Lithium-ion cells are an essential part of many devices in modern times. They can be found everywhere, such as mobile phones, laptops, cameras, electrical vehicles (EV), and more recently, in electric energy storage units for renewable energy. Batteries are the key enabler for the ongoing electrification of our transportation sector, allowing a new range of clean electric cars, buses and trucks. EVs offer longer range as the energy and power density of battery packs increase along with a cost reduction, making larger batteries more affordable. Despite the recent improvements in battery cells, these storage systems degrade over time through multiple mechanisms. The capacity and power of batteries fade depending on the way we use them, the number of cycles, time, temperature, etc. This is called “Battery Aging”.
In addition to being inconvenient for the user, the economic and environmental impacts of battery aging can be staggering. A lifetime increase of only 10% represents billions of dollars in savings and millions of tons of CO2,eq less in our atmosphere, considering the USD 94.4 billion global market for Li-ion batteries expected by 2025 [1] and an impact of 55 kg CO2,eq / kWh [2]. Needless to emphasise that battery aging should be a concern.
Failure and degradation
Physically, failures in batteries can be categorized as [3]:
- Mechanically induced, e.g., gas buildup or vibration leading to packaging failure;
- Chemically induced, e.g., chemical reactions that proceed with time, with rate dependence on temperature and chemical state;
- Electrochemically induced, e.g., side reactions are driven or accelerated by electrochemical (dis)charge processes;
- Electro-chemo-mechanically induced, e.g., material failure associated with volumetric changes and mechanical stresses caused by electrochemical (dis) charge process reactions;
- Thermal coupling of all of the above
Across all mechanisms, thermal behavior plays an important role, with chemical, electrochemical, and mechanical degradation rates tightly coupled to temperature.
Even when most of the above failure mechanisms are mastered, electrochemical aging remains inevitable. In a well-designed Li-ion cell, the solid electrolyte interface (SEI) film growth on the negative electrode is the dominant degradation mechanism. It consumes the cyclable Li in the system. Capacity fade (an irreversible loss of the ability of the battery to store charge) and resistance increase are the two main effects of the degradation in Li-ion cells. Capacity fade reduces the range and charge rate of the battery. Besides, resistance increase causes an increase in the internal heat generation, energy consumption, and reduction in power output, charge rate, and charging efficiency of the battery.
What impacts the aging of batteries?
The primary aging factors are:
- State of Charge (SOC)
- Depth of Discharge during the cycle (DOD)
- Current, I, mostly defined by its ratio over the current that would be required to empty the cell one hour, or the C-rate.
- Temperature, T, which is depends on the internal heat generation (current and resistance) and the thermal management of the battery pack.
Aging will not only occur due to its usage, but also simply as a function of time, so the contributions to aging can be categorized as (Fig. 1):
- Calendar aging
Correspond to the degradation as the battery is stored without being used, when there is no current through the battery. It mainly depends on the temperature and SOC.
- Cycle aging
Degradation of the battery as it is either charged or discharged. It is affected by temperature, DOD, and C-rate.
Why do we study battery aging?
Understanding the aging mechanisms and their modeling to prevent degradation is essential. As a battery designer, we are always looking for a battery with a long life. To achieve this goal, we should know how to estimate the batteries’ state of health (SOH). Besides, it is an essential part of the battery management system (BMS). It gives us the required information on how we can use and manage the battery to get long life. Moreover, we always need a prediction of the battery’s life based on the usage scenario. This cannot be obtained without an understanding of degradation mechanisms.
A typical capacity fade diagram is shown in figure 2. The approaches used in battery aging studies are model-based, data-driven based, and hybrid. Semin-Empirical models are basically model-based approaches with some accelerated tests conducted to calibrate and parametrize the model. These models are customized for a specific cell,so as new cells are developed, aging tests are required to recalibrate the predictive aging model. As shown in figure 2, at the first stage, a model is developed based on the experimental data. This model can be used as a predictor of the battery lifetime. Remaining Useful Life (RUL) and End of Life (EOL) are among the main outputs of the model. Detailed modeling in material and cell level (such as P2D) can be utilized for the aging assessment of batteries.
In the data-driven approaches, the capacity can be estimated with a black-box and gray-box approach. The black-box approach is performed entirely based on data. In the gray-box approach, a combination of modeling and data are used for aging model training [4].
Degradation Mechanisms in Lithium-ion Batteries
The battery capacity is directly determined by the amount of the active materials and the available lithium ions. As an analogy, we can imagine the active material as a reservoir, and the lithium ions as water flow flowing in and out of the reservoir. Following the same analogy, Loss of Active Material (LAM) is like a change of the reservoir components, for example, its chemical decomposition. Loss of Lithium-ion Inventory (LLI) on the other hand, is like losing water flowing to the reservoir, as some of the Li is consumed. Therefore, there will not be sufficient Li to perform reactions and provide energy.
The main degradation mechanisms leading to the Loss of Lithium Inventory (LLI) are [5]:
- SEI growth
- SEI decomposition
- Electrolyte decomposition
- Lithium plating and dendrite formation
Loss of Active Material (LAM) in anodes could be caused by:
- Binder decomposition of the anode
- Graphite exfoliation
- Lithium plating and dendrite formation
- Loss of electric contact
- Electrode particle cracking
- Transitions metal dissolution
- Corrosion of current collectors
In cathode, the loss of active material (LAM) can stem from:
- Binder decomposition of the cathode
- Structural disordering
- Loss of electric contact
- Electrode particle cracking
- Transitions metal dissolution
- Corrosion of current collectors
Managing the temperature and operating conditions are key to minimize degradation
Different operating regimes of temperature, SOC, current, and DOD will lead causes of degradation. Here is a list of their effects on the Li-ion cell aging:
High Temperatures
- SEI layer growth rates on the anode, resulting in faster LLI and cell resistance increase.
- Metal dissolution from the cathode.
- Electrolyte decomposition.
- Extremely high temperatures may trigger “thermal runaway”, the ultimate threat.
Low Temperatures
- Slow down the transport of Li ions in both electrodes and in the electrolyte.
- Attempts of fast charging at low temperatures may thus create crowding of Li ions.
- This may cause (local) lithium plating of graphite, which comes with LLI.
- Continuous inhomogeneous lithium plating will eventually cause the growth of lithium dendrites, which may penetrate the separator and short circuit the cell.
Overcharge/Over discharge
- When a cell is overcharged, the cathode is over-delithiated (no active lithium available), and the anode is over-lithiated (no more ‘room’ for lithium).
- The cathode material suffers from irreversible structural change when over-delithiated, followed by the dissolution of transition metal ions (such as Mn2+) and active material decomposition.
- Decomposition of the electrolyte and significant increase in the total internal resistance.
- Generate significant heat, due to Joule heating and the heat generated by a series of side reactions at both electrodes.
- During over-discharging, the anode potential increases abnormally which leads to the anodic dissolution of the copper (Cu) current collector and formation of Cu2+
Upon recharging, the reverse reaction can form copper dendrites, which may lead to internal short circuit.
High currents
- Excessive charge and discharge currents can cause localized overcharge and discharge to occur
- High currents come with more heat waste, which can raise the cell temperature and concomitantly the rates of aging processes.
- Once Li-ion batteries use organic electrolytes, their relatively low heat capacity makes them unusually prone to rapid temperature increase upon current flow if compared to water-based batteries.
- For graphite anodes, fast charging also results in metallic Li plating due to the graphite’s limited ability to accept Li-ions at high rates, leading to LLI.
It is therefore not surprising that Li-ion batteries can exhibit significantly different lifetime, not only based on their construction and materials, but also on their operation. Proper control of the battery usage with an appropriate BMS and aging algorithms can therefore have a dramatic impact of the battery lifetime. Thermal management should be closely integrated with the BMS to regulate the temperature of the cells and adapt the current draw or charging when out of the preferred temperature range. Battery thermal management therefore plays a critical role in maximising battery life, which in turn improves the user experience while reducing the overall cost and the environmental impact of using Li-ion batteries. We cannot live without them and they provide a unique path towards a more intelligent and greener future, so let’s do all we can to We could not do without Li-ion batteries today, so let’s do all we can to maximise their impact.
References:
[1] MarketsandMarkets (2020), Lithium-Ion Battery Market, Report SE 4967.
[2] Romare, M., Dahllöf, L. (2017). The life cycle energy consumption and greenhouse gaz emissions from Lithium-Ion Batteries : A study with focus on current technology and batteries for light-duty vehicles. Stockholm, Sweden: Swedish Environmental Research Institute.
[3] S. Santhanagopalan, K. Smith, J. Neubauer, G.-H. Kim, M. Keyser, and A. Pesaran, Design and Analysis of Large Lithium-Ion Battery Systems. Artech House, 2015.
[4] Y. Choi, S. Ryu, K. Park, and H. Kim, “Machine Learning-Based Lithium-Ion Battery Capacity Estimation Exploiting Multi-Channel Charging Profiles,” IEEE Access, vol. 7, pp. 75143–75152, 2019.
[5] M. Woody, M. Arbabzadeh, G. M. Lewis, G. A. Keoleian, and A. Stefanopoulou, “Strategies to limit degradation and maximize Li-ion battery service lifetime – Critical review and guidance for stakeholders,” J. Energy Storage, vol. 28, no. October 2019, p. 101231, 2020.
