Optimizing EV Battery Charging: How AI-Based Smart Chargers Can Manage the CV Phase for Better Battery Health
Modern electric vehicles (EVs) use lithium-ion batteries that follow a standardized charging method known as Constant Current–Constant Voltage (CC-CV). After an initial phase of fast, high-current charging (the CC phase), the system transitions into the Constant Voltage (CV) phase, where the voltage is held steady, and current tapers down as the battery nears full charge.
While the CV phase is essential for ensuring safe and complete charging, research has shown that prolonged exposure to the CV phase negatively affects battery State of Health (SoH). This opens the door for innovation: can smart, AI-powered chargers optimize this phase to extend battery life and improve performance? Recent research indicates that the conventional CV charging approach traditionally considered optimal could significantly shorten battery lifespan, leading to premature replacement costs that could have been avoided.
This blog explores the science behind the CV phase, its impact on battery degradation, and how intelligent control systems like Deep Q-Learning (DQL) can make EV charging SAFER, SMARTER and more SUSTAINABLE.
What Happens After 80%? A Closer Look at the CV Phase
What Activates the CV Phase & Scientific Rationale for the CV Phase
The Constant Voltage (CV) phase is triggered when a lithium-ion battery reaches its maximum safe voltage, typically around 4.2V per cell. At this point, the internal resistance increases, and the electrochemical insertion of lithium ions into the anode slows due to diffusion limitations. Continuing with constant current charging beyond this voltage risks overcharging, lithium plating, and thermal runaway. To mitigate these risks, the Battery Management System (BMS) switches to the CV phase, holding the voltage constant while allowing the charging current to taper off naturally. During this phase, lithium ions continue to move into the anode structure in a process called intercalation—where lithium ions are gradually inserted into the layers of graphite without breaking its structure. This allows the battery to safely absorb the final 15–20% of its capacity without exceeding safe electrochemical limits. However, prolonged time in the CV phase can lead to thermal stress, electrolyte decomposition, and gradual degradation of internal battery structures. Thus, the CV phase ensures safe, complete charging while balancing performance and long-term reliability.
Is the CV Phase Quietly Draining Your Battery’s Lifespan?
While essential for achieving full charge, prolonged exposure to the Constant Voltage (CV) phase in lithium-ion batteries contributes to degradation, especially with frequent charging to 100% State of Charge (SoC). The effects arise from electrochemical and thermal stresses, influenced by charging habits and cell chemistry.
1.Degradation Mechanisms
Electrolyte Breakdown: Accelerates oxidation, increasing internal resistance and reducing capacity.
SEI Layer Growth: Thickens the solid electrolyte interphase, consuming active lithium and lowering usable capacity.
Mechanical Stress: Induces lattice collapse and micro-cracks in nickel-rich cathodes and stresses graphite anodes.
2. High SoC and Voltage Effects
Prolonged operation near 100% SoC stresses electrodes and doubles degradation rates compared to the 20–80% SoC range.
3.Thermal Degradation
High C-rate CV charging generates heat as current tapers, accelerating cathode oxidation and electrolyte decomposition.
4. Quantitative Impacts
CC-CV charging causes ~4–5% capacity loss over 100 cycles.
Holding 4.2V for 2.5 hours can halve lifespan, compared to that stopping at 4.0V.
High C-rate CV charging can raise internal resistance by 15% over 500 cycles.
Regular full charging reduces cycle life by 20–30% compared to 80–90% SoC limits.
5.Practical Trade-Offs
Limiting SoC or shortening CV phase preserves State of Health (SoH) but reduces usable range.
Full charging maximizes range but accelerates aging.
Manufacturers target 80% SoH retention after ~1,000 cycles by optimizing charge strategies.
How AI-Based Smart Charging Can Optimize the CV Phase
With battery aging and efficiency as top priorities, AI-driven chargers offer intelligent control over the CV phase to enhance longevity and performance.
1.Adaptive Charge Termination
AI monitors battery health in real time and adjusts SoC cutoffs (e.g., 85–90%) to reduce CV exposure.
AI agents learn optimal termination points from historical data to extend battery lifespan.
2.Intelligent Current Tapering
Tapering rates are dynamically modulated based on temperature, battery age, and usage.
Customized tapering curves balance efficiency with reduced degradation.
3.Usage-Based Charging Optimization
AI tailors charging sessions using trip and usage data, often avoiding CV charging when unnecessary (e.g., daily commutes needing only 80–85% SoC).
4. Temperature-Aware CV Control
AI pauses or delays CV charging during high temperatures using thermal models and sensors.
Some systems initiate cooling or shift charging to cooler periods.
5. Dynamic Voltage Adjustment
AI lowers max voltage (e.g., to 4.1V) based on SoH and usage trends to reduce oxidative stress and SEI growth.
6.Pulsed Charging Techniques
Constant Power-Constant Voltage (CP-CV) methods introduce pulse intervals to reduce heat buildup, cutting charge time by >20% and improving longevity.
7.SoH-Aware CV Exit Timing
AI detects early stress indicators in aging cells and shortens CV duration accordingly.
8. Reinforcement Learning for Degradation Management
AI agents optimize long-term SoH by balancing voltage, temperature, and cycle counts using reward-based learning.
9.Fleet Load Balancing in CV Phase
Smart systems prioritize fast-charging (CC phase) vehicles over those in CV, easing depot demand and grid load.
10.Predictive CV Scheduling
AI forecasts CV phase timing and aligns it with off-peak hours or renewable energy availability to reduce energy costs and enhance grid integration.
Deep Q-Learning: A Closer Look at Algorithmic Control During the CV Phase
1. State
Represents battery parameters such as SoC, internal temperature, voltage, battery health indicators (e.g., capacity fade, internal resistance), and external factors like ambient temperature or trip demand.
2.Action
The DQL agent chooses from actions like reducing the voltage (e.g., from 4.2V to 4.1V), stopping the charge early (e.g., at 90% SoC), adjusting current tapering speed, or shifting CV charging to cooler time windows.
3.Reward and Penalty
Designed to balance battery longevity and energy throughput. The DQL agent receives rewards for actions that lead to efficient charging (e.g., faster charging with minimal stress) and for maintaining battery-friendly conditions (e.g., lower temperatures, optimal SoC range). Simultaneously, it receives penalties for actions that result in adverse outcomes such as high internal temperatures, prolonged CV durations, excessive voltage stress, State-of-Health (SoH) degradation etc. This dual-feedback mechanism helps the agent learn to avoid harmful charging strategies while promoting long-term performance and reliability.
4.Policy Learning
Over multiple training cycles using simulated or historical battery usage data, the DQL agent learns to select optimal control actions that minimize long-term degradation (e.g., slower SEI growth, lower heat buildup) while still delivering usable charge.
5. Practical Implementation
In operational scenarios such as EV fleet charging stations, the DQL model can be integrated with the charging controller or battery management system (BMS) to provide real-time decision support. By continuously evaluating battery health metrics and environmental conditions, the agent can dynamically recommend early termination of the CV phase or voltage reduction when further charging yields diminishing returns compared to the associated degradation risk. This enables adaptive charging strategies that go beyond static thresholds, optimizing long-term battery performance while maintaining operational readiness.
AI-Driven Smart Charging for Healthier Batteries
The CV phase in Li-ion battery charging is essential but can contribute to battery degradation if not managed properly. This is where AI transforms the equation entirely by enabling dynamic charge control that minimizes battery stress and extends lifespan.
These intelligent charging systems dynamically adjust charging parameters in real-time, reducing degradation mechanisms while preserving capacity cycles that translate directly to miles and years added to your EV investment.
Interested in implementing AI-driven smart charging for your EV fleet? Contact us today to explore how our intelligent charging solutions can optimize performance and extend your EV fleet's battery longevity.
References
Gandoman, Foad H., et al. "Understanding voltage behavior of lithium-ion batteries in electric vehicles applications." Batteries 8.10 (2022): 130.
Ruan, Haokai, et al. "State of health estimation of lithium-ion battery based on constant-voltage charging reconstruction."IEEE Journal of Emerging and Selected Topics in Power Electronics 11.4 (2021): 4393-4402.
Chen, Jinyu, et al. "State-of-Health Estimation of Lithium-Ion Battery Based on Constant Voltage Charging Duration." Batteries 9.12 (2023): 565.
Zhang, Ming, et al. "A review of SOH prediction of Li-ion batteries based on data-driven algorithms." Energies 16.7 (2023): 3167.
Shan, Chunlai, et al. "Review of Various Machine Learning Approaches for Predicting Parameters of Lithium-Ion Batteries in Electric Vehicles." Batteries 10.6 (2024): 181.
Zhang, Ruifeng, et al. "A study on the open circuit voltage and state of charge characterization of high capacity lithium-ion battery under different temperature." Energies 11.9 (2018): 2408.
Qian, Liqin, et al. "Revealing the Impact of High Current Overcharge/Overdischarge on the Thermal Safety of Degraded Li‐Ion Batteries." International Journal of Energy Research 2023.1 (2023): 8571535.
