecture.

3. Ordering Hypotheses using the Measure:

The ordering would be subjective and might vary based on specific context or geography, but a general ordering might look something like:

1. Criticality: How significant is the uncertainty reduction?
2. Fidelity: Can the hypothesis be tested with reasonable accuracy and precision?
3. Opportunity Cost: What's the trade-off in terms of resources, time, and strategic costs?

Using this, we can estimate an ordering:

Formula: Criticality X Fidelity / Opportunity Cost

For simplicity, I'll assign a relative score (1-10, with 10 being the highest) to each component of the hypotheses:

(Note: These are hypothetical scores for illustrative purposes)

Hypothesis	Criticality	Fidelity	Opportunity Cost	Score (Cx F/OC)
H1	9	8	3	24
H2	10	9	4	22.5
H3	8	7	2	28
H4	7	6	3	14
H5	6	6	2	18
H6	9	8	4	18
H7	8	7	3	18.67
H8	7	7	3	16.33
H9	8	8	4	16
H10	7	7	3	16.33

Ordering the hypotheses based on scores:

1. H3 (28)
2. H1 (24)
3. H2 (22.5)
4. H5 (18)
5. H6 (18)
6. H7 (18.67)
7. H8 (16.33)
8. H10 (16.33)
9. H9 (16)
10. H4 (14)

This framework allows for a systematic approach to evaluating and prioritizing hypotheses, although the specific scores and final ordering would need to be tailored to the specific context of the organization or region in question.

[jhoon-cho]

When considering "Tech infusion into EVs: what tech could break the BEV (Battery Electric Vehicle) architectures?" we're trying to understand the technological innovations or disruptions that could radically change or challenge current BEV designs.

1. Questions to Ask:

1. Battery Technology: What advancements in battery technology could alter the current BEV architecture?
2. Charging Innovations: Could new charging technologies or mechanisms change how BEVs are designed?
3. Autonomous Driving: How could the integration of full autonomous driving tech challenge current EV design?
4. Material Innovations: Are there new materials that could revolutionize the structural or functional aspects of BEVs?
5. Vehicle-to-Grid Systems: How might the integration of vehicle-to-grid technologies impact BEV designs?
6. Electronic Architectures: Could advancements in vehicle electronic systems or software redefine BEV architectures?

2. Hypotheses to Test for Each Question:

1. Battery Technology:

   • H1: Solid-state batteries could significantly reduce weight and increase energy density, altering BEV designs.

2. Charging Innovations:

   • H2: Wireless charging technologies could eliminate the need for charging ports and influence vehicle base design.
   • H3: Ultra-fast charging could necessitate enhanced thermal management systems in BEVs.

3. Autonomous Driving:

   • H4: Full autonomy might eliminate the need for traditional cockpit designs (e.g., steering wheel, pedals).
   • H5: The integration of advanced sensors for autonomy could change vehicle aesthetics and design.

4. Material Innovations:

   • H6: The use of advanced composites or new materials could drastically change BEV weight, strength, and cost.

5. Vehicle-to-Grid Systems:

   • H7: Vehicles designed for bi-directional energy flow might require unique battery and electrical system designs.

6. Electronic Architectures:

   • H8: Increased software reliance could shift BEVs from hardware-centric to software-centric designs.
   • H9: New vehicle communication standards (like V2X) could impose new design requirements.

3. Ordering Hypotheses using the Measure:

Again, let's estimate an ordering using hypothetical scores (1-10) for each component of the hypotheses:

Hypothesis	Criticality	Fidelity	Opportunity Cost	Score (Cx F/OC)
H1	10	9	4	22.5
H2	8	7	3	18.67
H3	9	8	3	24
H4	9	7	4	15.75
H5	8	8	3	21.33
H6	7	6	2	21
H7	7	7	3	16.33
H8	8	7	3	18.67
H9	6	6	2	18

Ordering the hypotheses based on scores:

1. H3 (24)
2. H1 (22.5)
3. H5 (21.33)
4. H6 (21)
5. H2 (18.67)
6. H8 (18.67)
7. H9 (18)
8. H7 (16.33)
9. H4 (15.75)

This framework allows for a systematic approach to evaluating and prioritizing technological disruptions that might challenge BEV designs. Specific scores and final ordering would be tailored to specific contexts and the ongoing state of technology and industry advancements.

[jhoon-cho]

Addressing the mismatch between historical EV (Electric Vehicle) sales projections and actual sales requires understanding the dynamics of consumer preferences, technological advancements, governmental policies, and market conditions. Here's how we can break it down:

1. Questions to Ask:

1. Consumer Awareness and Perceptions: Were consumers adequately educated and informed about the benefits and limitations of EVs?
2. Infrastructure Development: Was the necessary charging infrastructure in place to support the widespread adoption of EVs?
3. Technological Advancements: Did battery technology and EV range meet consumer expectations and needs?
4. Price Point and Incentives: Were EVs priced competitively compared to internal combustion engine (ICE) vehicles, and were there adequate incentives to purchase EVs?
5. Government Policies and Regulations: Were government policies conducive to EV adoption?
6. Vehicle Offerings: Were there sufficient model choices and vehicle types available to cater to a wide range of consumers?

2. Hypotheses to Test for Each Question:

1. Consumer Awareness and Perceptions:

   • H1: Lack of consumer education and misconceptions about EVs hindered their adoption rate.

2. Infrastructure Development:

   • H2: Inadequate charging infrastructure made consumers hesitant to buy EVs.

3. Technological Advancements:

   • H3: Battery technology didn't advance as quickly as projected, leading to range anxiety among potential consumers.

4. Price Point and Incentives:

   • H4: The higher upfront cost of EVs compared to ICE vehicles, even with potential long-term savings, deterred consumers.
   • H5: Lack of significant government or manufacturer incentives slowed EV adoption.

5. Government Policies and Regulations:

   • H6: Governments didn't implement aggressive enough policies to promote EV adoption or curb ICE vehicle usage.

6. Vehicle Offerings:

   • H7: Limited variety in EV models and types restricted wider market appeal.

3. Ordering Hypotheses using the Measure:

Assigning hypothetical scores (1-10) for each component of the hypotheses:

Hypothesis	Criticality	Fidelity	Opportunity Cost	Score (Cx F/OC)
H1	7	7	3	16.33
H2	9	8	4	18
H3	10	8	5	16
H4	8	7	3	18.67
H5	8	6	3	16
H6	9	7	4	15.75
H7	7	6	2	21

Ordering the hypotheses based on scores:

1. H7 (21)
2. H4 (18.67)
3. H2 (18)
4. H1 (16.33)
5. H3 (16)
6. H5 (16)
7. H6 (15.75)

This provides a prioritized view of hypotheses for investigating discrepancies between historical EV sales projections and actual sales. Specific scores and rankings would be adapted based on the context and unique conditions of specific regions or periods being analyzed.

[hyunjimoon]

[tomfid]

My first question is, why do we want to know? Are we going to place bets on EV architectures, or try to move the market in a beneficial direction through policy, or ... ?

[hyunjimoon]

Context is I wish to test the hypo-exp tool and I'm testing #159 to Hyundai, Vensim, Changi airport, and many entrepreneurs