In most cases, it is actually sufficient to look at the ECU’s inputs and outputs to functionally test a component (Figure 1). However, this becomes difficult when state machines are used in the ECU. Their current states can only be derived indirectly by their effects at the ECU’s outputs. In the case of sensors whose values are not transmitted over the network system, it is also very difficult for the test engineer to localize errors to the software interface. From outside the ECU, it is not clear exactly where the sensor value was incorrectly processed.

Different methods that offer access to internal ECU data are used, depending on the phase of ECU development. In early phases, for example, internal ECU values are often output in so-called “reserved development messages” (Figure 1). For the functional developer at a supplier, this is an effective and quick method that precisely targets a specific objective. However, these supplemental messages must be removed for later development phases, especially for system integration and series production. They induce additional bus load, and in the worst case they might even collide with messages of other system components. Another way to access internal values is through diagnostics (Figure 1). Some information is available directly via diagnostics, e.g. diagnostics offers access to fault memory. Special diagnostic services are also provided to read the required values from memory. The advantage here is that a standardized access method is used. The only precondition is full integration of the diagnostic driver; this is generally provided in today’s ECUs. The disadvantage of this method is that a lot of unnecessary diagnostic protocol information is transmitted along with the actual measured values, and this adds load to the network system interface. A data flow analysis of many values is not possible, especially since the measured values do not contain time stamp information.

XCP For Test Access

If network interface load needs to be kept low, an alternative is to use a calibration protocol. Originally, such protocols were developed for the ECU calibrator. They let calibrators modify parameters or characteristic maps in the ECU to optimize their algorithms. With the XCP protocol standardized by ASAM, the user can read individual values directly from the ECU as needed. The protocol can also periodically supply a defined set of measured values from the ECU via so-called Data Acquisition (DAQ) lists. The XCP protocol was defined for efficient provision of data over the network medium. As an example, after configuration the DAQ lists can be transmitted in response to a single identifier from the test system. In addition, measurement times of the DAQ lists can be synchronized to internal ECU processes. Automated test systems place similar requirements on the system. Use of the XCP protocol makes it possible to integrate internal values in test sequences without excessive loading of the ECU or the network system used. Another reason that a widely used standard like XCP is ideal is that it is very easy to configure in the tool chain. All necessary information is already in the A2L file such as internal program memory locations with their names and communication parameters. Depending on the development environment, the A2L file is either automatically generated, or it may need to be generated in a separate step from the linker-map information. In the test tool, the user only has to configure this file once for each ECU used in the test. In a second step, the user selects the symbols needed for the test sequences from the A2L file.

CANoe Option .AMD/XCP

Option .AMD/XCP supplements the CANoe test tool from Vector with the convenient option of reading and writing internal ECU values. Besides supporting the XCP standard, it also supports the previous protocol CCP. Once the A2L file has been configured and the necessary values selected, CANoe automatically acquires them and maps them as system variables. The user can then use these variables in any of the testing tasks. Besides offering access to ECU inputs and outputs, they also provide an in-depth look into the ECU’s memory (Figure 2).

In simple analysis tasks, users can display the data in the Trace or Graphic Window and use panels to evaluate the results. For more complex test sequences, CANoe’s Test Feature Set offers extensive options for creating test cases and automatically evaluating them. For example, this enables checking of the Network Management state machine for correct functionality. The necessary stimulation is performed in the CANoe rest-of-bus simulation, and the ECU’s reaction is not just measurable on the network; it is directly measurable in the ECU over XCP. The effort required to execute test cases is also significantly reduced, e.g. for test cases that require sensors. The test system writes the sensor values directly to memory cells in the ECU over XCP. This eliminates the need to connect and control original sensors at the ECU inputs – a demanding task. The ECU is notified that the sensor and associated hardware driver have measured the values correctly. The same approach can be used in the other direction. Here it is assumed that the output stage and actuator have been tested and accepted. In this case, the test system measures the value that the application prescribes to the driver stage over XCP.

Access With Large Quantities Of Data

If large quantities of data need to be exchanged between the test system and the ECU in a test case, or if especially quick processes need to be monitored, an XCP connection over a CAN network is no longer effective. In such cases, direct access to the ECU’s debug interfaces is recommended. This could be implemented via a NEXUS or JTAG interface, for example. These protocols directly access the ECU memory − partly without load on the microcontroller. Taking this approach, the user can quickly read out very large quantities of data from the system without loading the network and the ECU.

Vector VX hardware, for example, offers direct access to an ECU’s NEXUS or JTAG interface (Figure 2). Since this hardware communicates with the test system via XCP-on-Ethernet, integration in CANoe is as easy as integration for XCP access over CAN. Combining VX hardware with the CANoe test system further improves test system performance, without any negative effects on the communication medium. (MT)

NB: Oliver Falkner is group leader at Vector in product management of the Networks and Distributed Systems product line. Views expressed are personal.

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Detroit Engineered Products (DEP) has introduced DEP AIWorks, an engineering platform designed to integrate machine learning with physics-based simulation. The launch follows the conclusion of a five-city industry conclave held across Bengaluru, Delhi NCR, Hyderabad, Pune and Chennai.

DEP AIWorks is built as a physics-agnostic and tool-agnostic environment, allowing it to function across various datasets and engineering domains. The platform combines neural networks and physics-informed models with computer-aided engineering (CAE) solvers to provide predictive and generative capabilities within the product development lifecycle.

Core features of the platform include modular architecture, operational speed and ecosystem compatibility.

The platform is intended for use in the automotive, aerospace, energy, manufacturing and telecommunications sectors. It supports various stages of development, from early design exploration to manufacturing validation. By utilising data-driven learning alongside physics-based validation, the system aims to improve engineering productivity and accelerate decision-making cycles.

Radha Krishnan, President & Founder, DEP, said, “DEP AIWorks reflects the next step in how engineering organisations will adopt AI, not as a standalone tool, but as an integrated part of the product development lifecycle. By combining decades of simulation expertise with advances in AI, we are enabling teams to move faster while maintaining engineering rigor and reliability.”

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German tier 1 supplier ZF has introduced SolarBoost, a retrofittable solar panel system designed to support the 24-volt on-board electrical systems of city buses and coaches. The technology generates electricity during vehicle operation to recharge batteries, intended to reduce fuel consumption and maintenance requirements for fleet operators.

The system reduces the load on the drive engine by providing an alternative power source for on-board systems, which are traditionally supplied by the alternator. According to ZF, the additional energy can reduce fuel consumption by up to 3.5 percent, depending on weather conditions and application profiles.

The company states that key benefits for operators include battery longevity, as continuous recharging extends battery life. ZF reports potential savings equivalent to one battery per vehicle per year.

Furthermore, it enhances uptime by reduced requirement for stationary battery recharges and lower maintenance frequency. The system includes Bluetooth connectivity, allowing operators to track energy generation in real-time via a mobile application.

SolarBoost utilises a plug-and-play architecture designed for installation in an operator's own workshop using standard tools. The process does not require drilling into the vehicle structure or extensive rewiring, allowing for fleet-wide scaling with minimal disruption to service.

The hardware is engineered to withstand vibrations and weather conditions associated with heavy-duty transit. ZF provides a 5-year warranty and repair kits to support the long-term durability of the flexible panels.

The product is positioned as a scalable solution for bus operators to meet environmental targets. By utilizing renewable energy for electrical loads, the system assists in reducing the carbon footprint of intercity and urban transport fleets. It aligns with ZF’s broader strategy to deliver innovations that improve vehicle efficiency while supporting climate-friendly mobility.

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Recyclekaro, an e-waste and lithium-ion battery recycling firm, has been cleared for eligibility under the Incentive Scheme for Promotion of Critical Mineral Recycling. The scheme is administered by the Ministry of Mines under the National Critical Minerals Mission.

The company has committed an investment of approximately INR 3 billion to expand its operations. This brownfield expansion aims to increase total processing capacity to 50,000 metric tonnes.

Its targeted waste streams for mineral recovery include spent lithium-ion batteries, electronic circuit e-waste, rare earth magnets and spent catalytic converters.

The project is designed to increase the domestic recovery of lithium and rare earth elements, reducing reliance on mineral imports for the electric mobility and renewable energy sectors.

Recyclekaro plans to invest over INR 5 billion over the next five years into a research and development facility. This centre will focus on technologies for the recovery of rare earth and critical minerals. The objective of the expansion is to align with national resource security and circular economy targets.

Rajesh Gupta, Founder and Managing Director, Recyclekaro, said, “We are proud to have secured eligibility under the Government of India’s Critical Mineral Recycling Incentive Scheme and sincerely commend the Ministry of Mines for instituting a visionary and robust framework under the National Critical Minerals Mission. This marks a decisive step toward strengthening India’s energy security that relies on securing critical minerals domestically. This will support India’s net zero goals. Over the past 15 years, we have built world-class in-house technologies, conducted thousands of pilot-scale experiments, and are now investing over INR 5 billion next 5 years in our newly developed R&D facility. It is going to be amongst the biggest privately owned facilities in India dedicated to rare earth and critical mineral recovery. At Recyclekaro, we remain deeply committed to this national movement and invite researchers, innovators, and technology partners to collaborate in accelerating India’s clean energy and circular economy transition.”

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Kochi-headquartered deep-tech company RoshAi has raised INR 220 million in funding, which was led by IAN Alpha Fund, part of the IAN Group.

The capital is designated for product development, expansion of deployments and scaling operations across international industrial markets.

RoshAi develops autonomy solutions that can be retrofitted to existing heavy vehicles in sectors such as ports, mining and logistics. This approach allows operators to implement driverless operations without the requirement for new fleet investments.

The technology stack comprises three primary components:

Retrofit Hardware: Physical kits to enable autonomous control of conventional vehicles.

In-Vehicle Autonomy System: AI-powered software and sensors for navigation and obstacle detection.

Cloud-Based Fleet Management: A platform for remote monitoring and operational coordination.

The company reports that its systems have completed over 100,000 km of testing with no safety incidents.

The global industrial autonomous vehicle market is projected to reach USD 162.8 billion by 2030, up from USD 47.6 billion in 2024. RoshAi aims to capture this growth by targeting the United States, Australia and Southeast Asia. It currently collaborates with Tier 1 original equipment manufacturers (OEMs) and industrial operators on pilot projects.

Sarika Saxena, Managing Partner, IAN Alpha Fund, said, “RoshAi is solving industrial autonomy through a retrofit-first approach, enabling operators to upgrade existing fleets rather than invest in new infrastructure. With strong early validation, repeat customer engagement, and a scalable autonomy platform, the company is well-positioned to build a globally relevant deep-tech business from India.”

Roshy John, Founder & CEO, RoshAi, added, “Our focus is to make industrial operations safer and more efficient by enabling existing fleets to operate autonomously. This investment allows us to accelerate product development, scale deployments across global markets, and continue building a robust autonomy platform for industrial use cases. We are glad to have IAN’s support as we move into this next phase.”

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