Trends and Developments in Renewable Energy
How artificial intelligence, machine learning, and the Internet of Things are shaping the clean energy of the future
Modern, environmentally friendly energy alternatives—such as wind, solar, hydroelectric, and tidal power—are developing rapidly and constantly present engineers with new challenges. The pressure to create long-term, low-emission alternatives to gas and coal-fired power is growing. Financial considerations, policy measures, tax incentives, rising rates, and market fluctuations are affecting both businesses and consumers alike. To successfully compete with established non-renewable options, green energy technologies require a high level of performance and efficiency. Furthermore, improved battery capabilities and the use of AI (including machine learning) offer promising potential.
Lithium-ion batteries as an innovation in battery technology
The overall capacity and reliability of green energy must match—or at least be comparable to—that of oil and coal in order for it to be an attractive alternative. The long-standing challenges involved in achieving this goal are currently the primary technological and policy priorities for industry stakeholders, and slowly but surely, many remarkable successes and advances are being made. For instance, residential solar panels have been available since the 1970s, yet only recently has solar generation become the fastest-growing source of new megawatt capacity within the renewable sector. Early silicon photovoltaic cells were cost-prohibitive to manufacture, and battery technology was still in its infancy. Storing energy generated during peak periods was inefficient, leading to shortages during off-peak hours at night or on cloudy days. The requirement to store energy at high current rates while minimizing losses has driven significant advances in battery technology in recent years. In recent years, liquid and solid-state lithium-ion batteries in particular have developed much more significantly than the lead-acid batteries previously in common use. The higher purchase costs are now justified by the higher energy density combined with smaller size, lower weight, and longer service life.
Optimizing Energy Storage Systems and Extending the Lifespan of State-of-the-Art Batteries
The transition to lithium-ion technology and the electrification of control functions through battery management systems are key to fully realizing the potential of solar energy and other renewable options. Within a battery management system, adjustments can be made to the circuit board, which in turn can enhance the overall functionality of a specific unit. The battery management system acts as the "brain" of the battery by prioritizing electrical loads and controlling the flow of power. Regulating power intake and output in a peak production system preserves the battery’s long-term lifespan and prevents the loss of high-speed power capacity.
One way to mitigate damage is through precise monitoring of the battery’s energy level. The connectors in the battery management system’s circuit boards are an essential component of the signal transmission required for real-time communication of active battery levels. Consistent and accurate monitoring prevents the loss of signals, energy levels, and the battery’s own ability to store energy. Monitoring the battery status via sensors can prevent the battery from discharging too deeply and potentially causing internal damage. Active battery management monitoring systems can also prevent the use of maximum high-current rates over extended periods, which over time leads to a small but steady loss of the device’s performance. This principle also governs high-intake or high-speed charging: active communication within the management system enables the battery unit to regulate the charge and utilize high-speed charging only during optimal periods of energy generation. In a solar application, for instance, the system can be configured to maintain high current draw capacity only during peak daylight hours. Conversely, rapid discharge of the battery results in a rapid output current that can also lead to performance loss over time; this opposing source of damage can likewise be mitigated by an optimized, intelligently designed battery management system.
One way to mitigate damage is through precise monitoring of the battery’s energy level. The connectors in the battery management system’s circuit boards are an essential component of the signal transmission required for real-time communication of active battery levels. Consistent and accurate monitoring prevents the loss of signals, energy levels, and the battery’s own ability to store energy. Monitoring the battery status via sensors can prevent the battery from discharging too deeply and potentially causing internal damage. Active battery management monitoring systems can also prevent the use of maximum high-current rates over extended periods, which over time leads to a small but steady loss of the device’s performance. This principle also governs high-intake or high-speed charging: active communication within the management system enables the battery unit to regulate the charge and utilize high-speed charging only during optimal periods of energy generation. In a solar application, for instance, the system can be configured to maintain high current draw capacity only during peak daylight hours. Conversely, rapid discharge of the battery results in a rapid output current that can also lead to performance loss over time; this opposing source of damage can likewise be mitigated by an optimized, intelligently designed battery management system.
Optimized contact system in the battery management system
SMT connectors that are both robust and prevent the loss of electrical signals are critical for battery regulation. To maximize a battery’s range and efficiency, a connector must maintain the signal even when the circuit board is in motion or under stress. A dual-contact connector is an excellent option for maximizing the detection capability of a battery status monitoring system. Unlike conventional contact systems with blade and spring contacts, the dual-contact system is gender-neutral (both blade and spring contacts are present on every socket and plug). A dual contact is established at each individual pin, ensuring maximum contact reliability for the signal.
The smooth, dual-contact area of the zero8 connector ensures a fast and secure connection (see Fig. 1). Internally, the zero8 connector also prevents the contacts from being inserted incorrectly during installation by immersing the contacts in the insulating body, thereby protecting them from damage.
The smooth, dual-contact area of the zero8 connector ensures a fast and secure connection (see Fig. 1). Internally, the zero8 connector also prevents the contacts from being inserted incorrectly during installation by immersing the contacts in the insulating body, thereby protecting them from damage.
There is no end in sight for the continued development of lithium-ion batteries. Consumers continue to demand that electric vehicles travel ever-greater distances on a single charge. The more charge and discharge cycles a battery undergoes, the more its performance degrades over time. However, a well-designed battery management system can mitigate damage and extend the battery’s lifespan.
Optimizing Energy Production Through Machine Learning
New technologies in batteries and their associated management systems are opening up new possibilities in the field of clean energy. The emergence of artificial intelligence, machine learning, and the Internet of Things (IoT) offers potential for improving the efficiency of devices.
The IoT encompasses vast amounts of data from millions of electronic devices. Artificial intelligence (AI) attempts to mimic human intelligence and creativity to perform specific tasks. Machine learning is a subset of the broader concept of AI and focuses on analyzing available IoT data, using AI capabilities to interpret it. Machine learning uses data to make the best possible decision without direct human intervention or instructions.
Machine learning has the potential to process vast amounts of incoming data and make decisions within a system in fractions of a second without human intervention. This can help make large-scale solar, wind, hydro, and tidal power plants feasible. For example, AI can process new IoT data on incoming weather conditions and then adjust technology for generating energy from hydropower or tidal power to take advantage of increasing water speeds or higher tides. Systems with machine learning can also be used to detect anomalies. For example, if an external source of interference comes into play, decisions can be made without a human being present to make system adjustments. In the event of bad weather or other random extreme conditions, machine learning can make the decision to temporarily halt energy production to protect critical hardware from overload and avoid costly system failures.
To cope with the challenges of external disturbances, a system must detect the problem and respond to it in a timely manner. The faster a machine learning system can communicate internally, the faster it can adapt. 25+ Gbps has not only been present in the IoT sector for some time but has also become the standard for data centers. Yet the technology is far from reaching its limits. Even the requirements of PCIe 5.0 are now twice as high as those of its predecessor, PCIe 4.0, with a maximum data transfer rate of 32 GT/s (gigabit transfers per second). Selecting a reliable, high-speed connector is therefore the optimal choice for processing and interpreting IoT data at maximum speed within a green-energy solution.
The IoT encompasses vast amounts of data from millions of electronic devices. Artificial intelligence (AI) attempts to mimic human intelligence and creativity to perform specific tasks. Machine learning is a subset of the broader concept of AI and focuses on analyzing available IoT data, using AI capabilities to interpret it. Machine learning uses data to make the best possible decision without direct human intervention or instructions.
Machine learning has the potential to process vast amounts of incoming data and make decisions within a system in fractions of a second without human intervention. This can help make large-scale solar, wind, hydro, and tidal power plants feasible. For example, AI can process new IoT data on incoming weather conditions and then adjust technology for generating energy from hydropower or tidal power to take advantage of increasing water speeds or higher tides. Systems with machine learning can also be used to detect anomalies. For example, if an external source of interference comes into play, decisions can be made without a human being present to make system adjustments. In the event of bad weather or other random extreme conditions, machine learning can make the decision to temporarily halt energy production to protect critical hardware from overload and avoid costly system failures.
To cope with the challenges of external disturbances, a system must detect the problem and respond to it in a timely manner. The faster a machine learning system can communicate internally, the faster it can adapt. 25+ Gbps has not only been present in the IoT sector for some time but has also become the standard for data centers. Yet the technology is far from reaching its limits. Even the requirements of PCIe 5.0 are now twice as high as those of its predecessor, PCIe 4.0, with a maximum data transfer rate of 32 GT/s (gigabit transfers per second). Selecting a reliable, high-speed connector is therefore the optimal choice for processing and interpreting IoT data at maximum speed within a green-energy solution.
Miniaturization remains the dominant technical trend across many industries, and ept’s “Colibri” high-speed connector assembly is ideal for data stations, data storage, and other capabilities required for IoT, IIoT, and machine learning, as well as for power grids and microgrids for energy storage.
As reliable solar, wind, hydro, and geothermal technologies mature, new products demand more powerful management tools. Connectors have proven critical to the success of state-of-the-art battery management systems and to the optimization of machine learning. The clean energy sector still has several steps to take, and high-performance PCB design makes advanced electrification requirements—such as rapid machine learning responses and optimal battery technology—feasible enough to compete with outdated gas and coal.
As reliable solar, wind, hydro, and geothermal technologies mature, new products demand more powerful management tools. Connectors have proven critical to the success of state-of-the-art battery management systems and to the optimization of machine learning. The clean energy sector still has several steps to take, and high-performance PCB design makes advanced electrification requirements—such as rapid machine learning responses and optimal battery technology—feasible enough to compete with outdated gas and coal.
Interested?
Then choose your European-made connector right away:
product list
Noch Fragen?Dann kontaktieren Sie uns!
Zum Kontaktformular