Anna Heising
The global transition to e-mobility is gaining momentum, revolutionizing the transportation industry and paving the way for a cleaner, more sustainable future. Batteries are at the centre of this revolution.
Batteries generate high volumes of data, and this information can be tremendously valuable when software is applied. In recent years cloud-based battery analytics has emerged as a game-changer. Recognizing that improving battery efficiency is a key driver in the widespread adoption of electric vehicles, this article explores the value of battery cloud monitoring solutions for electric vehicles (EVs) and their role in battery safety, efficiency, and cost savings.
Battery Management Systems’ Role in Battery Operation
The Battery Management System (BMS) functions as the core processor of a battery, responsible for ensuring that the battery operates within its designated parameters while providing abstract data such as State of Charge (SOC) to the higher-level energy management system. It continually monitors the voltage, current, and temperature of all battery modules to carry out its tasks. However, if a sensor malfunctions or if the BMS logic becomes corrupted, it can result in potentially hazardous situations, such as:
- Unintended shutdown because of an incorrect assessment that the battery has exceeded its operational limits.
- Inaccurate SOC calculations leading to an underutilization of the battery and system imbalance.
- Deep discharging, which can cause the anode tab’s copper to dissolve and create an internal short-circuit risk.
- Overcharging, potentially leading towards a dangerous thermal runaway event.
Three Challenges Facing Electric Fleet Operators
Fleet operators must ensure that their electric vehicles operate efficiently, reliably, and safely. These are our customers’ top three challenges and where battery analytics can help.
- Ensuring Battery Safety and Reliability
Battery failures can have severe consequences, ranging from financial losses to safety-critical events. Battery cloud monitoring acts as an early warning system, providing insights into potential safety issues like cell imbalance, overcharging or discharging, excessive plating, insufficient cell quality, manufacturing defects, assembly faults, and abnormal temperature behaviour. By promptly detecting these anomalies, users can take a proactive maintenance approach helping to prevent catastrophic battery failures.
- Identifying Performance and Efficiency Issues
For fleet operators, unexpected failures and downtime result in higher maintenance expenses and often a loss in revenue. For example, an incorrect SOC estimation can lead to a lower range than indicated to the driver. This misinformation often results in a vehicle breakdown.
By using cloud computing to monitor parameters such as voltage, current, and temperature, it becomes possible to identify inefficient charging or discharging patterns. Users can fine-tune charging strategies, optimize energy usage, and extend battery life. This not only improves the overall efficiency of battery-powered systems but also reduces operational costs and environmental impact.
- Optimizing Replacement and Maintenance
Unexpected downtime and operational disruption can also be caused by sudden replacement and maintenance needs. To minimize those disruptions, battery cloud monitoring supports optimizing battery replacement cycles by enabling proactive planning and budgeting for battery maintenance. It facilitates effective maintenance strategies by providing actionable data on battery health.
By monitoring individual cell performance within a battery pack, it’s possible to identify weak or deteriorating cells that may compromise overall system performance. An accurate assessment of battery degradation helps determine the remaining useful life of batteries. This information enables targeted maintenance, such as cell replacement or balancing, rather than replacing entire battery packs unnecessarily.
Unexpected battery failures can lead to vehicle breakdowns, service calls, and time-consuming root cause analysis. While it might only be one vehicle within the fleet having a problem, this negatively impacts the entire fleet’s operation.
But, what causes a battery to fail?
Case Study: EV Battery Breakdown
It’s a scenario that is all too common for fleet operators—a vehicle stranded on the side of the road. In this case, the vehicle appeared to have a remaining SOC of 20% when it failed. The fleet operator reached out to ACCURE for a root cause analysis.
After analysing the data, we found that the voltage curve of the vehicle contains several deep discharges. Figure 1 displays the latest discharge before the breakdown, showing a huge spread of the minimum, average, and maximum cell voltage. In a normal scenario, this should not occur. While this is not a safety-critical issue, it can affect performance.
The BMS determines the SOC based on the battery’s nominal capacity — the amount of energy delivered by a fully charged battery. The result is not an accurate representation of the remaining vehicle range. Instead, the determination of the actual usable SOC must consider the minimum cell voltage.
It’s important to understand the relationship between voltage and SOC. The battery’s voltage fluctuates, either decreasing or increasing, based on their level of charge. When the battery is fully charged, the voltage reaches its peak, while it drops to its lowest point when the battery is empty. The specific correlation between voltage and SOC is directly influenced by the type of battery technology employed.
To assess the practical SOC for batteries during operation, diagnosis techniques such as the Open Circuit Voltage (OCV) curve can be employed. Each battery cell possesses a distinctive OCV curve (Figure 2), serving as a unique fingerprint for individual cell types. The OCV curve is the voltage curve of a battery as a function of the charge when no external current is flowing (and all chemical reactions inside of the battery are relaxed). If there is an OCV value given, it’s easy to determine the respective usable SOC – and the other way around.
Considering the minimum cell voltage of the time series in Figure 1, the OCV curve shows an actual usable SOC of around 1% in Figure 2, even if the vehicle appeared to have 20% left.
The problem was clear. The failure was due to a systematic error in the SOC calculation by the BMS.
Monitoring the vehicle’s battery data from its first day of operation can prevent and reduce downtime. In this example, cloud monitoring would have detected the battery behaviour in advance, allowing the operator to proactively address the problem.
Detecting Malfunctions with Battery Cloud Monitoring
The EV breakdown described above is just one example of how battery cloud monitoring can detect problems in advance. By using larger data sets and cloud computing power, many other battery problems can be identified, including:
- Rapid capacity changes (cell connection issues or knee-points of individual cells)
- Excessive battery self-discharge behaviour (internal micro-short circuits)
- Anomalies in battery impedance (weak cells or manufacturing imperfections)
- Anomalies in electrode potential (loss of lithium or loss of active material)
- Hot and cool spots (cooling system outages, contaminations within a cell, bad welding connections or uneven distribution of the material at the thermal interface)
- Drifting temperature behaviour (overcurrent, uneven load distribution)
- Balancing anomalies (sudden degradation or poorly calibrated BMS)
It’s clear that battery cloud monitoring is essential for electric vehicles. ACCURE is at the forefront of this transformation and is committed to developing pioneering AI and machine-driven software that ensures safe and reliable battery operations.
Anna Heising, Customer Success Manager ACCURE Battery Intelligence
