A disassembly operation can be technically impressive without being economically worthwhile. This study connects the physical steps needed to dismantle an electric-vehicle battery with the time, labour and equipment assumptions that determine whether robotics adds value, using a Mitsubishi Outlander PHEV battery pack as its case study.
Why the Whole Process Matters
Battery disassembly contains very different operations: exposing components, releasing connections, removing fasteners and handling parts do not place the same demands on a robot. Access can be restricted, component condition can vary and an apparently small obstruction can prevent the next operation from starting.
The useful unit of analysis is therefore the task within its process context. Asking whether a robot can perform one action is only the beginning; the economic question also includes how long that action takes, which preceding operations it depends on and how often human intervention remains necessary.
The paper focuses on pack-to-module disassembly. That boundary matters: findings about separating modules from a pack should not be presented as evidence that every subsequent cell-level recycling or material-recovery operation has been automated.
Linking Technical Feasibility to Economics
The assessment identifies operations, examines their suitability for automation and uses those findings to construct an economic roadmap. Technical feasibility and economic contribution are related but not interchangeable. Automating several short tasks may change less total cycle time than improving one long bottleneck.
Similarly, labour time and elapsed time are not identical. A robot may perform an operation while a person supervises it, but that does not mean all of the person’s time has become available for another task. An economic model needs a defensible account of intervention, utilisation and process organisation, not just the robot’s nominal movement duration.
graph TD
A[Observe pack-to-module operations] --> B[Task feasibility and intervention needs]
A --> C[Duration and process dependencies]
B --> D[Proposed automation scenario]
C --> D
D --> E[Equipment, labour and utilisation assumptions]
E --> F[Disassembly cost and annual economics]
F --> G[Sensitivity to different operating conditions]
Original explanatory diagram of the assessment logic, not an official figure or a claim that every input has the same importance.
This separation also explains why transferring the approach to a different pack requires new observations. A change in fastening, adhesives, connector access or the condition of incoming batteries can alter both feasibility and cost. Reusing the method is more defensible than copying the final savings percentage.
Results and Their Denominators
For the case studied, 57% of pack-to-module tasks were identified as readily automatable, with another 24% requiring minimal human intervention. These are task classifications. They do not mean that 81% of labour hours, factory costs or safety risks disappear.
Under the proposed roadmap and its assumptions, the study estimates a 12.85% increase in net annual revenue and a 43.75% reduction in disassembly cost. The figures describe different outcomes with different starting values. They should neither be added together nor described as the same efficiency improvement.
The scale of the cost change is potentially important, but it remains a scenario result. Capital and operating assumptions, workload, utilisation and the actual reliability of each automated operation influence whether a facility could obtain comparable economics. The paper is a case-based assessment, not a multi-factory demonstration of realised savings.
What the Findings Are Useful For
The practical contribution is a way to prioritise engineering effort. A promising target combines meaningful process impact with a credible route to automation. A difficult task that dominates the cycle may deserve research investment, whereas an easily automated but infrequent action may have little effect on annual performance.
A useful extension would examine how that priority changes with uncertain handling times, equipment utilisation or intervention rates. Such sensitivity analysis is not a claim that the paper has tested every possible operating condition; it is how an organisation can check whether the case-study conclusion survives its own circumstances.
Exact numerical reproduction requires the original operation observations and costing assumptions. The explanation here supplies the causal chain—tasks affect process performance, which affects economics—without presenting invented durations or illustrative prices as measured factory data.
Testing and Real-World Use
This could be tested by timing the operations for another battery design, classifying the intervention each would need and recalculating costs under several realistic utilisation assumptions. In a recycling or remanufacturing facility, that assessment could identify which automation investment is most likely to remove a costly bottleneck before a full cell is purchased.
Paper and Figures
See the IEEE Access paper, the open-access institutional PDF with the original figures and assessment details, and the publication record. Online publication was 19 December 2024; the journal volume is dated 2025.