Charging Curve Simulator (CC → CV)

Understanding how a battery charges is essential for optimizing performance, efficiency, and lifespan. The EV Battery Charge Simulator PRO allows engineers, e-bike builders, and enthusiasts to simulate CC/CV charging behavior, analyze energy flow, and instantly evaluate system performance.

This advanced interactive tool lets you input real-world parameters such as voltage, capacity, current, SOC, and chemistry, and visualize how your battery behaves over time. You’ll get detailed insights into charge duration, energy output (Wh), CC/CV phases, and efficiency, all displayed in dynamic charts.

With built-in Smart Fix, Auto Heal, and Evolution Mode, the system doesn’t just analyze — it actively improves your setup. It iterates, learns, and pushes your configuration toward optimal results, helping you achieve balanced charging, reduced CV time, and maximum energy utilization without guesswork.

Whether you're designing a custom e-bike battery, testing charging strategies, or optimizing performance for real-world use, this tool gives you a powerful, data-driven way to reach excellent results faster.

Expert Mode Explanation

The EV Battery Charge Simulator is built on a dynamic CC/CV mathematical model that replicates real lithium-ion charging behavior using time-based current decay, voltage saturation, and energy accumulation functions.

During the CC phase, the system applies a constant current until the battery reaches its voltage threshold. This phase is modeled as a linear energy injection process, where capacity utilization increases proportionally over time.

In the CV phase, the simulator switches to a voltage clamp model. Current is reduced exponentially based on a decay coefficient (tau), simulating real-world electrochemical resistance and internal balancing effects.

The system continuously evaluates three core metrics: charge efficiency, energy throughput (Wh), and phase balance ratio (CC vs CV time). These metrics determine the final performance rating.

Auto Heal applies constraint-based parameter correction, ensuring values remain physically realistic while improving weak points such as excessive CV duration or underutilized CC current.

Evolution Mode extends this logic using iterative optimization. Each cycle slightly mutates key variables (current, tau, capacity scaling), evaluates performance, and retains the best configuration using a greedy selection model.

The result is a self-improving simulation engine that converges toward optimal charging profiles without requiring manual tuning.

Scientific Mode (Math Model View)

This simulator is based on a simplified electro-physical model of lithium-ion charging behavior, combining constant current input, voltage saturation, and exponential decay during the CV phase.

CC Phase Model:
Energy accumulation is approximated as:
E(t) = I × Vavg × t
where current remains constant until voltage threshold is reached.

CV Phase Model:
Current decay follows an exponential function:
I(t) = I₀ × e-t / τ
where τ (tau) controls how fast current drops toward cutoff.

Total Energy Output:
Wh ≈ ∫ V(t) × I(t) dt approximated numerically during simulation steps.

Efficiency Score:
Efficiency is calculated by balancing:
• CC utilization ratio
• CV time penalty
• energy waste factor

This model is intentionally simplified to allow real-time simulation while maintaining realistic behavior trends.

System Flow Overview

How to Use

1. Enter your battery parameters such as voltage, capacity, SOC, current (CC), and cutoff current.
2. Select the battery chemistry (e.g., NMC or LFP) to match your real setup.
3. Click Run Simulation to visualize the charging curve and analyze results.
4. Review key metrics like charge time, energy (Wh), CC/CV phases, and efficiency.
5. Click Fix / Auto Heal to automatically optimize your configuration.
6. Use Evolution Mode to iterate multiple times and push your setup toward optimal performance.

Tips / Guide

• Avoid excessively long CV phases — they reduce efficiency and increase charge time.
• Balance CC current to prevent too fast or too slow charging cycles.
• Higher capacity increases runtime but may require tuning current and decay rate.
• Use Auto Heal first, then refine further with Evolution Mode for best results.
• Watch the efficiency and total time together — the goal is a balanced, optimized charge profile.

Why It Matters

Charging behavior directly impacts battery lifespan, efficiency, and real-world performance. Poorly tuned charging parameters can lead to wasted energy, longer charging times, and reduced battery health. By simulating and optimizing your charging profile, you can achieve a faster, more efficient, and balanced system while minimizing stress on the battery.

Who Should Use This Tool

• E-bike builders and DIY enthusiasts designing custom battery systems.
• Engineers and developers testing charging strategies and performance.
• Electric vehicle hobbyists optimizing battery efficiency and runtime.
• Technicians analyzing CC/CV behavior and charging curves.
• Anyone looking to improve battery charging performance using data-driven simulation.