AI Safety Assurance in Electric Vehicles: A Case Study on AI-Driven SOC Estimation
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arXiv
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| Main Authors: | , , , , , , |
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| Format: | Preprint |
| Published: |
2025
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| _version_ | 1866909768850866176 |
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| author | Skoglund, Martin Warg, Fredrik Mirzai, Aria Thorsen, Anders Lundgren, Karl Folkesson, Peter Havers-zulka, Bastian |
| author_facet | Skoglund, Martin Warg, Fredrik Mirzai, Aria Thorsen, Anders Lundgren, Karl Folkesson, Peter Havers-zulka, Bastian |
| contents | Integrating Artificial Intelligence (AI) technology in electric vehicles (EV) introduces unique challenges for safety assurance, particularly within the framework of ISO 26262, which governs functional safety in the automotive domain. Traditional assessment methodologies are not geared toward evaluating AI-based functions and require evolving standards and practices. This paper explores how an independent assessment of an AI component in an EV can be achieved when combining ISO 26262 with the recently released ISO/PAS 8800, whose scope is AI safety for road vehicles. The AI-driven State of Charge (SOC) battery estimation exemplifies the process. Key features relevant to the independent assessment of this extended evaluation approach are identified. As part of the evaluation, robustness testing of the AI component is conducted using fault injection experiments, wherein perturbed sensor inputs are systematically introduced to assess the component's resilience to input variance. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_03270 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | AI Safety Assurance in Electric Vehicles: A Case Study on AI-Driven SOC Estimation Skoglund, Martin Warg, Fredrik Mirzai, Aria Thorsen, Anders Lundgren, Karl Folkesson, Peter Havers-zulka, Bastian Software Engineering Robotics Integrating Artificial Intelligence (AI) technology in electric vehicles (EV) introduces unique challenges for safety assurance, particularly within the framework of ISO 26262, which governs functional safety in the automotive domain. Traditional assessment methodologies are not geared toward evaluating AI-based functions and require evolving standards and practices. This paper explores how an independent assessment of an AI component in an EV can be achieved when combining ISO 26262 with the recently released ISO/PAS 8800, whose scope is AI safety for road vehicles. The AI-driven State of Charge (SOC) battery estimation exemplifies the process. Key features relevant to the independent assessment of this extended evaluation approach are identified. As part of the evaluation, robustness testing of the AI component is conducted using fault injection experiments, wherein perturbed sensor inputs are systematically introduced to assess the component's resilience to input variance. |
| title | AI Safety Assurance in Electric Vehicles: A Case Study on AI-Driven SOC Estimation |
| topic | Software Engineering Robotics |
| url | https://arxiv.org/abs/2509.03270 |