Key Battery Terminologies
Every Designer Must Know
14 essential terms — defined precisely, explained in engineering depth, and connected directly to design decisions. These are not just vocabulary; they are the working language of battery engineering.
SOC is not a directly measurable quantity. Unlike voltage or current — which can be measured instantly with sensors — SOC must be estimated. In practical system design, batteries are rarely operated between 0% and 100% SOC. A typical EV pack operates between 10% and 90% SOC, preserving electrochemical stability and extending cycle life significantly.
SOH captures battery degradation — the irreversible loss of capacity and increase in internal resistance that occurs over time as a result of cycling and calendar aging.
If an EV requires 60 kWh of usable energy at end of life (80% SOH), the pack must be initially designed for approximately 75 kWh. This built-in end-of-life buffer is called the capacity margin and is a standard element of every battery pack sizing methodology.
Cycle life is a strong function of depth of discharge. Cells cycled to shallow DOD sustain dramatically more cycles than cells cycled to 100% DOD. The relationship is non-linear — halving the DOD can increase cycle life by an order of magnitude or more.
By limiting the usable SOC window — for example, operating a pack only between 20% and 80% SOC (60% effective DOD) — the designer can dramatically extend cycle life at the cost of using only a portion of the pack's total energy. Tesla's BMS limits daily charge to 80% SOC by default for this reason.
These two terms are often used interchangeably in casual conversation but represent fundamentally different physical quantities. Confusing them is a common beginner mistake with real engineering consequences.
Two cells with the same capacity can have very different energy if their voltages differ. A 100 Ah LFP cell (3.2 V) = 320 Wh. A 100 Ah NMC cell (3.6 V) = 360 Wh. When comparing cells or calculating pack energy, always use energy — not just capacity — to make accurate comparisons.
Nominal voltage is a single representative voltage value assigned to a cell for calculation and labelling purposes. It is not the voltage at which the cell operates at any specific instant — it is a simplified average used to make system-level calculations tractable.
The actual voltage of a lithium-ion cell varies continuously with state of charge, current, temperature, and age. The nominal voltage sits somewhere in between the upper and lower cut-offs — representative of the mid-point of the discharge curve, but rarely the actual operating voltage at any given moment.
A 48V nominal pack built from 13 NMC cells in series has a maximum voltage of 54.6 V (13 × 4.2 V) and a minimum voltage of 36.4 V (13 × 2.8 V). Any electronics connected — inverters, DC-DC converters, chargers — must be designed and rated for this full voltage range, not just the 48V nominal.
| Chemistry | Standard UCV | High-Energy Variant | Risk of Exceeding |
|---|---|---|---|
| NMC 622 / 811 | 4.2 V | 4.35 V – 4.4 V | Thermal runaway risk |
| LFP | 3.65 V | — | Accelerated aging |
| LCO | 4.2 V | — | High — oxygen release |
| LTO | 2.7 V | — | Very low risk |
Exceeding the upper cut-off voltage triggers a cascade of harmful events. At the cathode, the crystal structure begins to break down — releasing oxygen. At the anode, excess lithium deposits as metallic lithium on the anode surface. Metallic lithium can form dendrites and is the precursor to thermal runaway in overcharge scenarios.
The BMS must monitor every cell (or cell group) individually and terminate charging before any individual cell exceeds its upper cut-off voltage. In series strings where cell-to-cell capacity variation means some cells reach full charge before others, this is a key driver of the need for cell balancing.
LFP: 2.5 V per cell
LTO: 1.5 V per cell
LCO: 2.8 V per cell
The pack designer must ensure that the electrical architecture — particularly the contactor and protection circuit design — can reliably interrupt discharge current when the lower cut-off is reached, including under high-current conditions where voltage sag may cause a momentary dip below the lower cut-off even when average SOC is still safely above zero.
| C-rate | Current (50 Ah cell) | Duration | Heat Generation |
|---|---|---|---|
| 0.2C | 10 A | 5 hours | Very Low |
| 0.5C | 25 A | 2 hours | Low |
| 1C | 50 A | 1 hour | Moderate |
| 2C | 100 A | 30 min | High |
| 3C | 150 A | 20 min | Very High |
| 10C (peak) | 500 A | 6 min | Extreme |
If a system requires 200 A peak current and the selected cell has a peak limit of 10C at 5 Ah (50 A per cell), then a minimum of 4 cells in parallel are needed to share the current within the per-cell limit. C-rate specifications directly drive the minimum parallel cell count in every pack design.
Internal resistance (IR) is the opposition that a battery presents to the flow of electrical current through its internal structure. It is the single most important electrical characteristic of a cell after its capacity and voltage, with direct consequences for every performance metric that matters to a pack designer.
A cell with 10 mΩ internal resistance carrying 100 A generates 100 W of heat. In a 96-cell series pack all carrying the same 100 A, total resistive heat generation is 9.6 kW — requiring a substantial active cooling system. Always use the worst-case (highest) internal resistance value from the datasheet for thermal calculations.
These two measures of battery longevity are fundamentally different in their mechanisms, measurement, and design implications — yet they are often conflated or one is neglected at the expense of the other.
In real-world applications, usable battery life is determined by whichever of cycle life or calendar life is reached first. For most EVs driven limited kilometres per year, calendar life is often the binding constraint. For high-utilisation ESS performing two cycles per day over 15–20 years, cycle life is binding. Pack designers must account for both simultaneously.
These two terms describe the fundamental performance trade-off in battery design and are the primary axes on which different chemistries and cell formats are differentiated.
Cells optimised for high energy density — thick electrode coatings, high-nickel cathodes — tend to have higher internal resistance and lower rate capability. Cells optimised for high power density — thin coatings, large surface area — sacrifice energy density to minimise resistance. Energy density and power density cannot both be maximised simultaneously. The cell selection process must start with understanding which is the binding constraint for the application.
Even cells from the same manufacturing batch have small but real variations in capacity, internal resistance, and self-discharge rate. Over time, these differences accumulate. The cell with the lowest capacity reaches its upper cut-off first during charging and its lower cut-off first during discharge — limiting the entire pack's usable energy to the weakest cell.
The three primary trigger mechanisms are electrical abuse (overcharge, over-discharge, external short circuit), thermal abuse (excessive external heat from adjacent cells, inadequate cooling), and mechanical abuse (physical damage, crush, or penetration causing an internal short).
Pack designers address thermal runaway through a layered strategy: prevent trigger conditions (electrical protection + thermal management), limit severity of a single-cell event (cell-level venting + gas management), and prevent propagation (thermal barriers, cell spacing, pack-level venting). Full coverage in Chapter 7 — Safety Design.
OCV is a function of state of charge and temperature. For a given chemistry, the OCV vs. SOC curve is a well-characterised relationship used as a reference for SOC estimation — determined by the electrochemical potentials of the cathode and anode materials at different states of lithiation.
OCV is the voltage measured at rest when the system is first powered on — used to initialise the SOC estimator. It is used to verify cell health during incoming inspection. And the shape of the OCV-SOC curve for the chosen chemistry directly influences the design of the SOC estimation algorithm in the BMS — a decision pack designers must communicate clearly to the BMS engineering team.
