e-Mobility

Driving the digital transformation of green transportation.

The electric vehicle (EV) industry is on a fast track, fuelled by sustainability concerns and government incentives. Rapid advancements in battery technology, charging infrastructure, and car-to-infrastructure communication have contributed to this explosive growth.
The software sits at the heart of this transformation, critically optimizing performance, range, and safety for EVs. It enhances battery usage for extended range and efficient power delivery, enables seamless integration of ADAS (Advanced Driver Assistance System) features and real-time data processing for a safer driving experience, and facilitates vehicle-to-grid (V2G) communication for smarter energy management.

How Acsia Can Help?

Knowledge across consulting, design, and development with various AUTOSAR stacks and micro-controllers.

  • Consulting Services
    • Impact Assessment
    • Adoption Framework
  • Platform Services: Leverage wide experience in various Classic AUTOSAR platforms and automotive micro-controllers for efficient EV development.
    • MCAL/CDD Development
    • Standard Development
    • Standardization of Existing CDDs for AUTOSAR
    • Porting, Migration, Configuration, and Support
    • Classic AUTOSAR Stack Vendors: Vector Informatik (DaVinci Configurator Pro/DaVinci Developer), Elektrobit (tresos Studio), AVIN Systems, Dassault Systèmes (AUTOSAR Builder)
    • Micro-controllers: Renesas, Infineon, NXP, ST, and Cypres
  • Application Services: Portable application component development.
    • Custom SWC development
    • Model-Based Design & Development

Expertise in establishing continuous integration pipelines, test automation infrastructure, undertaking software to EV level testing, and SIL/MIL/HIL simulation and testing.

  • Integration Services
    • SWC (Software Component) + BSW (Basic Software) + MCAL (Micro-Controller Abstraction Layer)
    • Continuous Integration
    • Automated Integration Testing
  • Verification & Validation
    • Acceptance Testing Framework
    • Test Automation
    • Specific Testing (OEM/Tier-I)

Proven competence in ensuring strict adherence to industry accepted standards.

  • Develop FuSa designs and work products for EVs adhering to ISO 26262 and Automotive SPICE® L2.
  • Develop robust security design, implement software requirements and undertake testing to protect connected EVs from cyber threats.

Suite of cloud-enabled automotive industry-specific services.

  • Seamless Over-the-Air (OTA) Updates: Enable secure OTA software updates for continuous improvement of EV functionality.
  • Connected EV Solutions: Facilitate seamless car-to-infrastructure (V2X) communication and cloud integration for enhanced safety and traffic management.
  • XACT Charge Pro: A white labelled SaaS product for smart EV charging station management.

Project Highlights

  1. Developed a fail-safe DC-DC power conversion system tailored for an autonomous vehicle platform.
  2. With our partner ecosystem, desiged a robust and reliable power supply architecture capable of managing critical loads and ensuring uninterrupted operation of safety-critical systems, even amidst power fluctuations or failures.
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Design, development and testing of Cybersecurity base and application SW components for a smart gateway system with CAN message authentication.

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Hear From Experts
Stefan Juraschek
Former VP R&D (Electrical & Electronics), BMW Group
Strategic Advisor, Acsia
36+ years in automotive software and systems
“The global EV industry is booming driven by tech advancements, government support, and growing eco-consciousness. Acsia brings proven expertise in AUTOSAR consulting and services, ECU software development, and verification & validation. Our strong track record partnering with leading Tier-I suppliers and proven experience working on automotive microcontrollers by leading developers, position us perfectly for the evolving EV landscape.”
OEM Production Program Experience:
Why Acsia?
Deep e-Mobility Expertise
A proven track record in developing ECU software solutions for EVs across battery management units, power conversion units (PCU), and onboard charger.
AUTOSAR and Functional Safety Certifications
Expertise in AUTOSAR, ISO 26262, and ASPICE ensures compliance, reliability, and safety-critical software development for EVs
Strong Tier-I Relationships
Proven experience working with Vector Informatik (DaVinci Configurator Pro/DaVinci Developer), Elektrobit, AVIN Systems, and Dassault Systèmes.
MCAL Services
Deep expertise working on automotive micro-controllers and SoCs developed by Renesas, Infineon, NXP, ST, and Cypress.
OTA Update Solutions
Expertise in secure OTA updates allows for continuous improvement of EV software, bug fixes, and feature rollouts, enhancing customer experience and vehicle longevity.
BMS Development
Advanced BMS software development ensures efficient battery utilization, maximizes range, and provides accurate diagnostics for improved battery health.
Cybersecurity Expertise
A strong understanding of cybersecurity is crucial for protecting connected EVs from potential hacking threats.
State of the art Labs
Intensive investments in world-class labs to support OEM and Tier-I production programs.
Accelerators
Best practices and accelerators curated from multiple programs providing battery models, BMS software and hardware reference designs, battery twins, and modular, scalable solutions.
A proven track record in developing ECU software solutions for EVs across battery management units, power conversion units (PCU), and onboard charger.
Expertise in AUTOSAR, ISO 26262, and ASPICE ensures compliance, reliability, and safety-critical software development for EVs
Proven experience working with Vector Informatik (DaVinci Configurator Pro/DaVinci Developer), Elektrobit, AVIN Systems, and Dassault Systèmes.
Deep expertise working on automotive micro-controllers and SoCs developed by Renesas, Infineon, NXP, ST, and Cypress.
Expertise in secure OTA updates allows for continuous improvement of EV software, bug fixes, and feature rollouts, enhancing customer experience and vehicle longevity.
Advanced BMS software development ensures efficient battery utilization, maximizes range, and provides accurate diagnostics for improved battery health.
A strong understanding of cybersecurity is crucial for protecting connected EVs from potential hacking threats.
Intensive investments in world-class labs to support OEM and Tier-I production programs.
Best practices and accelerators curated from multiple programs providing battery models, BMS software and hardware reference designs, battery twins, and modular, scalable solutions.
What’s In It For You

Leverage Acsia’s expertise in AUTOSAR, ECU development, and pre-built EV software components to bring your EVs to market faster.

Benefit from Acsia’s expertise in battery management software and MCAL development services to ensure optimum battery utilization and powertrain performance, and maximize EV range.

Acsia’s optimized processes, global talent pool, and deep understanding of EV software challenges can significantly reduce costs and mitigate development risks.

Stay ahead of the curve with Acsia’s commitment to AUTOSAR compliance and secure Over-the-Air (OTA) updates, allowing your EVs to adapt to evolving technologies and customer needs.

Acsia’s expertise allows you to combine e-mobility solutions with ADAS, fostering a safer and more comfortable driving experience, and ensuring compliance with FuSa standards.

Utilize Acsia’s agile methodologies and global delivery model to scale your e-mobility software development efficiently, adapting to project complexities and evolving market demands.

Acsia’s mastery in ECU software development ensures efficient communication and management of critical EV components, leading to optimized performance and extended range.

Acsia’s expertise in OTA updates allows for continuous improvement of EV functionality and remote diagnostics, enhancing customer experience and vehicle uptime.

Frequently Asked Questions

With the automotive industry’s shift towards electric vehicles (or EVs), software has become essential for enhancing EV performance, optimizing energy efficiency, strengthening cybersecurity, and enabling smooth and intelligent vehicle connectivity. To stay competitive in the evolving e-Mobility landscape, companies require software that is reliable, scalable, and built for the future.

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Acsia develops advanced and reliable software for e-Mobility ECUs, battery management systems, power electronics, and connected vehicle solutions, ensuring efficiency, security, and advanced user experience in electric vehicles.

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Modern electric vehicles rely on smart, adaptive software for battery management, power electronics, autonomous driving, and over-the-air (OTA) updates. By using the latest software solutions, automotive OEMs and Tier-1 suppliers can enhance vehicle efficiency, safety, and user experience.

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Yes, Acsia offers solutions that enable EVs to communicate effectively with various charging stations, facilitating interoperability and efficient energy management across different platforms.

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XACT Charge Pro is a white-labelled Software-as-a-Service (SaaS) developed by Acsia, designed for smart electric vehicle (EV) charging station management. This solution facilitates seamless vehicle-to-infrastructure (V2X) communication and cloud integration, enhancing safety and traffic management.

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Acsia’s Battery Management System (BMS) software helps batteries last longer and store energy efficiently, maximizes range, and provides accurate diagnostics for improved battery health.

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  • A Fail-Safe DC-DC System for an Autonomous Vehicle Platform of a Renowned German OEM.
  • Software Design, Development and Testing of a Smart Gateway for a US OEM.
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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.

 

Pain Point

  • Employees are overwhelmed by generic training content and struggle to find relevant courses.
  • Managers lack visibility into skill gaps and training effectiveness.
  • Companies spend heavily on training programs without clear insights into ROI or business impact.
  • Current LMS solutions provide limited personalization and recommendations, leading to low engagement.

 

Challenge

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

  • Mapping employee skills, roles, and career paths to relevant training modules.
  • Using learning analytics to predict skill gaps and recommend personalized learning journeys.
  • Providing managers with team-level insights on training progress and skill readiness.
  • 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.
  • Organizations see higher training ROI and improved workforce agility.
  • Creates a culture of continuous learning, driving retention and innovation.
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.
  • Managers lack visibility into skill gaps and training effectiveness.
  • Companies spend heavily on training programs without clear insights into ROI or business impact.
  • Current LMS solutions provide limited personalization and recommendations, leading to low engagement.

Challenge

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

  • Mapping employee skills, roles, and career paths to relevant training modules.
  • Using learning analytics to predict skill gaps and recommend personalized learning journeys.
  • Providing managers with team-level insights on training progress and skill readiness.
  • 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.
  • Organizations see higher training ROI and improved workforce agility.
  • Creates a culture of continuous learning, driving retention and innovation.
AH2025/PS04 | AI/ML

Context

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

  • Manual log analysis is slow, error-prone, and requires deep expertise in both the system and its environment.
  • Critical issues can be missed or misdiagnosed, leading to longer downtimes and higher costs.
  • Existing monitoring tools often raise alerts without actionable insights, leaving developers to do the heavy lifting.

Challenge

Build an AI-powered log analytics assistant that can:

  • Ingest and parse unstructured application logs at scale.
  • Automatically flag potential defects or anomalies.
  • Summarize possible root causes in natural language.
  • Provide actionable insights that developers can use immediately.

Goal

Deliver a working prototype that:

  • Operates on sample log data.
  • Produces insights that are accurate, usable, and easy to interpret.
  • Bridges the gap between raw log data and developer-friendly diagnostics.

Outputs

  • Automated defect detection (flagging anomalies in logs).
  • Root cause summaries in natural language.
  • Actionable recommendations (e.g., suspected component failure, probable misconfiguration).
  • Visualization/dashboard (if possible) for quick triage.

Impact

  • Reduced time to diagnose failures, lowering downtime and maintenance costs.
  • Increased developer productivity, freeing engineers to focus on fixes rather than sifting logs.
  • Improved reliability of complex software systems.
  • Scalable approach that can be extended across industries (finance, automotive, telecom, healthcare).
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

  • Current in-car experiences are one-size-fits-all, failing to account for individual preferences or needs.
  • Manual adjustments while driving can be distracting and unsafe.
  • Accessibility gaps (e.g., for elderly passengers or those with hearing/visual impairments) remain unaddressed.

Challenge

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

  • Driver profile (age, preferences, past behaviour).
  • Calendar & journey type (work commute, leisure trip, urgent travel).
  • Mood (estimated from inputs like speech, facial cues, or self-reporting).
  • 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.
  • Personalized environment settings for comfort and safety.
  • Adaptive infotainment & navigation suggestions tailored to mood, journey type, and accessibility.

Impact

  • Safer driving experience with fewer distractions.
  • Higher passenger satisfaction through comfort and entertainment personalization.
  • Improved accessibility and inclusivity for diverse user needs.
  • New value proposition for automakers: cars as intelligent, personalized environments, not just vehicles.
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.
  • Resource allocation bottlenecks (overloaded developers, idle testers) often go unnoticed.
  • Risks (delays, defects, dependency issues) are only discovered late, impacting delivery timelines.
  • Lack of predictive insights leads to reactive, rather than proactive, project management.

Challenge

Build an AI-powered project management assistant that can:

  • Auto-generate project dashboards by integrating Jira, GitHub, and MS Teams data.
  • Provide real-time resource allocation insights (who is overloaded, who is free).
  • Predict risks and delays using historical patterns and live progress signals.
  • 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).
  • Resource allocation map showing workload distribution across the team.
  • 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.
  • Improved predictability → early identification of risks and delays.
  • Optimal resource utilization → balanced workloads across teams.
  • Better stakeholder communication → clear, automated updates.
  • Scalable for enterprises → can be deployed across multiple automotive software teams.
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.
  • Costs and expertise trade-offs are rarely quantified, making it hard to justify team composition to leadership or clients.
  • 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:

  • Employee database (skills, past projects, availability, cost)
  • Customer project requirements (tech stack, timeline, budget, domain)

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.
  • Higher client satisfaction due to right skills on the right project.
  • Lower staffing costs through data-driven optimization.
  • A scalable framework that can be extended for hackathons, consulting firms, or large enterprise project staffing.
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