I'm not aware of any specific issue with battery reliability in Favero products. However, they claim the battery life to be 50-60hrs on a single charge and the battery will charge to 80% of rated capacity after 500 charge cycles.
This is a typical spec for a li-ion battery or any other rechargeable lithium chemistry (so good luck with those BEV's folks)
Garmin Rally offers up to 90hrs suggesting that their internal rechargeable Li-ion is of 50% higher nominal rated capacity which will mean you don't charge it as often and hence, if you ride 20hrs per week those 500 charge cycles will take you further than the 500 charge cycles of the Favero products.
Charging methodology in the software can extend either products battery life (to 80% rated capacity) by up to 50% (700-800 charge cycles) but after this the battery is literally dying of old age.
Now Garmin says they have a charge boost feature which allows you to put about 12 hrs of battery life into the battery in just 15min. This feature will likely reduce the useful life of the battery.
From Google Ai:
Reporting issues: Some users have reported issues with battery performance declining prematurely or with one pedal failing to charge. If this happens, Favero advises contacting customer support through the app for troubleshooting.
The above isn't unusual for a Li-ion or an other battery type. Batteries are packed by machine and the quality assurance is automated. There will still be substandard cells that get through the QA process and end up in our purchased products:
I took this from ScienceDirect:
"
Introduction
Lithium-ion batteries are the prevalent technology for on-board energy storage in electromobility, mainly due to their high energy density – increasing two-fold over the last ten years – and their rapidly declining cost [1], [2]. However, incidents such as vehicle fires and rapid battery pack failure or deterioration resulting from fast charging pose a danger to passengers and could also harm the reputation of cell manufacturers and original equipment manufacturers (OEMs). Incidents of this nature can in part be attributed to insufficient cell quality and inadequate methods for assessing cell quality at the manufacturer’s facility [3].
Stochastic variations in the operative manufacturing conditions lead to cell inhomogeneities [4], [5], [6]. These include performance differences in electrode raw materials, variations due to machine wear and errors, or (sudden) changes in operating conditions (e.g. temperature, pressure, and humidity). Ultimately, the cumulative tolerances of chemical, mechanical, and electrical processes result in an inevitable minor to moderate production scatter between the produced lithium-ion cells [6], [7], [8], [9], [10].
Unless these errors are detected in the production process, they will result in potential defects in the produced battery cell, including impedance and capacity variations, varying self-discharge and heat generation rates, surface cracks, scratches, exposed foils, leaks, and overall varying attenuation velocities in performance [5], [9], [11].
Although the scrap rate per process step is in the range of tenths of a percent to a few percent [12], the accumulation of errors leads to a total production reject rate of 5 to 10% depending on the maturity and experience of the manufacturer [13], [14]. Consequently, if subsequent system-level failures are to be mitigated, detecting defective cells becomes a priority. One single defective cell in the pack is potentially enough to jeopardize the safety or function of the entire vehicle. Therefore, it is essential to ensure thorough cell screening at the end of production. In assembled battery modules for battery electric vehicles (BEVs), if they are not discarded, dissimilar or faulty cells can lead to a variation in the performance of modules (depending on whether a series or parallel configuration is used), an uneven temperature distribution, incomplete charging or discharging of several cells, safety problems, or accelerated degradation with effects up to and including premature failure [5], [9], [15], [16].
The large number of cells in current BEV battery packs increases the chance of having an outlier, should no sophisticated screening be deployed before distribution and assembly [17]. The shift to so-called cell-to-pack (CTP) designs, integrating many individual cells structurally and almost non-interchangeably into the vehicle design [18], [19], further increases this outlier risk. Therefore, individual defective or compromised cells can have major ramifications.
The end-of-line (EOL) test – together with the subsequent grading – is the last process step during cell finalization, representing the last quality control prior to distribution to the respective customer. Essential cell properties are measured and checked in-line providing a traceable indicator of the overall cell performance. The non-invasive process step involves two types of categories, electrical and non-electrical tests.
The former include internal resistance measurements via direct current internal resistance (DC-IR) or alternating current internal resistance (AC-IR) measurements for at least one state-of-charge (SOC) point as well as the monitoring of the open-circuit voltage (OCV) by means of additional self-discharge tests and insulation tests used dependent on the application [20], [21], [22], [23]. Nowadays, it is assumed that complete capacitance checks are often omitted, because cells with insufficient capacitance would be detected in the preceding process step of the formation. However, individual remeasurements per production batch may remain necessary to ensure that unilaterally limited capacity specifications are met. A special focus in quality assessment tends to be on the respective cell-to-cell variation within a production lot and the data-driven cycle life prediction, which can also be attempted on the basis of cell-specific formation data [24].
In the latter, optical inspections are usually carried out as part of further non-electrical tests to detect welding defects and impurities as well as cracks in the sealing seams in pouch cells. Dimension and weight tolerance checks are performed to maintain allowances for problematic cell swelling during module assembly and to avoid exceeding the total acceptable pack weight.
Simple tolerance bands, and dynamic procedures up to and including model-based and neural network procedures are used for quality classification [5], [25], [26], [27], [28], [29], [30], [31], [32]. For an extensive overview of classification strategies for lithium-ion batteries, the reader is referred to Li et al. [33] and Li et al. [4].
In large-scale industry applications, proprietary measurement protocols are used, based on IEC 62660-1 [34] or similar standards. Both 100% testing and testing of just a few cells per production lot are common, with durations typically less than 24 h per EOL test. These statements were confirmed in a personal interview (CUSTOMCELLS Holding GmbH, Germany, personal communication, May 25, 2022).
If cell production is separate from module construction in terms of location and organization, an incoming inspection analogous to the EOL test is often integrated into the process. The cell properties are determined and checked again, e.g. with constant current constant voltage (CC-CV) capacity checks, DC-IR determinations, OCV checks or impedance measurements at 1 kHz or 10 kHz (AC-IR) carries out on selected cells (BMZ Germany GmbH, Germany, personal communication, September 01, 2022).
Detailed specifications for quality testing, which are not subject to the non-disclosure agreements (NDA) of the respective manufacturers, would be, because of the insufficient publicly available data and the mentioned necessity for high quality cells (Section 1.2), of considerable interest for academia. Considering the sheer number of cells produced, it is of great value to reduce the testing time and cost [21].
In principle, destructive and non-destructive analysis methods can be used to detect defects in batteries, although only the latter are reasonable in the production environment of the value chain as they do not degrade battery performance [35]. Wu et al. [35] have shown that some defects can be detected using a non-destructive computed tomography (CT) scan, whereas Sazhin et al. [36] demonstrated that internal short circuits can be evaluated by measuring the self-discharge current. David et al. [37] evaluated the impact of four electrode coating defects on cell performance using large-format 0.5 Ah pouch cells and verified that cells with non-uniform coatings in the form of line defects showed a severe capacity fade due to cathode degradation. The electrical and non-electrical methods of EOL testing mentioned in the preceding section allow the identification and classification of more or less severe cell-quality outliers, evidenced by the use of these methods in industry. However, with the exception of homogeneity studies performed by Xie et al. [38], [39], the authors of this study are not aware of any publications dealing with electrical and holistic fault detection on series-connected cells that could further increase the present throughput of parallelized quality tests.
This article presents a novel method for the simultaneous characterization of multiple lithium-ion cells with the purpose of end-of-line test optimization in the context of cell production. On the basis of intentionally induced production defects, the detection of these faults is demonstrated. Extensive single-cell characterization provides measurement data for potential defect cases in lithium-ion cell production. In brief, the main elements and the scope of this study can be summarized as follows:
•
Production of coin cells with potential and application-related production defects 46 coin cells were built, of which 22 cells were used as references and six different fault types were represented by four cells each.
•
Investigation of the influence of these defect types on formation and single-cell characterization After assembly, the cells were measured during formation and in single-cell characterization, allowing an in-depth comparison of the observed variations.
•
Measurement of multiple series-connected cells (multi-cell characterization) demonstrating an optimized EOL-testing approach The measurement methodology allows the detection of the production defects shown while economizing on the required number of test channels.
The remainder of this article is structured as follows: Section 2 introduces the multi-cell characterization method of lithium-ion cells and its potential area of application. Section 3 describes the experimental process in which production defects are incorporated in the coin cells and the different fault types considered, as well as the applied test procedures for formation, single-cell characterization, calendar aging and multi-cell characterization. The results of the measurements of the separate process steps are presented and discussed in Section 4. Finally, Section 5 summarizes the findings and describes possible future work."
