Chapter 1: Fundamentals of Battery Materials and the Role of Materials Informatics

This chapter covers the fundamentals of Fundamentals of Battery Materials and the Role of Materials Informatics, which battery fundamentals. You will learn essential concepts and techniques.

Learning Objectives: - Understand the operating principles and key performance metrics of batteries - Grasp the challenges in battery material development - Recognize the value MI brings to battery development - Learn about battery industry trends and market size

Reading Time: 25-30 minutes

1.1 Battery Fundamentals

1.1.1 Battery Operating Principles

What is a battery: A device that converts chemical energy to electrical energy (or vice versa)

Basic configuration:

┌─────────────────────────────────┐
│        Anode (Negative)          │ ← Oxidation reaction (during discharge)
│    e.g., Graphite (LiC₆)         │
├─────────────────────────────────┤
│        Electrolyte               │ ← Li⁺ ion conduction
│    e.g., LiPF₆/EC+DMC            │
├─────────────────────────────────┤
│        Cathode (Positive)        │ ← Reduction reaction (during discharge)
│    e.g., LiCoO₂ (LCO)            │
└─────────────────────────────────┘

Reactions during discharge: - Anode: LiC₆ → C₆ + Li⁺ + e⁻ (Li⁺ release) - Cathode: Li⁺ + e⁻ + CoO₂ → LiCoO₂ (Li⁺ insertion) - Overall: LiC₆ + CoO₂ → C₆ + LiCoO₂ (E° ≈ 3.7 V)

During charging: Reverse reaction proceeds

1.1.2 Key Performance Metrics

Energy Density: - Definition: Energy per unit mass (Wh/kg) - Calculation: Energy density = Capacity (mAh/g) × Voltage (V) × 0.001 - Current status: LIB 200-300 Wh/kg, Target 500 Wh/kg (all-solid-state batteries)

Power Density: - Definition: Output per unit mass (W/kg) - Requirements by application: EV (>300 W/kg), Stationary (>50 W/kg)

Cycle Life: - Definition: Number of cycles until capacity drops to 80% of initial value - Current status: LIB 500-1,500 cycles, Target 3,000-5,000 cycles

Coulombic Efficiency: - Definition: CE = (Discharge capacity) / (Charge capacity) × 100% - Ideal value: 100% (actual 98-99.5%)

C-rate: - Definition: Indicator of charge/discharge speed - 1C: Full charge/discharge in 1 hour - 5C: Full charge in 12 minutes (fast charging)

1.1.3 Types of Batteries

Lithium-ion Battery (LIB): - Cathode: LCO, NMC, NCA, LFP - Anode: Graphite, Si alloy - Electrolyte: Organic liquid electrolyte (LiPF₆) - Energy density: 200-300 Wh/kg - Applications: Smartphones, EVs, Laptops

All-Solid-State Battery (ASSB): - Electrolyte: Solid (sulfide-based, oxide-based, polymer-based) - Advantages: High safety, high energy density (>500 Wh/kg) - Challenges: Interface resistance, manufacturing cost - Commercialization target: 2027-2030

Li-S Battery (Lithium-Sulfur): - Cathode: Sulfur (S₈) - Theoretical capacity: 1,672 mAh/g (5 times LCO) - Challenges: Polysulfide dissolution, cycle performance - Energy density target: 500-600 Wh/kg

Li-air Battery (Lithium-Air): - Cathode: Oxygen from atmosphere - Theoretical energy density: 11,680 Wh/kg (practical not yet achieved) - Challenges: Electrolyte decomposition, cycle performance - Research stage

Na-ion Battery (Sodium-Ion): - Uses Na instead of Li - Advantages: Low cost (Na abundant), similar chemistry - Energy density: 150-200 Wh/kg (70% of LIB) - Applications: Stationary storage, low-cost EVs

1.2 Current Status and Challenges in Battery Material Development

1.2.1 Traditional Development Process

Step 1: Literature Survey (1-3 months) - List candidate materials from past research - Issue: Enormous number of papers (over 10,000 publications/year)

Step 2: Material Synthesis (3-6 months) - Solid-state method, hydrothermal method, sol-gel method, etc. - Issue: Time-consuming optimization of synthesis conditions

Step 3: Evaluation Testing (6-12 months) - Charge-discharge tests, cycle tests, safety tests - Issue: Testing is time-consuming and costly

Step 4: Optimization (12-24 months) - Optimize composition, morphology, synthesis conditions - Issue: Vast parameter space

Total Development Period: 3-5 years (over 10 years to commercialization)

1.2.2 Major Challenges

Challenge 1: Energy Density Enhancement - Current: LIB 250 Wh/kg (cell level) - Target: 500 Wh/kg (all-solid-state, Li-S batteries) - Required technology: High-capacity cathode (>250 mAh/g), Li metal anode

Challenge 2: Fast Charging - Current: 30-60 minutes to 80% charge - Target: 10 minutes to 80% charge (800 km/h charging rate) - Barriers: Li plating, heat generation, ion diffusion limitation

Challenge 3: Cycle Life Extension - Current: 500-1,500 cycles (80% capacity retention) - Target: 3,000-5,000 cycles (equivalent to 500,000 km for EVs) - Degradation mechanisms: SEI growth, structural collapse, Li plating

Challenge 4: Safety Enhancement - Risks: Thermal runaway, fire - Causes: Internal short circuit, overcharging, mechanical damage - Countermeasures: Solid electrolyte, flame-retardant electrolyte, protection circuits

Challenge 5: Cost Reduction - Current: $150/kWh (2024) - Target: $50/kWh (EVs equivalent to gasoline vehicles) - High-cost factors: Co ($40,000/ton), Li ($15,000/ton)

Challenge 6: Supply Chain - Co dependence: Congo produces 60% (geopolitical risk) - Li dependence: Australia and Chile produce 80% - Solutions: Na-ion batteries, recycling

1.3 Battery Development Challenges Solved by MI

1.3.1 Capacity and Voltage Prediction

Traditional method: - DFT calculation: Several days to weeks per material - Experimental synthesis: Several weeks to months per material

MI approach: - Machine learning model: Prediction in seconds - Methods: Random Forest, XGBoost, Graph Neural Network - Data: 140,000+ materials from Materials Project

Example achievements: - Capacity prediction accuracy: MAE < 10 mAh/g (practical level) - Voltage prediction accuracy: MAE < 0.1 V - Screening speed: 10,000 materials/day

1.3.2 Cycle Degradation Prediction

Traditional method: - Actual cycle testing: 3-6 months for 1,000 cycles - Accelerated testing: Issues with data reliability

MI approach: - Time series models: LSTM, GRU - Predict 2,000 cycles from initial 100 cycles - Remaining Useful Life (RUL) prediction

Example achievements: - RUL prediction error: < 10% - Early anomaly detection: Detect accelerated degradation within 50 cycles - Cost reduction: 80% reduction in testing period

1.3.3 New Material Discovery

Traditional method: - Trial and error: 10-50 materials per year - Success rate: < 1% (to commercialization)

MI approach: - Bayesian optimization: Efficient exploration - Active Learning: Experiment → Prediction → Next experiment loop - High-throughput DFT: Hundreds of materials in parallel

Example achievements: - Exploration efficiency: Over 70% reduction in experiments - New material discovery: Na₃V₂(PO₄)₂F₃ (Na-ion cathode) - Development period: 5 years → 2 years (60% reduction)

1.3.4 Charging Protocol Optimization

Traditional method: - Fixed charging curve: CC-CV (Constant Current - Constant Voltage) - Issue: Fast degradation rate, room for optimization

MI approach: - Reinforcement Learning - Reward function: Charging speed + Cycle life - Degradation - Multi-objective Optimization

Example achievements: - 80% charge in 10 minutes, degradation rate < 1%/1000 cycles - EV charging time: 30 minutes → 10 minutes (Stanford University, 2020) - Patent filings: Tesla, Toyota, Panasonic

1.4 Battery Industry Impact

1.4.1 Market Size

2024: - Battery market: $50 billion (5 trillion yen) - Lithium-ion batteries: 85% - Next-generation batteries: 15% (R&D stage)

2030 forecast: - Battery market: $120 billion (12 trillion yen) - Growth rate: 15% annual average - Drivers: EV (70%), Stationary storage (20%), IoT (10%)

1.4.2 Major Sectors

Electric Vehicles (EV): - 2024: 14 million units/year (18% of total) - 2030 forecast: 30 million units/year (30% of total) - Required battery capacity: 1,500 GWh/year (2030)

Stationary Energy Storage Systems: - Applications: Renewable energy stabilization, peak shifting, emergency power - 2024: 50 GWh - 2030 forecast: 300 GWh - Importance: Absorbing solar and wind power variability

IoT Devices: - Smartphones, wearables, drones - Requirements: Miniaturization, long life, safety - 2030 forecast: 100 GWh

1.4.3 Major Companies

Battery Manufacturers (2024 global share): 1. CATL (China): 37% 2. LG Energy Solution (South Korea): 14% 3. BYD (China): 12% 4. Panasonic (Japan): 9% 5. Samsung SDI (South Korea): 5%

Automotive Manufacturers: - Tesla: In-house battery development (4680 cell) - Toyota: All-solid-state battery commercialization target (2027) - GM: Ultium battery platform - Volkswagen: PowerCo establishment (battery specialist)

Material Manufacturers: - Sumitomo Metal Mining: Cathode materials (NCM) - Hitachi Chemical: Anode materials, electrolytes - Asahi Kasei: Separators (30% global share)

1.4.4 Contribution to Carbon Neutrality

CO2 reduction effect: - EV adoption (2030): 500 million tons CO2/year reduction - Renewable energy stabilization: Fossil fuel reduction → 1 billion tons CO2/year reduction - Total: 1.5 billion tons CO2/year reduction (3% of total)

Life Cycle Assessment: - Manufacturing stage: CO2 emissions present (reduced by renewable grid) - Usage stage: Zero emissions (for EVs) - Disposal/Recycling: Target 95% recycling rate

Circular Economy: - Battery recycling: 90% Li recovery possible - Reuse: Repurpose EV retired batteries for stationary storage - Upcycling: Value enhancement through new technologies

1.5 Battery AI Strategy of Major Companies

1.5.1 Tesla

Strategy: - Charging optimization AI (numerous patents) - Machine learning for Battery Management System (BMS) - Manufacturing process optimization (Gigafactory)

Achievements: - 10-year warranty (over 160,000 km, 70% capacity retention) - Charging speed: Supercharger V4 (350 kW)

1.5.2 Panasonic

Strategy: - Material screening (DFT + ML) - Cycle degradation prediction model - Manufacturing quality control AI

Achievements: - Joint development with Tesla (2170 cell, 4680 cell) - Energy density: 260 Wh/kg (industry-leading)

1.5.3 Toyota Motor Corporation

Strategy: - All-solid-state battery commercialization (2027 target) - AI exploration of solid electrolyte materials - Manufacturing process development

Achievements: - All-solid-state battery prototype (1,000 km driving range) - Charging time: 10 minutes to 80% (target)

1.5.4 CATL

Strategy: - Na-ion battery commercialization (started 2023) - ML exploration of Li-free materials - Fast charging technology (Qilin Battery)

Achievements: - Energy density: 255 Wh/kg (2024) - Cost: $80/kWh (industry lowest)

1.6 Career Paths in Battery Research

1.6.1 Academia

Positions: - Postdoctoral researcher (3-5 years) - Assistant professor (5-10 years) - Associate professor (10-15 years)

Salary (Japan): - Postdoc: 4-6 million yen/year - Assistant professor: 6-8 million yen/year - Associate professor: 8-12 million yen/year

Major institutions: - The University of Tokyo (Research Center for Advanced Science and Technology) - Tohoku University (Institute of Multidisciplinary Research for Advanced Materials) - Kyoto University (Institute for Chemical Research) - National Institute of Advanced Industrial Science and Technology (AIST)

1.6.2 Industry

Positions: - Battery Scientist - Material Engineer - Process Engineer

Salary (Japan): - 3 years experience: 5-7 million yen/year - 10 years: 8-12 million yen/year - Management: 12-18 million yen/year

Major companies: - Panasonic - Toyota Motor Corporation - Murata Manufacturing - Sumitomo Metal Mining

1.6.3 Startups

Representative examples: - QuantumScape (USA): All-solid-state battery, market cap $5B - SES (USA): Li-Metal battery, $1.4B raised - Northvolt (Sweden): Sustainable batteries, $8B raised - Factorial Energy (USA): Solid electrolyte, $200M raised

Career: - High risk, high return - Technical + business + fundraising skills required - Large return potential through stock options

1.7 Summary

What We Learned

1. Battery fundamentals: - Operating principles (redox reactions, ion conduction) - Key performance metrics (energy density, cycle life, safety) - Types of batteries (LIB, all-solid-state, Li-S, Na-ion)

2. Development challenges: - Energy density enhancement (300 → 500 Wh/kg) - Fast charging (10 minutes to 80%) - Cycle life extension (500 → 3,000 cycles) - Safety enhancement, cost reduction

3. Role of MI: - Capacity/voltage prediction (seconds) - Cycle degradation prediction (80% testing period reduction) - New material discovery (70% experiment reduction) - Charging protocol optimization

4. Industry impact: - Market size: $50B (2024) → $120B (2030) - CO2 reduction through EV adoption: 500 million tons/year - Major companies: CATL, Panasonic, Toyota, Tesla

Next Steps

In Chapter 2, we will learn in detail about MI methods specialized for battery materials: - Four types of battery material descriptors - Building capacity/voltage prediction models - Cycle degradation prediction (LSTM/GRU) - Material exploration through Bayesian optimization

Exercises

Q1: Explain the reactions that occur at the anode and cathode during discharge of a lithium-ion battery.

Q2: Calculate the average voltage of a cathode material with an energy density of 300 Wh/kg and a capacity of 180 mAh/g.

Q3: List three advantages of all-solid-state batteries over conventional lithium-ion batteries.

Q4: Explain why material exploration using MI can reduce the number of experiments by 70% compared to traditional methods.

Q5: Discuss the role of batteries in achieving carbon neutrality from the perspectives of EV adoption and renewable energy stabilization (within 400 characters).

References

1. Goodenough, J. B. & Park, K.-S. "The Li-ion rechargeable battery: a perspective." J. Am. Chem. Soc. (2013).
2. Manthiram, A. "A reflection on lithium-ion battery cathode chemistry." Nat. Commun. (2020).
3. Chen, Y. et al. "A review of lithium-ion battery safety concerns." Proc. IEEE (2021).
4. Sendek, A. D. et al. "Machine Learning-Assisted Discovery of Solid Li-Ion Conducting Materials." Chem. Mater. (2019).
5. Attia, P. M. et al. "Closed-loop optimization of fast-charging protocols for batteries with machine learning." Nature (2020).

Next Chapter: Chapter 2: MI Methods Specialized for Battery Material Design

License: This content is provided under the CC BY 4.0 license.