Solutions for achieving automation in logistics and retail

Enabling large-scale logistics automation transformation

Due to low space utilization and various inefficiencies in product transfer and transportation processes, the operational efficiency of a typical warehouse or distribution center is only between 80% and 85%. Moreover, a recent survey of warehouse automation operations managers reveals that only 20% of warehouses are currently automated. However, this percentage is expected to surge to over 90% by 2027, ushering in a significant wave of investment over the next five years.
 
This large-scale logistics automation transformation will rely on key applications such as asset tracking, machine vision, and object dimensioning. To ensure efficient product flow through the supply chain, advanced barcode scanners and handheld computers are essential. These devices must not only support an increasing number of advanced functions but also feature compact form factors while maintaining the convenience of battery power. These are the design challenges facing OEMs.
 
Logistics companies are increasingly focused on achieving ambitious sustainability goals. To reach these targets, companies are not only modernizing their fleets with electric vehicles but also increasing investments in end-to-end automation of the logistics supply chain. One critical element is implementing dimensioning of object, parcel, and pallet at the beginning of the logistics cycle. This information enables downstream processes to improve planning efficiency, thereby optimizing the utilization of delivery vehicles and cargo containers. Combined with in- cabin/container asset tracking, this enables end-to-end product tracking throughout the delivery process, helping to reduce errors caused by misplaced products while effectively enhancing logistics safety and security.

Automatic data capture scanners are key technology for automation transformation

Automatic Data Capture (ADC) is a key technology for enabling automation transformation. ADC devices range from simple barcode scanners to more complex handheld computers. Scanners are commonly used for logistics transportation, inventory tracking, order fulfillment, and product tracking in manufacturing. Although the tasks performed by these devices are relatively straightforward, the devices must be ruggedly designed to ensure safety and security while adapting to many different work environments. Key requirements for scanners include fast charging capability, which allows facilities to keep handheld devices running with fewer batteries and chargers, significantly reducing total capital investment and ensuring precise battery charging - since a full charge not only maximizes battery utilization but also reduces waste from unnecessary charge cycles.
 
Furthermore, edge node authentication must be improved. It is estimated that approximately 5% to 7% of batteries are clones. These cloned batteries can pose safety hazards during charging and discharging and cause operational downtime, leading to revenue loss. On the other hand, accidental drops can damage handheld devices. Integrating a high-g accelerometer can detect whether a device has been dropped and potentially damaged, making warranty protection particularly important for handheld devices.
 
Because automated environments are often noisy and chaotic, devices with audio functionality on the user interface require high-quality audio amplifiers for Dynamic Speaker Management (DSM) to enhance speaker output - maintaining fidelity sound quality from small form factor speakers while maximizing output power and reducing power consumption. Additionally, handheld devices with built-in automatic object dimensioning can sense objects and products, measure their dimensions, and provide critical information that significantly optimizes logistics transportation and improves downstream efficiency.

Smart battery gauges enable fast, accurate, and secure charging

For battery-powered handheld devices, a battery gauge is an important feature. Consider a warehouse operating 24/7. If a device's battery gauge has a 10% error, a battery that could last 8 hours would be marked as depleted after only 7.2 hours of operation, while the actual charge is not yet fully exhausted. Compared to a precise gauge, this would result in over 120 extra battery swaps per scanner each year. Accurate charging extends the operating time of handheld devices, makes full use of each battery's remaining capacity, and reduces the frequency of battery changes. In large warehouses with numerous devices, the cumulative benefit of this improvement is particularly significant, substantially lowering overall operating costs.
 
Fuel gauges can be implemented in two ways: host-side or battery-side. In a host-side system, a simple battery pack connects to a host charger, where the application processor in the host charger communicates with the fuel gauge IC connected to the host side. This architecture is suitable for systems with an embedded battery, or a removable battery with a relatively short service life (just a few years), as well as cost-sensitive applications. Conversely, in a battery-side system, the battery pack has a fuel gauge IC built-in. This architecture is suitable for removable battery systems with a long service life. By authenticating the battery when it is first mounted on handheld devices, this method also enables effective battery secure authentication.
 
Traditional fuel gauge methods are primarily based on coulomb counters (measuring charge and discharge current through a sense resistor to estimate charge flow), open-circuit voltage (OCV) measurement (estimating remaining charge - e.g., 4.2V correspond to 100% charge, 2.8V represent a depleted battery), or a combination of both. Each method has drawbacks. Coulomb counters accumulate offset over time, requiring error reset when the battery is fully discharged or under no load. Voltage gauge devices rely on the battery's open-circuit voltage. However, typical battery discharge curves are flat, making OCV difficult to determine. Additionally, load conditions significantly impact accuracy. Furthermore, coulomb counters and voltage gauge devices do not inherently account for internal self-discharge, aging of the cell, or temperature - all factors that significantly affect the state of charge of a battery.

Advanced sensing technology using two independent algorithms

To improve accuracy, more advanced sensing technology is required. For example, Analog Devices' ModelGauge™ family provides precise fuel gauge data by using two separate algorithms to accurately assess the state of charge batteries: ModelGauge and ModelGauge m5.
 
ModelGauge expresses the state of charge as a percentage. This algorithm estimates OCV under load conditions without disconnecting the load. OCV is calculated using real-time simulation, taking battery voltage as an input and incorporating the battery's dynamic parameters. This method provides good accuracy at temperatures above 0°C.
 
ModelGauge m5 is a highly sophisticated algorithm that provides not only the state of charge but also absolute capacity (in mAh), time to empty, time to full, cell age, age forecast, and other detailed battery information. This algorithm measures voltage, current, and temperature, enabling accurate measurement under all operating conditions - including complex conditions like cold temperatures or high loads. This algorithm is suitable for both host-side and battery-side implementations.
 
ADI offers numerous fuel gauge devices with integrated protectors and authenticators, suitable for host-side (MAX1726x series) and battery-side designs (MAX17201/MAX17211 and MAX17300/MAX17310 with self-discharge detector). For larger 2S and higher batteries, both ModelGauge (MAX17049) and ModelGauge m5 (MAX17261/MAX17263) can be used with integrated chargers (linear: MAX17330/MAX17332 or buck: MAX77840/MAX77818) to provide a one-chip battery management system.
 
For devices requiring USB charging, ADI offers AccuCharge® technology, which leverages standard USB BC1.2 and more advanced USB-C Power Delivery (PD) new charging technologies to provide a complete signal chain for battery charging. For example, the MAX77757 and MAX77787 offer automatic Type-C and BC1.2 detection compliant with JEITA charging configurations. All configurations are set using resistors or digital input pins, with resistor settings prioritized to ensure proper boot even when the battery is depleted. All USB detection is built-in, and the architecture designed around these devices enables a firmware-free design process. The high integration of these devices results in smaller, more efficient end designs. For example, with improved thermal management, the system can charge faster and more efficiently, while the form factor is reduced by 34%, facilitating compact wearable designs.
 
For charging power beyond 15W, ADI offers USB-C PD systems combining the MAX77958 PD controller with AccuCharge technology-enabled chargers such as the MAX77985/MAX77986 (for 1-cell batteries) or the MAX77960/MAX77961 charger (for 2-cell and higher batteries). The MAX77958 PD controller provides fully compliant USB-C PD3.0 charger control, automatic cable orientation and power role detection, and an I2C master interface for controlling the charger.
 
The MAX77985/MAX77986 improve efficiency for USB-C PD battery-powered devices. Given that battery packs in handheld computers and mobile scanners are changed multiple times daily, fast charging means reduced downtime. Built-in high-efficiency, integrated controllers and chargers enable high-performance charging via USB-C PD. As a result, battery packs charge faster without increasing temperature, minimizing battery stress and significantly extending battery operating life. With these architectures, OEMs can improve efficiency for applications using single Lithium cell and multi-cell batteries (suitable for higher voltage use cases).
 
Preventing counterfeiting is an important consideration when designing battery-powered devices. Various industries require large quantities of high-value batteries, making batteries a lucrative target for counterfeiters. Counterfeit batteries are typically manufactured to lower standards, thus posing a higher risk of internal short circuits. Dangerous short circuits can cause thermal runaway, a chain reaction leading to smoke or fire incidents.
 
Smart battery fuel gauge circuits can alert the system to internal short circuits and disconnect the battery, thereby avoiding potential problems. Additionally, systems with battery-side smart fuel gauges can use the gauge to authenticate the battery. The battery and device share a secret key, allowing the battery to verify its authenticity to the device upon mounting. If the battery is determined to be unauthentic, the device can prevent operation and avoid potential safety issues associated with using counterfeit batteries.

Conclusion

Driven by the waves of digitalization and intelligentization, the logistics and retail industries are rapidly advancing toward a new phase of high automation and precise operations. By integrating sensing technology, edge computing, artificial intelligence, and real-time data analytics, not only can warehouse management efficiency and delivery accuracy be effectively improved, but inventory structure can also be optimized and customer experience enhanced. Looking ahead, as the connectivity capabilities of automation equipment and systems continue to evolve, enterprises will be able to build more flexible and resilient supply chain systems, maintain competitive advantages when facing changing market demands, and truly realize an efficient, low-cost, and sustainable intelligent logistics and retail ecosystem.
 
ADI offers a wide range of high-precision battery gauge devices with additional battery protection and authentication features, utilizing a 160-bit key for SHA-256 secure authentication to prevent battery cloning. The fuel gauge IC can be programmed in the factory with a secure key before being shipped to battery manufacturers for final battery pack assembly. These solutions will be key enablers for achieving logistics and retail automation.