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Die letzte Verteidigungslinie: Software-Validierung und -Tests in der E-Mobility-Landschaft
by Vasantharaj G
Electric vehicle in motion with a digital interface, symbolising software validation and testing for e-mobility by Acsia.
Comprehensive software validation and testing ensure the safety and reliability of electric vehicles by Acsia.

In Kürze

  • Elektrofahrzeuge (EVs) sind softwaredefiniert, so dass strenge Tests und Validierungen für die Sicherheit, Zuverlässigkeit und Leistung entscheidend sind.
  • Die Softwarevalidierung geht über einfache Tests hinaus und stellt sicher, dass EV-Software in realen Szenarien einwandfrei funktioniert und die Erwartungen der Benutzer erfüllt.
  • Acsia verfolgt bei der Validierung einen mehrschichtigen Ansatz, der Simulation, Hardware-in-the-Loop (HIL)-Tests und Validierung in der Praxis kombiniert, um robuste und zuverlässige EV-Softwarelösungen zu liefern.

Elektrofahrzeuge (EVs) stehen an der Spitze der digitalen Transformation der Automobilindustrie. Als technischer Architekt für die Automobilindustrie kenne ich die Komplexität dieser Fahrzeuge, die im Wesentlichen rollende Softwareplattformen sind. Vom Batteriemanagement und der Motorsteuerung bis hin zu fortschrittlichen Fahrerassistenzsystemen (ADAS) und Benutzeroberflächen – Software ist das Rückgrat moderner Elektrofahrzeuge.

Mit dieser zunehmenden Abhängigkeit von Software steigt auch der Bedarf an rigorosen Tests und Validierungen. Es reicht nicht mehr aus, nur sicherzustellen, dass einzelne Softwarekomponenten isoliert korrekt funktionieren. Wir müssen garantieren, dass das gesamte System, das aus einer Vielzahl miteinander verbundener Komponenten besteht, in realen Szenarien einwandfrei funktioniert. An dieser Stelle wird die Softwarevalidierung zur letzten Verteidigungslinie, um die Sicherheit, Zuverlässigkeit und Leistung von Elektrofahrzeugen zu gewährleisten.

Jenseits der Verifizierung: Die Essenz der Validierung

Während sich das Testen von Software darauf konzentriert, zu überprüfen, ob der Softwarecode die festgelegten Anforderungen erfüllt, geht die Validierung noch einen Schritt weiter. Dabei wird die Leistung der Software unter realen Bedingungen bewertet, indem verschiedene Fahrszenarien, Umweltfaktoren und Benutzerinteraktionen simuliert werden. Dieser umfassende Ansatz stellt sicher, dass die EV-Software nicht nur wie vorgesehen funktioniert, sondern auch die Erwartungen und Bedürfnisse von Fahrern und Passagieren erfüllt.

Das Validierungs-Toolkit: Ein vielschichtiger Ansatz

Die Softwarevalidierung für EVs umfasst eine Kombination von Techniken und Tools, die alle eine entscheidende Rolle bei der Gewährleistung der Gesamtqualität und Sicherheit des Endprodukts spielen:

  • Simulation: Virtuelle Simulationen ermöglichen das Testen von Software unter einer Vielzahl von Bedingungen, einschließlich extremer Temperaturen, Gefahren im Straßenverkehr und unerwarteter Ereignisse. Dies beschleunigt den Entwicklungsprozess und reduziert den Bedarf an kostspieligen physischen Prototypen.
  • Hardware-in-the-Loop (HIL)-Tests: HIL-Tests heben die Simulation auf die nächste Stufe, indem echte Hardwarekomponenten in die simulierte Umgebung integriert werden. So können Sie die Interaktion der Software mit physischen Sensoren, Aktoren und anderer Hardware testen und Kompatibilität und Leistung sicherstellen.
  • Vehicle-in-the-Loop (VIL)-Tests: Bei VIL-Tests wird die Software an einem echten Fahrzeug in einer kontrollierten Umgebung getestet, z.B. auf einer Teststrecke oder einem Prüfgelände. Dies ermöglicht eine Echtzeitbewertung der Leistung der Software unter realistischen Fahrbedingungen.
  • Feldtest: Bei den Feldtests wird die Software in realen Fahrzeugen eingesetzt und es werden Daten zu ihrer Leistung, Zuverlässigkeit und Benutzerfreundlichkeit gesammelt. Dies ist der letzte Schritt im Validierungsprozess und liefert unschätzbares Feedback für weitere Verbesserungen.

Acsia: EV-Software-Validierung meistern

Wir bei Acsia wissen um die entscheidende Bedeutung der Software-Validierung in der E-Mobilitätslandschaft. Unser Team aus erfahrenen Ingenieuren und Testern verfolgt einen mehrschichtigen Ansatz zur Validierung, bei dem fortschrittliche Simulationstools, HIL-Testumgebungen und reale Validierungsmethoden zum Einsatz kommen, um die Robustheit und Zuverlässigkeit unserer EV-Softwarelösungen sicherzustellen.

Wir halten uns an die höchsten Industriestandards, einschließlich ISO 26262 für funktionale Sicherheit, um sicherzustellen, dass unsere Software die strengen Sicherheitsanforderungen erfüllt. Außerdem arbeiten wir eng mit unseren Kunden zusammen, um ihre individuellen Bedürfnisse zu verstehen und unsere Validierungsprozesse entsprechend anzupassen.

Da die Revolution der Elektromobilität immer schneller voranschreitet, wird die Software-Validierung ein entscheidender Faktor für die Sicherheit und den Erfolg von Elektrofahrzeugen bleiben. Wir bei Acsia haben uns verpflichtet, umfassende Validierungsdienste anzubieten, die unseren Kunden Vertrauen einflößen und sie in die Lage versetzen, qualitativ hochwertige EV-Lösungen zu liefern.

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AH2025/PS06 | AI/ML

Context

Continuous employee learning is essential for companies to stay competitive in a fast-changing business environment. Organizations adopt Learning Management Systems (LMS) to upskill employees, meet compliance requirements, and support career growth. However, existing LMS platforms often act as content repositories rather than personalized learning assistants.

 

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Develop an AI-powered LMS that goes beyond course hosting, by:

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Goal

Create a smart, data-driven LMS that improves employee engagement, learning outcomes, and workforce readiness while giving leadership clear visibility into training impact.

 

Outputs

  • Personalized learning recommendations for each employee.
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  • Training ROI insights linked to productivity and career growth.

 

Impact

  • Employees gain relevant, career-aligned skills faster.
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AH2025/PS05 | AI/ML

Context

Continuous employee learning is essential for companies to stay competitive in a fast-changing business environment. Organizations adopt Learning Management Systems (LMS) to upskill employees, meet compliance requirements, and support career growth. However, existing LMS platforms often act as content repositories rather than personalized learning assistants.

Pain Point

  • Employees are overwhelmed by generic training content and struggle to find relevant courses.
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Challenge

Develop an AI-powered LMS that goes beyond course hosting, by:

  • Mapping employee skills, roles, and career paths to relevant training modules.
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  • Enabling employees to learn flexibly, with adaptive learning paths based on performance.

Goal

Create a smart, data-driven LMS that improves employee engagement, learning outcomes, and workforce readiness while giving leadership clear visibility into training impact.

Outputs

  • Personalized learning recommendations for each employee.
  • Skill gap dashboards for managers and HR.
  • Learning progress analytics with completion, performance, and adoption rates.
  • Training ROI insights linked to productivity and career growth.

Impact

  • Employees gain relevant, career-aligned skills faster.
  • Managers can strategically deploy talent based on verified skills.
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AH2025/PS04 | AI/ML

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Software teams struggle to diagnose system failures from massive log files. Manual analysis is slow, error-prone, and requires expert knowledge. Root cause extraction from unstructured, noisy logs. Use creative algorithms, LLM prompting strategies, or hybrid heuristics.

Pain Point

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Build an AI-powered log analytics assistant that can:

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Goal

Deliver a working prototype that:

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Outputs

  • Automated defect detection (flagging anomalies in logs).
  • Root cause summaries in natural language.
  • Actionable recommendations (e.g., suspected component failure, probable misconfiguration).
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Impact

  • Reduced time to diagnose failures, lowering downtime and maintenance costs.
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AH2025/PS03 | AI/ML

Context

Drivers and passengers spend significant time in vehicles where comfort, safety, and accessibility directly affect satisfaction and well-being. Yet today’s in-car systems remain largely static and manual, requiring users to adjust climate, seats, infotainment, and navigation themselves. With increasing connectivity, AI offers the potential to transform cars into adaptive, intelligent companions.

Pain Point

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  • Accessibility gaps (e.g., for elderly passengers or those with hearing/visual impairments) remain unaddressed.

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Build a Generative AI-powered cockpit agent that dynamically personalizes the in-car experience based on contextual data such as:

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  • Accessibility needs (visual/hearing impairments, elderly passengers).

Goal

Deliver real-time, adaptive personalization of:

  • Comfort settings: AC, seat adjustments, lighting.
  • Infotainment: music, podcasts, news.
  • Navigation guidance: route optimization based on urgency, preferences, and accessibility.

Outputs

  • Dynamic in-car assistant that responds to context in real-time.
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Impact

  • Safer driving experience with fewer distractions.
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AH2025/PS02 | AI/ML

Context

Automotive software development is highly complex, involving multiple tools (Jira, GitHub, MS Teams, Confluence), distributed teams, and strict compliance standards (ISO 26262, ASPICE). Project managers must continuously monitor tasks, track resources, and identify risks. However, the sheer volume of data across tools makes real-time visibility and decision-making difficult.

Pain Point

  • Project managers waste time manually consolidating data from Jira, GitHub, and communication platforms.
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  • Lack of predictive insights leads to reactive, rather than proactive, project management.

Challenge

Build an AI-powered project management assistant that can:

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  • Deliver natural language summaries for managers and stakeholders.

Goal

Enable project managers to see the full picture instantly, automate reporting, and take data-driven decisions on resources and risks without manual effort.

Outputs

  • Automated project dashboards (progress, backlog, velocity, open PRs/issues).
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  • Risk prediction engine (e.g., “Module X likely delayed by 2 weeks due to dependency on Y”).
  • AI-generated summaries (daily/weekly status reports in plain language).

Impact

  • Reduced management overhead → fewer hours wasted on reporting.
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AH2025/PS01 | AI/ML

Context

In modern organizations, assembling the right project team is critical to success. Managers must balance skills, experience, cost, availability, and domain expertise, but decisions are often made using intuition or partial information. This leads to suboptimal teams, missed deadlines, or budget overruns.

Pain Point

  • Team formation today is time-consuming and heavily manual, requiring managers to cross-check spreadsheets, HR databases, and project needs.
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  • Traditional staffing tools focus on availability but fail to optimize across multi-dimensional constraints (skills, budget, past project fit, timeline).

Challenge

Build a Generative AI assistant that takes as input:

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Goal

Enable managers to form the best-fit, economically feasible project teams in minutes, rather than days, while providing transparency into why each recommendation was made.

Outputs

  • Optimal team composition: Recommended employees, with justification.
  • Economic feasibility analysis: Skill coverage vs cost vs timeline.
  • Alternative team recommendations: Trade-off scenarios (e.g., lower cost, faster delivery, more experienced).

Impact

  • Faster project staffing → quicker project kick-offs.
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