Against the backdrop of rising penetration of new energy vehicles, the auto insurance sector serving them is undergoing significant pricing mechanism reforms. On March 23, it was learned that the autonomous pricing coefficient for new energy vehicle insurance has been optimized again, expanding from [0.6, 1.4] to [0.55, 1.45]. This marks the second adjustment since September 2025.
In recent years, China's new energy vehicle industry has developed rapidly. In 2025, both production and sales of new energy vehicles exceeded 16 million units, increasing by 29% and 28.2% year-on-year respectively, maintaining the top global position for 11 consecutive years. Despite this rapid development, the issue of "owners complaining about high premiums and insurers complaining about losses" in new energy vehicle insurance has persisted.
However, the industry continues to explore reforms. Efforts range from optimizing pricing coefficients to make premiums better reflect risk, exploring "battery-swapping" models to clarify battery asset risk boundaries, to encouraging automakers to leverage their data and technological advantages within the industry chain. This transformation spans the entire value chain, including repair cost control, precise insurance pricing, improved industry regulation, and future development planning, fundamentally reshaping the new energy vehicle insurance ecosystem towards a win-win situation for owners and insurers.
The autonomous pricing coefficient range has been expanded again nationwide. This coefficient is an interval factor allowing insurers to adjust baseline premiums based on factors like vehicle model risk, usage nature, and driver behavior. A wider range grants insurers greater flexibility to set premiums according to actual risk levels, enabling more accurate risk matching, improved underwriting profitability, and allowing low-risk owners to enjoy lower premiums.
For consumers, the key question is whether their premiums will increase or decrease. According to the commercial auto insurance premium formula (Premium = Baseline Premium × No-Claim Discount Coefficient × Autonomous Pricing Coefficient), the theoretical maximum price reduction is 8.33%, calculated as (0.55 - 0.6) / 0.6. The theoretical maximum increase is 3.57%, calculated as (1.45 - 1.4) / 1.4. However, these are theoretical limits; actual premium changes are influenced by other factors like traffic violation records and vehicle parts-to-cost ratio. An analyst noted that while the coefficient adjustment raises the "ceiling" and lowers the "floor" for pricing, the full theoretical discount may not be realized due to these constraints.
Drivers likely to benefit from the new lower price floor are typically those with good driving habits, a zero-claim record, and vehicles with a low parts-to-cost ratio, as insurers are motivated to attract such low-risk business. Conversely, high-mileage vehicles, those with high claim frequencies like ride-hailing cars, premium models with high repair costs, or models with exceptionally high parts-to-cost ratios may face premium increases due to their higher risk profile.
This is the second adjustment to the new energy vehicle insurance autonomous pricing coefficient, following the first adjustment in September 2025 which expanded the range from [0.65, 1.35] to [0.6, 1.4]. Compared to the one-step expansion for traditional fuel vehicle insurance in 2023 (from [0.65, 1.35] to [0.5, 1.5]), adjustments for new energy vehicle insurance are more frequent but smaller in scale. This "small steps, quick pace" approach aims to prevent market disruption from sudden pricing liberalization, such as恶性 price wars or drastic premium fluctuations, giving insurers time to upgrade actuarial models and accumulate multi-dimensional data on driving behavior and vehicle wear for smoother risk-price matching. This approach aligns with regulatory guidance issued last year, which emphasized稳步 optimizing the coefficient range to enhance pricing科学性.
The continuous expansion of autonomous pricing power is expected to push insurers to dynamically adjust average premiums based on their risk control capabilities, business structure, and comprehensive cost ratios, potentially further optimizing underwriting profit margins. However, it also demands higher pricing accuracy and risk management capabilities from insurers, compelling them to move away from crude pricing models towards精细化 management using big data and AI to identify risk differences among vehicle models, usage types, and driving behaviors, and establish corresponding rate systems. Failure to do so risks losing customers from overpricing or underwriting losses from underpricing.
Beyond pricing adjustments, the industry is exploring deeper structural reforms to address the long-standing dilemma. In 2026, exploration of commercial insurance for the "battery-swapping" model has accelerated. This model involves selling, managing, and insuring the vehicle and its battery as separate assets. Policy documents have signaled support for researching such products. Pilot programs in some regions have shown initial success. For instance, a logistics company reported a 30-50% reduction in initial investment costs and approximately 30% lower insurance premiums for 10 new energy trucks under this model compared to traditional procurement.
The "battery-swapping" model can lower premiums and optimize risk because, traditionally, the expensive battery significantly increases the vehicle's insured value and premium. Separating them means the vehicle insurance covers only the chassis, leading to a lower sum insured and thus lower premium—pilots show reductions over 30%. Furthermore, having batteries professionally managed and maintained by specialized operators reduces risks like improper charging/discharging, potentially decreasing failure and fire rates, thereby lowering claim frequency from the source. An insurer emphasized that this model is seen as a key innovation to systematically address core issues like owner value retention anxiety and complex loss assessment for insurers, by clarifying risk ownership and achieving precise asset-risk matching.
Whether through pricing coefficient optimization or "battery-swapping" models, these are individual levers for change. Solving the dual challenge of "high premiums and underwriting losses" requires a long-term, comprehensive approach. It necessitates time to accumulate data and experience, plus coordinated efforts across the ecosystem involving insurers, automakers, and other stakeholders to explore innovative paths for sustainable industry development.
Regulators are actively promoting cross-industry collaboration. Plans include guiding insurers and automakers to sign cooperation memorandums, exploring comprehensive vehicle rating systems, and aiming to reduce the total cost of ownership throughout the vehicle lifecycle for mutual benefit.
Market practice highlights collaboration between insurers and automakers in building maintenance ecosystems and sharing data as crucial. Insurers should increase R&D investment into new energy vehicle technology, deeply studying risks associated with core systems like the battery, motor, electronic control, and autonomous driving. By combining historical claims data with technical parameters, more accurate risk assessment models can be developed, moving beyond traditional pricing limitations to set fair premiums. Deepening cooperation with automakers is also vital. Utilizing telematics and IoT technologies to legally obtain dynamic data on driving behavior and battery health can enable highly personalized pricing strategies ("one price per person, one fee per trip"). This rewards safe drivers with lower premiums while accurately covering high-risk groups, addressing the core challenges of difficult pricing and high赔付.
