Evaluation of 1D CNN Autoencoders for Lithium-ion Battery Condition Assessment Using Synthetic Data Christopher J. Valant1, Jay D. Wheaton2, Michael G. Thurston3, Sean P. McConky4, and Nenad G. Nenadic5 1,2,3,4,5 Rochester Institute of Technology, Rochester, NY, 14623, USA [email protected] [email protected] [email protected] [email protected] [email protected] ABSTRACT (Yan & Yu, 2015), whereas classical machine learning mod- els require a more statistically large data set (Bishop, 2013), To access ground truth degradation information, we simulated with a significant number of instances of failure, which are charge and discharge cycles of automotive lithium ion bat- expensive to collect. By the time of this study, autoencoders teries in their healthy and degrading states and used this in- have become the first choice method for anomaly detection in formation to determine performance of an autoencoder-based PHM (Eklund, 2018). anomaly detector. The simulated degradation mechanism was an abrupt increase in the battery’s rate of time-dependent ca- PHM systems can evolve in time, growing their capabilities pacity fade. The neural network topology was based on one- from anomaly detection, towards diagnostics and prognostics dimensional convolutional layers. The decision-support sys- (Sikorska, Hodkiewicz, & Ma, 2011; Bussey, Nenadic, Ardis, tem, based on the sequential probability ratio test, interpreted & Thurston, 2014). As more failure data becomes available, the anomaly generated by the autoencoder. Detection time representation learning features of deep learning can be ex- and time to failure were the metrics used for performance ploited to learn better Condition Indicators (CIs). The exper- evaluation. Anomaly detection was evaluated on five differ- iments with real world data have demonstrated the potential ent simulated progressions of damage to examine the effects of the deep learning models to effectively detect anomalies. of driving profile randomness on performance of the anomaly However, because the ground truth of failure is typically not detector. accessible without significant feature engineering (see, e.g., the fundamental axioms of structural health monitoring, and 1. INTRODUCTION Axiom IVa in particular (Worden, Farrar, Manson, & Park, 2007)), we explored the effectiveness of deep learning mod- The layers of capability of Prognostics Health Monitoring els using simulated failures. The selection of the physical sys- (PHM) in ascending order are: anomaly detection, diagnos- tem and its model that generated synthetic data was based on tics, and prognostics. This manuscript is chiefly concerned the following criteria: the system had to be well-researched with the first capbility - anomaly detection. An early success- in PHM community (to provide familiarity with the nature of ful demonstration of an autoencoder neural network for vibra- the solution and facilitate intuitive interpretations), it had to tion data provided the first layer of Prognostics Health Mon- be nonlinear (to avoid trivial solutions), with information con- itoring (PHM) capability – anomaly detection (Japkowicz, tained in multiple time scales, and to have existing published Myers, Gluck, et al., 1995). Since then, there has been a models (to leverage known results and reduce the debugging revolution in training deep neural networks, which facilitated time). A lithium ion battery system quickly emerged from training of deeper, more expressive neural network models this criteria as a good candidate since the system was well- and demonstrated its performance on many important ma- researched in the PHM community (see e.g. (Saha & Goebel, chine learning tasks (LeCun, Bengio, & Hinton, 2015). The 2008; Olivares, Munoz, Orchard, & Silva, 2013)). significant advantage of the deep learning approach in PHM is that it can be trained on abundantly available normal data The simulated battery system enabled the study of a 1D Con- volutional Neural Network (CNN) autoencoder, built to de- Christopher J. Valant et al. This is an open-access article distributed under the terms of the Creative Commons Attribution 3.0 United States License, which tect anomalous behavior where ground truth knowledge of permits unrestricted use, distribution, and reproduction in any medium, pro- how the system degraded up to failure was known. Degrada- vided the original author and source are credited. 1 ANNUAL CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2019 tion was set in the form of battery cell capacities. A driving Ultimately, the battery behavior for the purpose of anomaly profile in the form of power was input into the simulation, detection is represented by battery stack voltage and current where alternating cycles of charging and driving with regen- waveforms. erative braking were present. 2. METHOD To better evaluate performance of an autodencoder as an anomaly detector, we decided to build a simulation model be- cause real failures of high-severity, low-frequency type (the type that is of interest to PHM), are expensive and the ground truth is often not accessible. The first step was to select the system to simulate. As stated above, the requirements were that the model be complex and nonlinear, with multiple tem- poral scales. With nonlinear relationships between series re- sistance and state-of-charge (SOC), open-circuit voltage and SOC, a couple of minutes time constant associated with re- laxation and charge/discharge cycle on the order of hours, the lithium ion battery had the desired characteristics. Section 3 describes the customized simulation. Because the focus was to assess anomaly detection capabil- Figure 1. A closer view of charge up and driving with regen- ity of an autoencoder-based model, the model was trained on erative braking. normal data to learn the internal representation. The details of data preprocessing and model development are provided in Section 4. 3.2. Extending a published simulation library We built a decision-support layer to operate on the error sig- Simulating faults required extensions of the EES’s Advanced- nal produced by the autoencoder (the difference between its StackCycling simulation, including updated constants along input and output) to assess the performance of the model with with protection against over-voltage during regeneration, respect to its capability to operate as an anomaly detector. variable time-based capacity aging, variable resistance with Section 5 describes the decision support and the metrics used SOC, and measurement noise. The extended top level block for the assessment in detail. diagram is shown in Figure 2 with modifications of the origi- nal example explained in turn. Finally, in Section 6, we compared performances of multiple simulated failures to understand the sensitivity of the model mu=0.003 I att cellParameters INormalNoise B to the rate of degradation and to the randomness of the driving +1 cycle. idealCommutingSwitch sigma=0.05 + 1 s +1 A 3. BATTERY MODEL SIMULATION currentSensor 3.1. Simulation environment voltageSensor Standard Modelica libraries, with Open Modelica interface FTP72 on dch. Vmax np OMEdit were used for simulation (Fritzson et al., 2006). The V ch. battery model was based on the Electrical Energy Storage driving Vmin ns Battery Stack (EES) library (Einhorn et al., 2011). The EES library was on cycler V Batt v V chosen because it was readily available, has been referenced NormalNoise +1 mu=0 in literature, and provided non-trivial examples that served as + charger +1 the starting point for our battery simulations. We started with ground sigma=0.001 the example AdvancedStackCycling which includes a three 1 s cell battery stack, charger, vehicle driving cycle, battery man- Figure 2. Top level Modelica stack cycling block. agement system, and observable states. An example simula- tion output of one cycle including the voltage, current, SOC are shown in Figure 1. This includes a Constant Current (CC) 3.2.1. Over-voltage protection - Constant Voltage (CV) charge cycle followed by a rest pe- Over-voltage during the driving cycle was a consequence of riod before entering a drive cycle with regenerative braking. the way the simulation model extrapolates the drive cycle 2 ANNUAL CONFERENCE OF THE PROGNOSTICS AND HEALTH MANAGEMENT SOCIETY 2019 power in a Modelica CombiTimeTable. With periodic extrap- C = C0 + K(t) · t (1) olation, the time-series drive cycle was continuously repeated ( to ensure a value existed for the entire simulation time. Since kC ; t ≤ tonset K(t) = (2) the table was time-based rather than event based, each driving md · kC ; t > tonset cycle started at a different point on the drive cycle, not nec- essarily at the beginning. This can result in the driving cycle 3.2.3. Variable resistance with SOC starting at a high regeneration period (charging) with a fully charged battery. Without protection, we observed voltage Batteries typically exhibit higher resistance at a lower state spikes above the maximum cell voltage in certain instances. of charge. This behavior was implemented in the simulations This was mitigated by adding a two-second moving average as shown in Figure 4. A linearly decreasing resistance occurs % on the voltage to compare with a set threshold voltage 1% be- until SOC reaches 50 and becomes a constant value. While this functional dependence was not quantitatively based on a low Vmax. If this threshold was exceeded, current was set to zero to represent a regeneration safety limit in a vehicle. This specific battery behavior, it provided a qualitative model that resulted in a slightly different simulation profile for each driv- was more accurate than assuming a constant resistance. In ing cycle, adding complexity to the model. 0.975 3.2.2. Capacity aging ] m 0.950 [ In addition to voltage protection, complexity was added by e c n 0.925 including multiple linear capacity aging rates to represent a t s i healthy and faulty capacity fade over the lifetime of the bat- s 0.900 e R tery. Capacity aging was defined as a time-dependent adjust- l l 0.875 e ment to the battery capacity as shown in Eq.