Whitepaper Battery Health Report - Chapter 4

Battery-ageing simulation – comparing three treatments

As established, batteries are sensitive, and multiple factors (temperature, mileage, geography, topography) influence their ageing process. For the purposes of this paper, however, we focused on three specific factors – driving, charging, and parking behaviour.

Why? Because we know that the usage behaviour of the BEV driver – how they drive, to what intensity level they charge their battery, how much electricity is consumed during driving time (e.g. impact of use of air conditioning) – is one key determinant of battery degradation.

TWAICE battery experts designed and carried out a simulation with the aim of better assessing the impact of battery treatment and its financial impact. Three levels of treatment behaviour were investigated: worse, average and better battery treatment. All relate to and have a (greater or lesser) effect on the degradation of a BEV battery.


Simulation conditions

LocationGermany
Mileage15,000 km p.a.
TimeframeThree years
Battery SizeAverage (‘Golf’ class)

Fact: All conditions are considered equal. Variations in these have a noticeable impact on battery health.


For the purposes of the simulation, the border conditions – temperature, mileage, geography – were all constant. We assumed an annual mileage of 15,000km over three years. The simulation represents driving in Germany (no speed limits on large parts of the national motorway system, temperate northern European climate).

Table 1: Three simulated treatments (all other factors constant)

Driver 1 (worse treatment)Driver 2 (average treatment)Driver 3 (better treatment)
General driving styleAggressiveAverageAnticipatory
Use of motorwaysRegularlySometimesRarely
Fast chargingFrequentlyOccasionallyNever
Charging with a depth of discharge > 90%FrequentlySometimesRarely
Average depth of dischargeHighMediumLow
Average state of charge storage level80-100%50-80%30-50%

Driver 1 represents worse battery treatment. Their general driving style can be termed aggressive; this kind of driver might be inclined to accelerate sharply when the light turns green, for example, and rarely practices regenerative braking. Factors such as traffic conditions and time of day will influence the degree of ‘aggressiveness’ – heavy traffic, for example, will automatically reduce it. Probably under time pressure and with a lot of mileage to cover, they use the motorways a lot, which means frequently fast charging their BEV. The average depth of discharge is correspondingly high. These drivers may well suffer from ‘range anxiety’, which will impact their charging treatment – the main motivation
being never to run out of range. The state of charge storage level is between 80% and 100%.

Driver 2 is our Joe Average, representing average battery-treatment behaviour and an ensuing neutral effect on battery-ageing. These drivers are in the grey zone between their aggressive and gentle counterparts. In a real-world scenario, they might be conscious of the recommended conditions and behaviour for BEV drivers and will generally try to adhere to these; however, unplanned events sometimes make this impossible. They are mixing the urban trips with some long-haul motorway journeys.

Driver 3 is at the other end of the scale and displays better battery treatment behaviour. This kind of driver is likely to take full advantage of the potential to regenerate energy, a concept known as eco-driving. Their anticipatory driving style is gentle, which can be attributed to their lifestyle and the factors affecting it: compared to Driver 1, they may rarely feel under time pressure and never carry out fast charging, presumably, because they never carry out fast charging, presumably, because they never need to.

Figure 1: Battery state of health (SoH) over time

Figure 1 shows the SoH development of an average-sized battery under the three simulated treatment conditions (worse, average, better battery treatment). While all three SoH show a gradual linear decrease after year one, the graph clearly depicts how high-intensity battery treatment can make a considerable difference – as much as 4.5%-points – to battery capacity at a 36 months point in time. The simulation also enables predictions of what happens thereafter, even bigger differences emerge. Assuming the state of health of 80% to represent the end of life, we see a spread of 41% end-of-life difference between the worse and the better treatments (see Table 2).

Table 2: End-of-life predictions

Driver 1
(worse treatment)
Driver 2
(average treatment)
Driver 3
(better treatment)
Battery end of life (SoH at 80%)~ 8.5 years~ 10.5 years~ 12 years

All the above demonstrates how beneficial it would be to assessors of BEV residual values to have access to (1) the treatment history of a battery and (2) prediction information regarding remaining battery life. Gaining data on the operating condition of a battery is key to assessing the remaining useful lifetime – this much is clear. But how can this information be obtained? This section addresses the question and provides a possible solution.

Data is the essential enabler of understanding battery condition and the remaining useful lifetime.

As established, capturing battery data is essential to providing information on the current condition and the remaining useful lifetime. But what kind of data are we actually talking about?

Predictive battery analytics: TWAICE Digital Twin

Using readily available data from the BMS, the TWAICE software creates a virtual copy of the real battery capturing its entire usage history – a so-called digital twin.
This digital twin provides information not only on the current condition, the remaining useful lifetime and capacity/performance development but also enables simulations about changes in operating strategies, e.g. second life.
The key lies in the combination of augmenting physical realities and data- and model-driven predictions. The approach merges the benefits of empirical models with machine learning. It delivers reliable SoH determinations and predictions from the outset and continues to provide improving analytics with an increasing data pool.

Battery status: Capacity

A first indication on the quality of the battery can already be provided by driving a so-called full cycle with the battery, i.e. completely charging and discharging it, to identify its capacity and the corresponding range. The information on the current capacity does provide the information about the available performance at that time but cannot provide sufficient information about the remaining useful lifetime.

Battery history: Load characteristics and operating conditions

To enable the assessment of the used battery and its remaining useful lifetime, the consideration of its use is essential. This information ranges from the total energy throughput and cycle amount to the depth of discharge, the energy throughput at each time (e.g. charging), to the temperatures within the system. Whilst the data is always generated by sensors in the battery system and used by the BMS to prevent malfunctions, the information is not always saved or made available. In addition to the battery-system data itself, the operating conditions of the BEV also play a role. Climate, to just name one, can have an adverse effect on a battery exposed to excessive heat, humidity or cold. Such factors (except for actual temperature changes) are not captured by regular systems but their effect can be identified by assessing the electric-thermal behaviour of batteries.

In summary, the availability of detailed data from the vehicle and the battery can make a big difference in the assessment of used vehicles. From rough indications on the quality (e.g. current capacity) to precise predictions about the remaining useful lifetime and capacity, as well as performance developments, the assessments depend completely on data.

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